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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
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<issn pub-type="epub">1664-8021</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1769972</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2026.1769972</article-id>
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<subj-group subj-group-type="heading">
<subject>Original Research</subject>
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</article-categories>
<title-group>
<article-title>The role of miRNAs in the development of brain metastases originating from lung adenocarcinoma</article-title>
<alt-title alt-title-type="left-running-head">Torner et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fgene.2026.1769972">10.3389/fgene.2026.1769972</ext-link>
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<contrib contrib-type="author">
<name>
<surname>Torner</surname>
<given-names>Bernadett</given-names>
</name>
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<sup>1</sup>
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<surname>Klekner</surname>
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<sup>2</sup>
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<surname>Balogh</surname>
<given-names>Istv&#xe1;n</given-names>
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<sup>1</sup>
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<surname>Penyige</surname>
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<surname>G&#xe9;czi</surname>
<given-names>D&#xf3;ra</given-names>
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<surname>Geszti</surname>
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<surname>Birk&#xf3;</surname>
<given-names>Zsuzsanna</given-names>
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<aff id="aff1">
<label>1</label>
<institution>Department of Medical Genetics, Faculty of Medicine, University of Debrecen</institution>, <city>Debrecen</city>, <country country="HU">Hungary</country>
</aff>
<aff id="aff2">
<label>2</label>
<institution>Department of Neurosurgery, Faculty of Medicine, University of Debrecen</institution>, <city>Debrecen</city>, <country country="HU">Hungary</country>
</aff>
<author-notes>
<corresp id="c001">
<label>&#x2a;</label>Correspondence: Zsuzsanna Birk&#xf3;, <email xlink:href="mailto:birko@med.unideb.hu">birko@med.unideb.hu</email>
</corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-20">
<day>20</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1769972</elocation-id>
<history>
<date date-type="received">
<day>17</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>30</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>06</day>
<month>02</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Torner, Klekner, Balogh, Penyige, G&#xe9;czi, G&#xe1;sp&#xe1;r, Geszti and Birk&#xf3;.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Torner, Klekner, Balogh, Penyige, G&#xe9;czi, G&#xe1;sp&#xe1;r, Geszti and Birk&#xf3;</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-20">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Introduction</title>
<p>Brain metastases (BMs) represent most malignant lesions of the central nervous system. Lung cancer&#x2014;particularly lung adenocarcinoma (LUAD, &#x223c;25%)&#x2014;is the most common source of BMs. MicroRNAs (miRNAs) play a crucial role in regulating gene expression, thereby contributing to tumor progression and metastatic spread. Identifying these regulatory molecules may enable a deeper understanding of the mechanisms driving LUAD brain metastasis (LUAD-BM) development and reveal therapeutic targets to prevent or limit disease progression.</p>
</sec>
<sec>
<title>Methods</title>
<p>Next-generation RNA sequencing (RNA-seq) was performed on six LUAD-BM and six non-tumorous human brain tissue samples to assess miRNA expression profiles. Additionally, RNA-seq data from 20 primary LUAD and 15 normal lung tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. MiRNAs showing the most pronounced alterations in LUAD-BM samples were selected for validation by real time quantitative polymerase chain reaction (RT-qPCR).</p>
</sec>
<sec>
<title>Results</title>
<p>Analysis of RNA-seq data identified 229 differentially expressed (DE) miRNAs between LUAD-BM and control samples. Functional annotation analysis indicated that these DE miRNAs are key regulators of tumorigenesis and metastasis. Using the Mann&#x2013;Whitney U test, ten miRNAs were confirmed to differ significantly between LUAD-BM and normal brain tissue. Receiver operating characteristic (ROC) curve analysis demonstrated their diagnostic potential. Among the ten validated miRNAs, miR-200c-3p, miR-146b-5p, and miR-3934-5p showed distinct expression patterns between primary LUAD and LUAD-BM, while miR-10a-5p, miR-210-3p, and miR-130b-3p exhibited stepwise dysregulation along the normal lung&#x2013;LUAD&#x2013;LUAD-BM axis, suggesting their involvement in metastatic progression.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>We identified ten miRNAs that showed preliminary ability to differentiate LUAD-BM from normal brain tissue. These findings indicate possible diagnostic and therapeutic implications. Among these, six miRNAs showed significant expression changes along the normal control&#x2013;primary LUAD&#x2013;LUAD-BM axis, highlighting their potential as biomarkers and therapeutic targets in BM development.</p>
</sec>
</abstract>
<kwd-group>
<kwd>biomarker panel</kwd>
<kwd>brain tissue</kwd>
<kwd>invasion</kwd>
<kwd>lung adenocarcinoma brain metastasis</kwd>
<kwd>miRNAs</kwd>
<kwd>next-generation sequencing</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This work was supported by grant 2017-1.2.1-NKP-2017-00002&#x201c;National Brain Research Program NAP 2.0&#x201d; and the EK&#xd6;P-24-3-I-DE-69 University Research Scholarship Program of the Ministry For Culture and Innovation from the source of the National Research, Development and Innovation fund.</funding-statement>
</funding-group>
<counts>
<fig-count count="5"/>
<table-count count="7"/>
<equation-count count="0"/>
<ref-count count="82"/>
<page-count count="17"/>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>RNA</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<label>1</label>
<title>Introduction</title>
<p>Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung tumor cases and is considered one of the leading causes of cancer-related mortality world-wide (<xref ref-type="bibr" rid="B42">Molina et al., 2008</xref>). Lung adenocarcinoma (LUAD) represents the most common histological sub-type of NSCLC, around 40%&#x2013;50% of all lung cancer patients are diagnosed with it (<xref ref-type="bibr" rid="B20">Herbst et al., 2018</xref>). In addition, LUAD frequently gives rise to distant metastases, particularly in the brain. Approximately 25% of patients with LUAD develop brain metastases (BMs) during the course of their disease (<xref ref-type="bibr" rid="B38">Lukas et al., 2014</xref>). LUAD brain metastases (LUAD-BM) are often associated with severe neurological symptoms and poor prognosis, with a median overall survival of 4&#x2013;15&#xa0;months (<xref ref-type="bibr" rid="B60">Sperduto et al., 2020</xref>).</p>
<p>From a clinical perspective, LUAD-BMs are often diagnosed synchronously with the primary tumor. Treatment strategies include a multimodal approach combining local therapies&#x2014;such as stereotactic radiosurgery or whole-brain radiotherapy&#x2013;with systemic treatments, including tyrosine kinase inhibitors in case of epidermal growth factor receptor (EGFR) mutant NSCLC patients (<xref ref-type="bibr" rid="B31">Le Rhun et al., 2021</xref>).</p>
<p>The molecular mechanisms underlying brain-specific metastasis in LUAD remain incompletely understood. However, current evidence suggests that they involve a complex interplay between tumor-intrinsic factors, such as gene expression signatures and microRNA (miRNA) profiles (<xref ref-type="bibr" rid="B44">Najjary et al., 2023</xref>). Certain oncogenic drivers, particularly EGFR mutations and anaplastic lymphoma kinase rearrangements, have been associated with a higher incidence of BMs, suggesting that these molecular subtypes may possess intrinsic neurotropic properties (<xref ref-type="bibr" rid="B51">Remon and Besse, 2018</xref>).</p>
<p>MiRNAs are short non-coding RNAs (&#x223c;22 nucleotides) that regulate gene expression post-transcriptionally, primarily by binding to complementary sequences within the 3&#x2032;untranslated region of target mRNAs. In cancer, dysregulated miRNA expression contributes to tumor initiation, progression, and metastasis by modulating oncogenes, tumor suppressors, and key signaling pathways (<xref ref-type="bibr" rid="B57">Smolarz et al., 2022</xref>). Given that miRNAs regulate a wide range of genes involved in migration, invasion, adhesion, colonization, and epithelial&#x2013;mesenchymal transition (EMT), they can be considered key molecular regulators within the metastatic cascade. Moreover, several studies have reported that miRNAs may con-tribute to the disruption of the blood&#x2013;brain barrier and the establishment of pro-metastatic microenvironment (<xref ref-type="bibr" rid="B59">Souza et al., 2023</xref>; <xref ref-type="bibr" rid="B58">Sol&#xe9; and Lawrie, 2019</xref>; <xref ref-type="bibr" rid="B26">Kim et al., 2018</xref>). In LUAD-BM, limited therapeutic options, delayed diagnosis, and difficulties in accurate detection contribute to poor clinical outcomes (<xref ref-type="bibr" rid="B59">Souza et al., 2023</xref>; <xref ref-type="bibr" rid="B26">Kim et al., 2018</xref>; <xref ref-type="bibr" rid="B63">Tominaga et al., 2015</xref>).</p>
<p>Only a limited number of studies to date have identified dysregulated miRNAs in LUAD-BM (<xref ref-type="bibr" rid="B52">Remon et al., 2016</xref>; <xref ref-type="bibr" rid="B30">Koh et al., 2024</xref>; <xref ref-type="bibr" rid="B79">Zhang L. et al., 2022</xref>; <xref ref-type="bibr" rid="B8">Daugaard et al., 2017</xref>). Furthermore, it should be considered that expression patterns can vary across racial, ethnic, and geographic groups (<xref ref-type="bibr" rid="B9">Dluzen et al., 2016</xref>; <xref ref-type="bibr" rid="B46">Nassar et al., 2017</xref>; <xref ref-type="bibr" rid="B50">Rawlings-Goss et al., 2014</xref>). Identifying biomarkers that predict early metastatic dissemination and the establishment of a pro-metastatic microenvironment is therefore essential, as such molecules may offer novel therapeutic opportunities to prevent or limit the development of LUAD-BM.</p>
<p>In this study, we focused on characterizing the miRNA landscape in LUAD-BM using intraoperative tissue samples collected at the Department of Neurosurgery, Faculty of Medicine, University of Debrecen. Our objective was to identify specific miRNAs whose altered expression may contribute to metastatic progression and brain colonization, providing insights into the molecular mechanisms underlying LUAD-BM. Differentially expressed (DE) miRNAs between LUAD-BM and normal brain tissues were first identified through high-throughput next-generation sequencing (NGS) and subsequently validated in an extended cohort using real time quantitative polymerase chain reaction (RT-qPCR). To further investigate their functional relevance, pathway enrichment analyses were con-ducted to explore their roles in tumor progression and invasion. Integrating these findings with publicly available LUAD and normal lung miRNA-seq data allowed us to examine expression patterns across the normal lung&#x2013;primary LUAD&#x2013;LUAD-BM axis, high-lighting miRNAs potentially involved in metastatic spread and revealing novel candidates for diagnostic or therapeutic targeting.</p>
</sec>
<sec sec-type="results" id="s2">
<label>2</label>
<title>Results</title>
<sec id="s2-1">
<label>2.1</label>
<title>Clustering of RNA sequencing (RNA-seq) data and identification of DE miRNAs</title>
<p>Brain tissue samples used for NGS were obtained from six LUAD-BM patients and from peritumoral, tumor-free regions of six lower-grade glioma patients, serving as control samples. We performed hierarchical clustering to compare the normalized RNA-seq datasets, revealing distinct differences in miRNA expression patterns between the LUAD-BM and control groups (<xref ref-type="fig" rid="F1">Figure 1A</xref>). For the clustering analysis, miRNAs were ranked according to their standard deviation, and the 100 miRNAs with the largest expression changes were included in the analysis. With k-Means clustering, the top 100 miRNAs with the highest expression variability were explicitly divided into six groups based on their expression levels (<xref ref-type="fig" rid="F1">Figure 1B</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Hierarchical clustering, K-means clustering, principal component analysis (PCA), and differential expression analysis (DESeq2) of RNA-Seq data. <bold>(A)</bold> Heatmap of differentially expressed (DE) microRNAs (miRNAs) in LUAD brain metastasis (LUAD-BM) and control samples were created using iDEP 2.01. MiRNAs were ranked by their standard deviation across all samples, and hierarchical clustering was carried out for the top 100 miRNAs. Expression levels are represented by a color scale, with magenta indicating higher and green indicating lower relative expression. <bold>(B)</bold> K-means clustering was performed to identify different clusters in the dataset. The up- and downregulated miRNAs are labelled green and magenta, respectively. <bold>(C)</bold> The heatmap represents the upregulated (magenta) and downregulated (green) miRNAs. <bold>(D)</bold> The PCA plot illustrates the distribution of miRNA expression profiles in LUAD-BM samples (magenta) and control samples (green). <bold>(E)</bold> The volcano plot illustrates the differential expression of miRNAs in LUAD-BM compared to non-tumoros control samples, determined by DESeq2 analysis. MiRNAs exhibiting a log2Fold Change (FC) greater than 1 with a statistically significant p-value (&#x3c;0.05) are highlighted in magenta, while those with a log2FC less than &#x2212;1 and a p-value &#x3c;0.05 are marked in green.</p>
</caption>
<graphic xlink:href="fgene-17-1769972-g001.tif">
<alt-text content-type="machine-generated">Panel A, B, and C display hierarchical clustering heatmaps with green to magenta color ranges representing gene expression differences between control and LUAD-BM samples. Panel D shows a principal component analysis scatter plot, distinguishing control (blue squares) and LUAD-BM (pink circles) groups by variance. Panel E presents a volcano plot illustrating gene expression changes, highlighting upregulated (magenta), downregulated (green), and unchanged (gray) genes in relation to fold change and statistical significance.</alt-text>
</graphic>
</fig>
<p>
<xref ref-type="fig" rid="F1">Figure 1D</xref> presents the results of the principal component analysis as a scatter-plot. This technique enables dimensionality reduction of large datasets while preserving the overall variability of miRNA expression profiles. The plot clearly demonstrates a distinct separation between the metastatic and control groups along the PC1 axis, which ac-counts for 54.8% of the total variance. As expected, the tumor-free control samples form a tight cluster, whereas the metastatic samples&#x2014;although they also cluster&#x2014;exhibit significantly greater heterogeneity in expression. These findings, supported by both clustering and principal component analysis, confirm the distinctive miRNA expression profiles between the two groups, consistent with their underlying pathological characteristics.</p>
<p>Using the DESeq2 package within the iDEP 2.01 tool, a total of 118 significantly upregulated and 111 downregulated miRNAs were identified in LUAD-BM samples com-pared to controls (<xref ref-type="sec" rid="s13">Supplementary Table S1</xref>). The analysis was performed using a threshold of false discovery rate (FDR) &#x3c; 0.01 and fold change &#x3e;2. The volcano plot (<xref ref-type="fig" rid="F1">Figure 1E</xref>) and the heatmap (<xref ref-type="fig" rid="F1">Figure 1C</xref>) show that the development of BM leads to a massive transcriptomic response.</p>
</sec>
<sec id="s2-2">
<label>2.2</label>
<title>Pathway enrichment analysis of DE miRNAs in LUAD-BM</title>
<p>We constructed miRNA&#x2013;target gene interaction networks using the miRNet tool, followed by functional enrichment and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. This approach enabled an independent evaluation of the enrichment results for miRNAs exhibiting the most significant expression changes in LUAD-BM samples. We considered the interaction networks of the 50 most upregulated and the 50 most downregulated miRNAs during the analysis. KEGG pathway analysis demonstrated that the targets of miRNAs overrepresented in LUAD-BM are involved not only in general tumor processes, the p53 signaling pathway, and cell cycle regulation, but also in pathways critical for migration, including focal adhesion, the ErbB signaling pathway, and the neurotrophin signaling pathway (<xref ref-type="fig" rid="F2">Figure 2a</xref>). Furthermore, the upregulated miRNAs showed significant enrichment in the NSCLC pathway. Targets of downregulated miRNAs were also enriched in pathways such as the p53 signaling pathway, focal adhesion, adherens junction, neurotrophin signaling pathway, ErbB signaling pathway, cell cycle regulation, the transforming growth factor-beta signaling pathway, and Wnt signaling pathway (<xref ref-type="fig" rid="F2">Figure 2b</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was analyzed for the 15 most upregulated <bold>(a)</bold> and downregulated <bold>(b)</bold> miRNAs, based on data from the miRNet tool. The dot sizes correspond to the number of genes associated with each KEGG pathway, and their significance is indicated by false discovery rate (FDR) values and the &#x2212;log10 of the p-values.</p>
</caption>
<graphic xlink:href="fgene-17-1769972-g002.tif">
<alt-text content-type="machine-generated">Two dot plots compare enriched pathways using FDR on the x-axis and pathway names on the y-axis. Dot size indicates gene count, and color shows &#x2013;log10 p-value, with more significant pathways in magenta. Panel (a) highlights &#x201C;Pathways in cancer&#x201D; and &#x201C;Prostate cancer&#x201D; as most significant, while panel (b) identifies &#x201C;Prostate cancer&#x201D; and &#x201C;Pathways in cancer&#x201D; as top pathways.</alt-text>
</graphic>
</fig>
<p>To minimize potential data noise, we analyzed the experimentally validated target genes of the five most upregulated and five most downregulated miRNAs using the miRTARGET web tool. Experimentally validated target genes were selected for each group based on statistical significance (p &#x3c; 0.05). Approximately 38% of the targets of the top upregulated miRNAs were downregulated in LUAD, while about 30% showed increased expression. Conversely, among the targets of the most downregulated miRNAs, 25% exhibited reduced and 40% elevated expression in LUAD samples.</p>
</sec>
<sec id="s2-3">
<label>2.3</label>
<title>Validation of DE miRNAs by RT-qPCR</title>
<p>To validate the results obtained from NGS on a larger cohort, eight upregulated miRNAs&#x2013;miR-200c-3p, miR-210-3p, miR-10a-5p, miR-130b-3p, miR-146b-5p, miR-503-5p, miR-196b-5p, and miR-3934-5p&#x2013;and two downregulated miRNAs&#x2013;miR-138-2-3p and miR-195-5p&#x2013;were selected for RT-qPCR validation based on their log2 fold change (log2FC) and adjusted p-values (<xref ref-type="table" rid="T1">Table 1</xref>). RT-qPCR was performed to quantify the relative expression levels of the selected miRNAs, using miR-103a-3p as the reference miRNA. Validation was performed using total RNA isolated from 30 LUAD-BM and 30 control brain tissue samples.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>List of eight upregulated and two downregulated miRNAs selected for validation, including their corresponding log2Fold Change (log2FC) and adjusted P values.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Regulation</th>
<th align="center">miRNA</th>
<th align="center">LUAD-BM vs. Normal brain control (log2FC)</th>
<th align="center">LUAD-BM vs. Normal brain<break/>Control (adj-pval)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">Up</td>
<td align="center">miR-200c-3p</td>
<td align="center">9.24</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-210-3p</td>
<td align="center">7.37</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-10a-5p</td>
<td align="center">7.15</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-130b-3p</td>
<td align="center">4.57</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-146b-5p</td>
<td align="center">4.14</td>
<td align="center">&#x3c;0.01</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-503-5p</td>
<td align="center">3.88</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-196b-5p</td>
<td align="center">3.54</td>
<td align="center">&#x3c;0.01</td>
</tr>
<tr>
<td align="center">Up</td>
<td align="center">miR-3934-5p</td>
<td align="center">2.84</td>
<td align="center">&#x3c;0.01</td>
</tr>
<tr>
<td align="center">Down</td>
<td align="center">miR-138-2-3p</td>
<td align="center">&#x2212;2.1</td>
<td align="center">&#x3c;0.01</td>
</tr>
<tr>
<td align="center">Down</td>
<td align="center">miR-195-5p</td>
<td align="center">&#x2212;2.26</td>
<td align="center">&#x3c;0.05</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Based on the normalized Ct values obtained from RT-qPCR, statistical analysis using the Mann&#x2013;Whitney U test confirmed that the expression levels of miR-200c-3p, miR-210-3p, miR-10a-5p, miR-130b-3p, miR-146b-5p, miR-503-5p, miR-196b-5p, and miR-3934-5p were significantly elevated in LUAD-BM samples compared to controls (<xref ref-type="fig" rid="F3">Figure 3</xref>; <xref ref-type="table" rid="T2">Table 2</xref>). Conversely, the expression of miR-138-2-3p and miR-195-5p was found to be significantly reduced, further validating the NGS-based differential expression findings. To assess the diagnostic value of the selected miRNAs, receiver operating characteristic (ROC) curves and distribution graphs were generated, and the optimal threshold values, as well as sensitivity and specificity values, were determined (<xref ref-type="fig" rid="F4">Figure 4</xref>; <xref ref-type="table" rid="T2">Table 2</xref>). The miR-200c-3p exhibited an area under the curve (AUC) of 0.95, accompanied by a sensitivity of 0.93 and specificity of 1 (<xref ref-type="fig" rid="F4">Figure 4a</xref>). The miR-210-3p showed an AUC of 0.9, with sensitivity and specificity values of 0.98 and 0.83, respectively (<xref ref-type="fig" rid="F4">Figure 4b</xref>). For miR-10a-5p, the AUC was 0.93, with sensitivity at 0.97 and specificity at 0.79 (<xref ref-type="fig" rid="F4">Figure 4c</xref>). The miR-130b-3p had an AUC of 0.87, along with a sensitivity of 0.93 and a specificity of 0.8 (<xref ref-type="fig" rid="F4">Figure 4d</xref>). Meanwhile, miR-146b-5p had both sensitivity and specificity at 0.86, with an AUC of 0.89 (<xref ref-type="fig" rid="F4">Figure 4e</xref>). MiR-503-5p showed an AUC value of 0.73, with sensitivity and specificity measured at 0.7 and 0.9, respectively (<xref ref-type="fig" rid="F4">Figure 4f</xref>). MiR-196b-5p had an AUC of 0.88, with a sensitivity of 0.7 and a specificity of 0.97, and for miR-3934-5p, the AUC was 0.81, with sensitivity at 0.63 and specificity at 0.93 (<xref ref-type="fig" rid="F4">Figures 4g,h</xref>). For downregulated miR-138-2-3p and miR-195-5p, the AUC values were 0.91 and 0.78, the sensitivities were 0.97 and 0.87, and the specificity values were 0.83 and 0.63, respectively (<xref ref-type="fig" rid="F4">Figures 4i,j</xref>). Based on available literature, we have summarized in <xref ref-type="table" rid="T3">Table 3</xref> the diagnostic potential and prospective therapeutic applications of the miRNAs investigated in this study, highlighting their roles in lung cancer progression, drug resistance, and possible clinical relevance.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Statistical significance analysis of up- and downregulated miRNAs. Differences were analyzed using the Mann&#x2013;Whitney U test. Color code: LUAD-BM, magenta; control, green. &#x2a;p &#x3c; 0.05; &#x2a;&#x2a;p &#x3c; 0.01; &#x2a;&#x2a;&#x2a;p &#x3c; 0.001; &#x2a;&#x2a;&#x2a;&#x2a;p &#x3c; 0.0001.</p>
</caption>
<graphic xlink:href="fgene-17-1769972-g003.tif">
<alt-text content-type="machine-generated">Boxplot chart with ten panels compares normalized Ct values for different microRNAs (e.g., hsa-miR-200c-3p, hsa-miR-210-3p) between LUAD-BM and control groups, indicating statistically significant differences with asterisks and showing higher or lower expression in each group.</alt-text>
</graphic>
</fig>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Diagnostic performance of selected miRNAs based on ROC curve analysis. The table shows p values, area under the curve (AUC) with 95% confidence intervals (95% CI), optimal cut-off points, sensitivity, and specificity for each miRNA.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">miRNA</th>
<th align="center">p value</th>
<th align="center">AUC</th>
<th align="center">95% CI</th>
<th align="center">Optimal cut-off point</th>
<th align="center">Sensitivity</th>
<th align="center">Specificity</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.95</td>
<td align="center">0.88&#x2013;1.022</td>
<td align="center">0.82</td>
<td align="center">0.93</td>
<td align="center">1</td>
</tr>
<tr>
<td align="center">miR-210-3p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.9</td>
<td align="center">0.8&#x2013;0.99</td>
<td align="center">7.54</td>
<td align="center">0.97</td>
<td align="center">0.83</td>
</tr>
<tr>
<td align="center">miR-10a-5p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.93</td>
<td align="center">0.86&#x2013;0.99</td>
<td align="center">12.57</td>
<td align="center">0.97</td>
<td align="center">0.79</td>
</tr>
<tr>
<td align="center">miR-130b-3p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.87</td>
<td align="center">0.77&#x2013;0.97</td>
<td align="center">7.58</td>
<td align="center">0.93</td>
<td align="center">0.8</td>
</tr>
<tr>
<td align="center">miR-146b-5p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.89</td>
<td align="center">0.82&#x2013;0.98</td>
<td align="center">4.08</td>
<td align="center">0.86</td>
<td align="center">0.86</td>
</tr>
<tr>
<td align="center">miR-503-5p</td>
<td align="center">&#x3c;0.01</td>
<td align="center">0.73</td>
<td align="center">0.58&#x2013;087</td>
<td align="center">7.56</td>
<td align="center">0.7</td>
<td align="center">0.9</td>
</tr>
<tr>
<td align="center">miR-196b-5p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.88</td>
<td align="center">0.74&#x2013;0.95</td>
<td align="center">10.4</td>
<td align="center">0.7</td>
<td align="center">0.97</td>
</tr>
<tr>
<td align="center">miR-3934</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.81</td>
<td align="center">0.71&#x2013;0.92</td>
<td align="center">12.83</td>
<td align="center">0.63</td>
<td align="center">0.93</td>
</tr>
<tr>
<td align="center">miR-195-5p</td>
<td align="center">&#x3c;0.001</td>
<td align="center">0.75</td>
<td align="center">0.62&#x2013;0.88</td>
<td align="center">16.74</td>
<td align="center">0.97</td>
<td align="center">0.83</td>
</tr>
<tr>
<td align="center">miR-138-2-3p</td>
<td align="center">&#x3c;0.00001</td>
<td align="center">0.91</td>
<td align="center">0.84&#x2013;0.99</td>
<td align="center">2.46</td>
<td align="center">0.87</td>
<td align="center">0.63</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Receiver operating characteristic (ROC) analysis of up- and downregulated miRNAs. Analyses using the ROC curve analysis, distribution graph and sensitivity (Se) &#x2013; specificity (Sp) curve of upregulated <bold>(a)</bold> miR-200c-3p (AUC: 0.95; optimal cut-point value: 0.82; Se: 0.93; Sp: 1); <bold>(b)</bold> miR-210-3p (AUC: 0.9; optimal cut-point value: 7.54; Se: 0.98. Sp: 0.83); <bold>(c)</bold> miR-10a-5p (AUC: 0.93; optimal cut-point value: 12.57; Se: 0.97; Sp:0.79); <bold>(d)</bold> miR-130b-3p (AUC: 0.87; optimal cut-point value: 7.58; Se: 0.93; Sp: 0.8); <bold>(e)</bold> miR-146b-5p (AUC: 0.89; optimal cut-point value: 4.08; Se: 0.86; Sp: 0.87); <bold>(f)</bold> miR-503-5p (AUC: 0.73; optimal cut-point value: 7.56; Se: 0.7; Sp: 0.9); <bold>(g)</bold> miR-196b-5p (AUC: 0.88; optimal cut-point value: 10.4; Se: 0.7; Sp: 0.97); <bold>(h)</bold> miR-3934-5p (AUC: 0.81; optimal cut-point value: 12.83; Se: 0.63; Sp: 0.93; <bold>(i)</bold> miR-138-2-3p (AUC: 0.91; Se: 0.97; Sp: 0.83); <bold>(j)</bold> miR-195-5p (AUC: 0.78; Se: 0.87; Sp: 0.63).</p>
</caption>
<graphic xlink:href="fgene-17-1769972-g004.tif">
<alt-text content-type="machine-generated">Ten panels labeled a to j display paired line graphs for different microRNAs, each showing a receiver operating characteristic (ROC) curve on the left and optimal cut-point value analysis on the right. ROC curves plot sensitivity versus 1-specificity, while optimal cut-point graphs plot sensitivity and specificity against normalized Ct value. Each panel is titled with a unique hsa-miR identifier.</alt-text>
</graphic>
</fig>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Summary of the possible diagnostic and therapeutic applications of the miRNAs investigated in lung cancer based on published literature.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">miRNA</th>
<th align="center">Diagnostic potential</th>
<th align="center">Therapeutic potential</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">miR-10a-5p</td>
<td align="center">Elevated level is associated with aggressive disease and higher metastatic risk (<xref ref-type="bibr" rid="B48">Patnaik et al., 2011</xref>)</td>
<td align="center">miR-10a inhibition may reverse cisplatin resistance in NSCLC (<xref ref-type="bibr" rid="B61">Sun et al., 2015</xref>)<break/>Targeting miR-10a could suppress tumor growth and invasion (via PTEN/AKT/ERK pathway) (<xref ref-type="bibr" rid="B77">Yu et al., 2015</xref>)</td>
</tr>
<tr>
<td align="center">miR-146b-5p</td>
<td align="center">May serve as a marker of recurrence and poor survival (<xref ref-type="bibr" rid="B55">Sang et al., 2025</xref>)</td>
<td align="center">Inhibiting miR-146b-5p or PI3K/AKT signaling may reduce NSCLC growth and osimertinib resistance (<xref ref-type="bibr" rid="B55">Sang et al., 2025</xref>)</td>
</tr>
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">Exhibits tumor-suppressive effects in NSCLC by inhibiting cell proliferation and migration (<xref ref-type="bibr" rid="B32">Lei et al., 2018</xref>)</td>
<td align="center">Inhibits cell proliferation and migration in NSCLC by targeting LDHA (<xref ref-type="bibr" rid="B32">Lei et al., 2018</xref>)<break/>Inhibition of EMT via miR-200c-3p overexpression may overcome EGFR TKI resistance and promote NSCLC cell death (<xref ref-type="bibr" rid="B69">Wang et al., 2020</xref>)</td>
</tr>
<tr>
<td align="center">miR-130b-3p</td>
<td align="center">High expression correlates with poor overall survival and is associated with vascular and lymphatic invasion in NSCLC (<xref ref-type="bibr" rid="B21">Hirono et al., 2019</xref>)</td>
<td align="center">Inhibition of APE1-mediated miR-33a/miR-130b regulation may restore DICER1 expression, reducing chemoresistance and invasiveness in lung cancer (<xref ref-type="bibr" rid="B2">Antoniali et al., 2022</xref>)<break/>Inhibition of DEPDC1 via exosomal miR-130b-3p may suppress NSCLC growth and migration, promote apoptosis, and block EMT (<xref ref-type="bibr" rid="B39">Lv et al., 2024</xref>)<break/>Inhibition of miR-130b or restoration of TIMP-2 may suppress NSCLC invasion by reducing MMP-2 activity (<xref ref-type="bibr" rid="B21">Hirono et al., 2019</xref>)</td>
</tr>
<tr>
<td align="center">miR-503-5p</td>
<td align="center">High miR-503-5p expression may serve as a biomarker for cisplatin resistance and angiogenesis in LUAD (<xref ref-type="bibr" rid="B19">Han and Wang, 2023</xref>)</td>
<td align="center">Inhibition of miR-503-5p may overcome cisplatin resistance, suppress angiogenesis and EMT, and promote apoptosis in LUAD via upregulation of CTDSPL (<xref ref-type="bibr" rid="B19">Han and Wang, 2023</xref>)<break/>Inhibition of the JMJD2C/MALAT1/miR-503-5p/SEPT2 axis may suppress NSCLC progression by modulating histone methylation and downstream targets (<xref ref-type="bibr" rid="B80">Zhang et al., 2022b</xref>)</td>
</tr>
<tr>
<td align="center">miR-210-3p</td>
<td align="center">Upregulation of miR-210-3p in lung cancer tissues and cells may indicate poor prognosis in lung cancer (<xref ref-type="bibr" rid="B5">Chen et al., 2021</xref>)</td>
<td align="center">Inhibition of PCGF3 via miR-210-3p antagomir may suppress lung cancer growth and metastasis by modulating Bax, Bcl-2, MMP-2, and MMP-9 expression<break/>Inhibition of miR-210-3p may suppress lung cancer progression and EMT while promoting apoptosis, partly via the USF1/PCGF3 pathway (<xref ref-type="bibr" rid="B5">Chen et al., 2021</xref>)</td>
</tr>
<tr>
<td align="center">miR-3934-5p</td>
<td align="center">TP53INP1 and miR-3934-5p levels may serve as biomarkers for lung cancer progression and cisplatin sensitivity (<xref ref-type="bibr" rid="B53">Ren et al., 2019</xref>)</td>
<td align="center">Inhibition of miR-3934-5p may enhance cisplatin sensitivity, suppress proliferation, and promote apoptosis in NSCLC via upregulation of TP53INP1 (36)</td>
</tr>
<tr>
<td align="center">miR-195-5p</td>
<td align="center">Low miR-195 expression may indicate poor prognosis in lung cancer (<xref ref-type="bibr" rid="B34">Liu et al., 2015</xref>)</td>
<td align="center">Inhibition of CHEK1 via miR-195 overexpression may suppress NSCLC growth, migration, and invasion, improving prognosis (<xref ref-type="bibr" rid="B34">Liu et al., 2015</xref>)<break/>Inhibition of FGF2 via miR-195-5p overexpression may enhance cisplatin sensitivity and suppress migration, invasion, and EMT in NSCLC (<xref ref-type="bibr" rid="B70">Wang et al., 2021</xref>)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2-4">
<label>2.4</label>
<title>Comparative analysis of miRNA profiles in LUAD-BM and LUAD tissues</title>
<p>To investigate whether the experimentally validated differentially expressed (DE) miRNAs identified in LUAD brain metastases are also altered in primary lung tumors, differential expression analysis was performed using LUAD-BM samples from our cohort (n &#x3d; 6). For comparison, miRNA expression profiles of primary LUAD (n &#x3d; 20) and normal lung control samples (n &#x3d; 15) were obtained from The Cancer Genome Atlas (TCGA) database and used to assess expression patterns along the normal lung&#x2013;primary LUAD&#x2013;LUAD-BM axis. The analysis indicated significant downregulation of miR-200c-3p (log2FC &#x3d; &#x2212;1.30, <italic>p</italic> &#x3c; 0.05), while significant upregulation of miR-146b-5p (log2FC &#x3d; 4.34, <italic>p</italic> &#x3c; 0.0001) and miR-3934-5p (log2FC &#x3d; 2.60, <italic>p</italic> &#x3c; 0.0001) in LUAD-BM in comparison to primary LUAD samples. However, these miRNAs did not show differential expression in primary LUAD compared to normal lung controls. The analysis further demonstrated that miR-10a-5p (log2FC &#x3d; &#x2212;1.19, <italic>p</italic> &#x3c; 0.01) was significantly downregulated in primary LUAD samples relative to normal lung controls, with an additional de-crease in expression observed in LUAD-BM compared to primary LUAD (log2FC &#x3d; &#x2212;2.17, <italic>p</italic> &#x3c; 0.0001). Conversely, miR-210-3p (log2FC &#x3d; 4.97, <italic>p</italic> &#x3c; 0.0001) and miR-130b-3p (log2FC &#x3d; 1.11, <italic>p</italic> &#x3c; 0.01) were significantly upregulated in primary LUAD samples relative to normal lung controls, and their expression levels increased further in LU-AD-BM compared to primary LUAD (miR-210-3p: log2FC &#x3d; 1.93, <italic>p</italic> &#x3c; 0.0001; miR-130b-3p: log2FC &#x3d; 3.24, <italic>p</italic> &#x3c; 0.0001). No significant differences in expression were observed for miR-503-5p and miR-195-5p in the LUAD-BM versus LUAD comparison. However, miR-503-5p (log2FC &#x3d; 2.25, <italic>p</italic> &#x3c; 0.0001) was significantly upregulated, while miR-195-5p (log2FC &#x3d; &#x2212;3.32, <italic>p</italic> &#x3c; 0.0001) was significantly downregulated in primary LUAD samples compared to normal lung controls. Moreover, miR-138-2-3p is not ex-pressed in lung tissue, which explains why no differences were observed along the nor-mal lung&#x2013;LUAD&#x2013;LUAD-BM axis. However, its expression was found to be downregulated in LUAD-BM compared to normal brain tissue. These results indicate that among the ten validated miRNAs that were significantly dysregulated in LUAD-BM compared with control brain tissue samples, miR-200c-3p, miR-146b-5p, and miR-3934-5p showed significant expression differences between primary LUAD and LUAD-BM, while miR-10a-5p, miR-210-3p, and miR-130b-3p exhibited stepwise dysregulation from normal lung to primary LUAD and further to LUAD-BM (<xref ref-type="table" rid="T4">Table 4</xref>). These results may reflect their role in metastatic progression.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Comparison of the expression levels of 10 DE miRNAs, including their respective log2FC and adj-p-values. The analysis was performed by first comparing LUAD samples with normal lung samples, followed by a comparison of LUAD-BM samples with LUAD controls from the TCGA database.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">miRNA</th>
<th align="center">LUAD vs. Normal lung control (log2FC)</th>
<th align="center">LUAD vs. Normal lung control (adj-pval)</th>
<th align="center">LUAD-BM vs. LUAD (log2FC)</th>
<th align="center">LUAD-BM vs. LUAD (adj-pval)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">miR-196b-5p</td>
<td align="center">3.53</td>
<td align="center">&#x3c;0.0001</td>
<td align="center">&#x2212;3.14</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-10a-5p</td>
<td align="center">&#x2212;1.19</td>
<td align="center">&#x3c;0.01</td>
<td align="center">&#x2212;2.17</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">0.36</td>
<td align="center">ns</td>
<td align="center">&#x2212;1.30</td>
<td align="center">&#x3c;0.05</td>
</tr>
<tr>
<td align="center">miR-503-5p</td>
<td align="center">2.45</td>
<td align="center">&#x3c;0.0001</td>
<td align="center">0.48</td>
<td align="center">ns</td>
</tr>
<tr>
<td align="center">miR-195-5p</td>
<td align="center">&#x2212;3.32</td>
<td align="center">&#x3c;0.0001</td>
<td align="center">0.91</td>
<td align="center">ns</td>
</tr>
<tr>
<td align="center">miR-210-3p</td>
<td align="center">4.97</td>
<td align="center">&#x3c;0.0001</td>
<td align="center">1.93</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-3934-5p</td>
<td align="center">&#x2212;0.89</td>
<td align="center">&#x3c;0.05</td>
<td align="center">2.60</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-130b-3p</td>
<td align="center">1.11</td>
<td align="center">&#x3c;0.01</td>
<td align="center">3.24</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-146b-5p</td>
<td align="center">&#x2212;0.85</td>
<td align="center">&#x3c;0.05</td>
<td align="center">4.34</td>
<td align="center">&#x3c;0.0001</td>
</tr>
<tr>
<td align="center">miR-138-2-3p</td>
<td align="center">0</td>
<td align="center">ns</td>
<td align="center">0</td>
<td align="center">ns</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="s3">
<label>3</label>
<title>Discussion</title>
<p>Lung cancer represents the most prevalent cause for cancer-related mortality world-wide; within this, approximately 85% of all cases are diagnosed with NSCLC (<xref ref-type="bibr" rid="B82">Zhou et al., 2021</xref>). A considerable proportion of patients with NSCLC develop BM that significantly worsen prognosis and pose unique therapeutic challenges, especially among patients with LUAD. Response rates to monotherapy with PD-1 or PD-L1 inhibitors among those with BMs are generally low (<xref ref-type="bibr" rid="B27">Kim et al., 2020</xref>). It can be explained by the unique immune microenvironment of the central nervous system, restricted drug penetrance across the blood&#x2013;brain barrier, and the clonal selection and evolution of those groups of tumor cells that tend to migrate to the brain (<xref ref-type="bibr" rid="B65">Tsakonas et al., 2022</xref>). Recently, miRNAs have emerged as critical regulators of gene expression and key players in cancer biology. These small, non-coding RNA molecules can influence multiple oncogenic processes, including proliferation, invasion, metastasis, and therapy resistance (<xref ref-type="bibr" rid="B24">Hudson et al., 2024</xref>). In this study, we aimed to identify miRNAs that are DE in LUAD-BM tissue samples compared to control brain and primary LUAD samples, with the goal of uncovering candidates potentially involved in the development of BM originating from LUAD. Further-more, we explored their potential utility as biomarkers for predicting the risk of BM in LUAD patients.</p>
<p>KEGG pathway analysis was performed using the 50 most upregulated and 50 most downregulated miRNAs identified by miRNA NGS in the LUAD-BM versus control brain tissue comparison. The predicted target genes of these dysregulated miRNAs showed a strong association with cancer-related pathways, highlighting their potential involvement in tumor progression and metastasis. For example, aberrant expression of focal adhesion molecules that act as mechanosensors mediating bidirectional communication between the cell and its microenvironment could lead to enhanced invasion and migration capacity of the tumor cells (<xref ref-type="bibr" rid="B74">Yayan et al., 2024</xref>). It has been shown that the application of integrin inhibitors and E-cadherin up-regulators during therapy leads to a 70% reduction of invasion (<xref ref-type="bibr" rid="B54">Riley et al., 2019</xref>). Furthermore, neurotrophin signaling pathways play a significant role in tumor development. Specifically, nerve growth factor and brain-derived neurotrophic factor were reported to stimulate tumor cell proliferation, survival, migration, and/or invasion and favor tumor angiogenesis (<xref ref-type="bibr" rid="B7">Chopin et al., 2016</xref>).</p>
<p>Our RT-qPCR validation confirmed that the expression levels of miR-200c-3p, miR-210-3p, miR-10a-5p, miR-130b-3p, miR-146b-5p, miR-503-5p, miR-196b-5p, and miR-3934-5p were significantly upregulated, while miR-138-2-3p and miR-195-5p were significantly downregulated in LUAD-BM tissue samples compared to control brain tis-sue. In addition to identifying dysregulated miRNAs in LUAD-BM tissue samples compared to brain tissue controls, we performed differential expression analysis on 15 normal lung tissue samples, 20 primary LUAD samples&#x2013;downloaded from the TCGA database and selected from European populations&#x2013;and our six LUAD-BM samples (<xref ref-type="table" rid="T4">Table 4</xref>). Furthermore, based on literature data, we analyzed the functions of the target genes of the selected miRNAs, focusing on those previously shown to be associated with migratory processes in LUAD/NSCLC (<xref ref-type="table" rid="T5">Table 5</xref>; <xref ref-type="fig" rid="F5">Figure 5</xref>).</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Experimentally validated target genes of miR-10a-5p, miR-210-3p, miR-130b-3p, miR-3934-5p, miR-200c-3p and miR-146-5p together with their associated biological functions and functional effects in LUAD/NSCLC, based on literature data.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">miRNA</th>
<th align="center">Target</th>
<th align="center">Biological function</th>
<th align="center">Functional effect in LUAD/NCSLC</th>
<th align="center">Reference</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">miR-10a-5p</td>
<td align="center">PIK3CA</td>
<td align="center">Regulates AKT/mTOR signaling, cell growth, survival</td>
<td align="center">Suppresses PI3K/AKT pathway; modulates cisplatin resistance</td>
<td align="center">
<xref ref-type="bibr" rid="B23">Huang et al. (2020)</xref>
</td>
</tr>
<tr>
<td align="center">miR-10a-5p</td>
<td align="center">PTEN</td>
<td align="center">Negative regulator of PI3K/AKT signaling, cell proliferation, migration</td>
<td align="center">Downregulation promotes proliferation, migration, invasion via AKT/ERK activation</td>
<td align="center">
<xref ref-type="bibr" rid="B77">Yu et al. (2015)</xref>
</td>
</tr>
<tr>
<td align="center">miR-210-3p</td>
<td align="center">CCL2</td>
<td align="center">Regulates macrophage recruitment and polarization under hypoxia</td>
<td align="center">High levels reduce monocyte infiltration, and promote tumor progression</td>
<td align="center">
<xref ref-type="bibr" rid="B3">Arora et al. (2024)</xref>
</td>
</tr>
<tr>
<td align="center">mir-130b-3p</td>
<td align="center">STK11</td>
<td align="center">Regulates cell proliferation, migration, invasion, and immune escape</td>
<td align="center">Upregulation promotes the proliferation, migration, invasion, and immune escape</td>
<td align="center">
<xref ref-type="bibr" rid="B6">Chen et al. (2023)</xref>
</td>
</tr>
<tr>
<td align="center">mir-130b-3p</td>
<td align="center">FOXO3</td>
<td align="center">Regulation of Keap1/NFE2L2/TXNRD1 signaling and tumor progression</td>
<td align="center">Upregulation promotes lung cancer progression</td>
<td align="center">
<xref ref-type="bibr" rid="B17">Guo et al. (2021)</xref>
</td>
</tr>
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">RRM2</td>
<td align="center">Regulates DNA synthesis and cell proliferation</td>
<td align="center">Downregulation promotes DDP resistance and tumor progression</td>
<td align="center">
<xref ref-type="bibr" rid="B36">Liu et al. (2022)</xref>
</td>
</tr>
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">GPC4</td>
<td align="center">Regulates HS3ST1-mediated glycolysis, tumor growth and metastasis</td>
<td align="center">Downregulation enhances glycolysis, cell proliferation, migration, invasion and tumor growth</td>
<td align="center">
<xref ref-type="bibr" rid="B25">Ji et al. (2024)</xref>
</td>
</tr>
<tr>
<td align="center">miR-200c-3p</td>
<td align="center">GLI3</td>
<td align="center">Regulate cell cycle and proliferation</td>
<td align="center">Downregulation promotes proliferation, invasion, and inhibits apoptosis</td>
<td align="center">
<xref ref-type="bibr" rid="B75">Yi et al. (2024)</xref>
</td>
</tr>
<tr>
<td align="center">miR-3934-5p</td>
<td align="center">TP53INP1</td>
<td align="center">Regulates cell proliferation, and apoptosis</td>
<td align="center">Upregulation leads to increased proliferation, decreased apoptosis, and enhanced DDP resistance</td>
<td align="center">
<xref ref-type="bibr" rid="B53">Ren et al. (2019)</xref>
</td>
</tr>
<tr>
<td align="center">miR-146b-5p</td>
<td align="center">PTEN</td>
<td align="center">Regulates PI3K/AKT signaling pathway, proliferation</td>
<td align="center">Upregulation promotes NSCLC proliferation and osimertinib resistance</td>
<td align="center">
<xref ref-type="bibr" rid="B55">Sang et al. (2025)</xref>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Schematic representation of experimentally validated target genes of miR-10a-5p, miR-210-3p, miR-130b-3p, miR-3934-5p, miR-200c-3p and miR-146-5p, illustrating their functional roles and biological implications in LUAD/NSCLC, based on literature data.</p>
</caption>
<graphic xlink:href="fgene-17-1769972-g005.tif">
<alt-text content-type="machine-generated">Diagram illustrates six microRNAs and their associated target genes and downstream effects in cancer. Each microRNA regulates specific genes, influencing drug resistance, proliferation, migration, invasion, tumor progression, cell survival, and immune escape.</alt-text>
</graphic>
</fig>
<p>Based on differential expression analysis conducted on the above-mentioned tissue samples miR-200c-3p expression was significantly decreased, whereas miR-146b-5p and miR-3934-5p expression was significantly increased in LUAD-BM samples compared to primary LUAD samples, while no detectable differences were observed between primary tumors and normal lung tissue. These findings suggest that the expression changes of these miRNAs are specifically associated with the development of secondary brain tumors. The upregulation of miR-146b-5p and miR-3934-5p observed in the LUAD-BM versus primary LUAD comparison is consistent with our findings in the LUAD-BM versus normal brain comparison, where we also detected upregulation.</p>
<p>According to several studies, miR-200c may exert contrasting regulatory effects depending on the cancer type, tumor microenvironment, and even the specific genetic background of the tumor cells (<xref ref-type="bibr" rid="B18">Guo et al., 2025</xref>; <xref ref-type="bibr" rid="B29">Klicka et al., 2022</xref>). However, in LUAD tissue samples compared to normal lung tissue, miR-200c has generally been identified as a tumor suppressor (<xref ref-type="bibr" rid="B35">Liu et al., 2017</xref>). It has been reported that all members of the miR-200 family are highly expressed in lung tissue but are barely detectable in the brain (<xref ref-type="bibr" rid="B62">Teplyuk et al., 2012</xref>; <xref ref-type="bibr" rid="B33">Liang et al., 2007</xref>). These data are consistent with our results, as our sequencing data indicated that normal brain expresses miR-200c-3p at significantly lower levels than normal lung. This provides an explanation for the observed relative increase of miR-200c-3p in LUAD-BM samples compared to normal brain, despite the downregulation detected in primary LUAD. MiR-200c proved to be involved in EMT in LUAD, which is a hallmark of metastasis that facilitates detachment from the primary tumor, enhanced motility, and invasion. (<xref ref-type="bibr" rid="B15">Gregory et al., 2008</xref>). Lei et al. demonstrated that miR-200c exhibits tumor-suppressive effects in NSCLC by inhibiting cell proliferation and migration through targeting lactate dehydrogenase A, representing a potential therapeutic target (<xref ref-type="bibr" rid="B32">Lei et al., 2018</xref>). Moreover, miR-200c-3p contributes to EGFR TKI sensitivity by regulating the EMT process (<xref ref-type="bibr" rid="B69">Wang et al., 2020</xref>).</p>
<p>Interestingly, both tumor-suppressive and oncogenic roles of miR-146b-5p have been reported in NSCLC, highlighting its context-dependent regulatory function. For example, Sang et al. demonstrated its significantly elevated level in NSCLC tissue samples coupled with worse prognosis. Furthermore, they proved that methyltransferase 16 mediated miR-146b-5p m6A modification led to the induction of cell proliferation and osimertinib resistance via activation of the phosphatidylinositol 3-kinase/AKT signaling pathway, and modification of miR-146b-5p may promote osimertinib resistance (<xref ref-type="bibr" rid="B55">Sang et al., 2025</xref>). These data, consistent with our results, support the notion that inhibition of miR-146b-5p overexpression could serve as a potential therapeutic strategy against LUAD-BM and the suppression of chemotherapy resistance.</p>
<p>MiR-3934-5p remains poorly characterized, as it has been rarely investigated in previous studies. Ren et al. highlighted the possible therapeutic potential of miR-3934-5p in A549 cell line, showing that inhibiting miR-3934-5p can restore tumor protein P53 inducible nuclear protein 1 expression and make LUAD cells more responsive to chemotherapeutic agents such as cisplatin. MiR-3934-5p upregulation enhances proliferation, inhibits apoptosis, and confers DDP resistance in lung cancer (<xref ref-type="bibr" rid="B53">Ren et al., 2019</xref>). We propose that inhibition of miR-3934-5p could possess possible therapeutic potential in overcoming therapy resistance.</p>
<p>The analysis revealed a significant downregulation of miR-10a-5p in primary LUAD samples compared to normal lung controls, with further decreased expression observed in LUAD-BM relative to primary LUAD. In contrast, miR-210-3p and miR-130b-3p were significantly upregulated in primary LUAD compared to normal lung, and their expression levels increased further when LUAD-BM was compared to primary LUAD. These results suggest that the expression changes of these miRNAs may be closely associated with the progression of metastasis.</p>
<p>The dual role of miR-10a-5p as both a tumor suppressor and an oncogene has been reported in several cancer types, including breast, bladder, ovarian, and gastric cancers (<xref ref-type="bibr" rid="B22">Hu et al., 2019</xref>; <xref ref-type="bibr" rid="B45">Nakayama et al., 2013</xref>; <xref ref-type="bibr" rid="B12">Gao et al., 2024</xref>; <xref ref-type="bibr" rid="B78">Zaravinos et al., 2012</xref>; <xref ref-type="bibr" rid="B72">Xiao et al., 2014</xref>; <xref ref-type="bibr" rid="B40">Ma et al., 2007</xref>; <xref ref-type="bibr" rid="B43">Moriarty et al., 2010</xref>; <xref ref-type="bibr" rid="B68">Wang et al., 2015</xref>; <xref ref-type="bibr" rid="B56">Singh et al., 2025</xref>). Additionally, previous studies have reported that miR-10a-5p acts as a tumor suppressor in gliomas by inhibiting glioma cell proliferation and migration via downregulation of tumor suppressor candidate 7 (<xref ref-type="bibr" rid="B71">Wang et al., 2023</xref>). However, only a few studies have investigated the role of miR-10a-5p in NSCLC. Yu et al. proved that upregulation of miR-10a tends to induce metastasis formation of lung cancer through the regulation of the phosphatase and tensin homolog/AKT/mitogen-activated protein kinase signaling pathway (<xref ref-type="bibr" rid="B73">Yan et al., 2015</xref>). Furthermore, silencing miR-10a reverses cisplatin resistance in human lung cancer cell lines through the transforming growth factor-beta/Smad2/signal transducer and activator of transcription 3/signal transducer and activator of transcription 5 path-way, thereby indicating a possible therapeutic relevance (<xref ref-type="bibr" rid="B61">Sun et al., 2015</xref>). Contradictory results have been reported regarding miR-10a-5p in studies of LUAD. These discrepancies may be attributed to differences in expression across racial, ethnic, and geographic populations. Contradictory results have been reported regarding miR-10a-5p in studies of LUAD. These discrepancies may be attributed to differences in expression across racial, ethnic, and geographic populations.</p>
<p>In the development and growth of metastatic brain tumors, the brain microenvironment, composed mainly of the astrocytes, plays an important role (<xref ref-type="bibr" rid="B1">Alsidawi et al., 2014</xref>). According to the results of several studies, miR-210 directly induces a change in the brain microenvironment. For example, according to the analysis of Camacho et al., miR-210 isolated from exosomes of metastatic brain cells with breast cancer origin was the only significantly over-expressed miRNA when compared to parental breast cancer cells (<xref ref-type="bibr" rid="B4">Camacho et al., 2013</xref>). In LUAD, elevated miR-210 supports survival in the brain niche by modulating mitochondrial metabolism and reducing oxidative stress (<xref ref-type="bibr" rid="B49">Puiss&#xe9;gur et al., 2011</xref>). In the context of lung cancer, inhibition of miR-210-3p in A549 cells resulted in reduced migration, invasion capacity, and cell viability, while significantly increasing the rate of apoptosis (<xref ref-type="bibr" rid="B5">Chen et al., 2021</xref>). According to another study, a three-miRNA (miR-210, miR-214, and miR-15a) based signature was able to predict BM in LUAD patients with 91.4% accuracy (<xref ref-type="bibr" rid="B81">Zhao et al., 2018</xref>). It has also been reported that high levels of miR-210-3p promote tumor progression by modulating C-C motif chemokine ligand 2 mediated macrophage recruitment and polarization under hypoxic conditions (<xref ref-type="bibr" rid="B3">Arora et al., 2024</xref>). Furthermore, Chen et al. demonstrated that silencing miR-210-3p may represent a promising approach for lung cancer treatment, as it can inhibit tumor growth, invasion, and metastasis by modulating the polycomb group ping finger 3/upstream transcription factor 1 axis, while also affecting apoptosis, angiogenesis, and EMT (<xref ref-type="bibr" rid="B5">Chen et al., 2021</xref>). Daugaard et al. also reported the upregulation of miR-210-3p and found that it was significantly associated with the development of distant metastases (<xref ref-type="bibr" rid="B8">Daugaard et al., 2017</xref>). These results, consistent with our findings, support the notion that miR-210-3p may serve as a potential therapeutic target to prevent the development of LUAD-BM.</p>
<p>Previous clinical studies have demonstrated that overexpression of miR-130b is associated with aggressive tumor phenotypes and poor prognosis across several cancer types, including glioma (<xref ref-type="bibr" rid="B1">Alsidawi et al., 2014</xref>). Moreover, elevated miR-130b expression has been significantly correlated with the formation of distant metastases in both colon and lung cancers (<xref ref-type="bibr" rid="B1">Alsidawi et al., 2014</xref>). In LUAD, Kim et al. reported that miR-130b overexpression was linked to higher histological grade, lymph node metastasis, and lymphovascular invasion, further supporting its role in tumor progression and metastatic potential (<xref ref-type="bibr" rid="B28">Kim et al., 2021</xref>). It has also been reported that upregulation of miR-130b-3p promotes lung cancer progression by regulating serine/threonine kinase 11 mediated proliferation, migration, invasion, and immune escape, as well as FOXO3-dependent kelch like ECH associated protein 1/NFE2 like BZIP transcription factor 2/thioredoxin reductase 1 signaling (<xref ref-type="bibr" rid="B6">Chen et al., 2023</xref>; <xref ref-type="bibr" rid="B17">Guo et al., 2021</xref>). Moreover, miR-130b-3p is considered a possible therapeutic target in NSCLC, as exosomal miR-130b-3p has been shown to serve as a potential predictive marker and therapeutic target by suppressing NSCLC growth, migration, and EMT, and promoting apoptosis through inhibition of DEP Domain Containing 1 (<xref ref-type="bibr" rid="B39">Lv et al., 2024</xref>). Furthermore, inhibition of miR-130b or restoration of TIMP metallopeptidase inhibitor 2 may reduce NSCLC invasion by decreasing Matrix Metallopeptidase 2 activity (<xref ref-type="bibr" rid="B21">Hirono et al., 2019</xref>; <xref ref-type="bibr" rid="B11">Frydrychowicz et al., 2023</xref>). These data, consistent with our findings&#x2013;showing that changes in miR-130b-3p expression can be detected even in primary tumor&#x2013;suggest that targeting this miRNA may play a significant role in inhibiting the metastatic process from its initial phases.</p>
<p>Furthermore, no significant differences in expression of miR-503-5p and miR-195-5p were observed between LUAD-BM and primary LUAD. However, miR-503-5p was significantly upregulated, whereas miR-195-5p was significantly downregulated in primary LUAD compared to normal lung controls.</p>
<p>MiR-503-5p shows association with regulation of the development of different cancer types like LUAD and additionally, it accelerates metastasis and angiogenesis formation in tumors, too (<xref ref-type="bibr" rid="B10">Fei et al., 2020</xref>). Recent studies have reported that miR-503-5p plays a role in mediating chemotherapy resistance in various tumor types (<xref ref-type="bibr" rid="B47">Park and Kim, 2019</xref>). Han et al. demonstrated that elevated levels of it were associated with cisplatin resistance and angiogenesis of LUAD cells via regulation of CTD small phosphatase like gene expression, suggesting that miR-503-5p may serve as a potential therapeutic target (<xref ref-type="bibr" rid="B19">Han and Wang, 2023</xref>). Moreover, Zhang et al. demonstrated that inhibition of the JMJD2C/metastasis associated lung adenocarcinoma transcript 1/miR-503-5p/Septin 2 axis may suppress NSCLC progression by modulating histone methylation (<xref ref-type="bibr" rid="B80">Zhang J. et al., 2022</xref>).</p>
<p>Deregulation of miR-195-5p has been reported in multiple cancers. Depending on the specific cancer type, it can play an oncogenic or tumor-suppressive role as a posttranscriptional regulator (<xref ref-type="bibr" rid="B41">Mohan et al., 2024</xref>). Long et al. detected the significant downregulation of miR-195-5p both in LUAD tissue samples and in lung cancer cell lines (H1299, A549) in comparison with the control samples. Additionally, they demonstrated that elevated expression of miR-195-5p can induce inhibition of lung cancer cell proliferation, migration, and invasion, leading to reduced tumor growth and metastasis formation of lung cancer via targeting forkhead box K1 (<xref ref-type="bibr" rid="B37">Long and Wang, 2020</xref>). Other studies proved the tumor suppressor activity of miR-195-5p in NSCLC through the direct regulation of MYB, insulin-like growth factor 1 receptor, hepatoma-derived growth factor or checkpoint kinase 1 (<xref ref-type="bibr" rid="B34">Liu et al., 2015</xref>; <xref ref-type="bibr" rid="B16">Guo et al., 2014</xref>; <xref ref-type="bibr" rid="B67">Wang et al., 2014</xref>; <xref ref-type="bibr" rid="B76">Yongchun et al., 2014</xref>). It was further demonstrated that miR-195 is associated with a better prognosis in lung cancer, while targeting Fibroblast Growth Factor 2 can enhance chemosensitivity in A549/DDP cells (<xref ref-type="bibr" rid="B34">Liu et al., 2015</xref>; <xref ref-type="bibr" rid="B70">Wang et al., 2021</xref>).</p>
<p>A limited number of studies have focused on the miRNA expression differences in LU-AD-BM compared to primary LUAD. Zhang et al. compared the miRNA expression pro-files of LUAD samples without BM to those with BM to identify potential biomarkers and mechanisms involved in metastasis. They identified 20 dysregulated miRNAs using next-generation RNA sequencing in an Asian population (<xref ref-type="bibr" rid="B79">Zhang L. et al., 2022</xref>). In another study, the authors identified a 25-miRNA signature based on the mRNA profiles of LUAD patients with and without BMs, based on their tissue mRNA profiles using machine learning&#x2013;based prediction (<xref ref-type="bibr" rid="B30">Koh et al., 2024</xref>). Remon et al. compared expression patterns between EGFR-mutant NSCLC samples without BMs and those with confirmed BMs (<xref ref-type="bibr" rid="B52">Remon et al., 2016</xref>).</p>
<p>Tsakonas et al. performed matched analyses to reveal a unique miRNA signature by comparing primary NSCLC samples to brain metastatic samples. The study reported downregulation of miR-129-2-3p, miR-124-3p, miR-219a-2-3p, miR-219a-5p, and miR-9-5p, and upregulation of miR-142-3p, miR-150-5p, miR-199b-5p, miR-199a-3p, and miR-199a-5p (<xref ref-type="bibr" rid="B65">Tsakonas et al., 2022</xref>). The heterogeneity observed in miRNA expression patterns is likely influenced by population-specific genetic factors that affect baseline miRNA levels. Consequently, expression patterns may vary across racial, ethnic, and geographic groups (<xref ref-type="bibr" rid="B9">Dluzen et al., 2016</xref>; <xref ref-type="bibr" rid="B46">Nassar et al., 2017</xref>; <xref ref-type="bibr" rid="B50">Rawlings-Goss et al., 2014</xref>). Therefore, geographic and ethnic context should be considered a key factor when assessing the clinical utility of miRNAs.</p>
<p>To the best of our knowledge, this is the first study to simultaneously compare the miRNA expression profiles of LUAD-BM tissues with normal brain tissue, primary LU-AD, and normal lung. This approach allows mapping of expression changes along the transition from normal lung to primary LUAD and LUAD-BM. Additionally, this is the first study specifically focusing on LUAD that utilized brain tissue samples to investigate the expression profiles of LUAD-BM. Furthermore, we identified for the first time the dysregulation of miR-10a-5p, miR-200c-3p, miR-3934-5p, miR-130b-3p, miR-146b-5p, miR-503-5p, and miR-195-5p in LUAD-BM human samples that could play an important role in the development of BM with LUAD origin. Our findings provide initial insights in-to the role of these miRNAs; however, they should be regarded as preliminary, and additional research is required to determine their diagnostic and therapeutic implications.</p>
</sec>
<sec sec-type="materials|methods" id="s4">
<label>4</label>
<title>Materials and methods</title>
<sec id="s4-1">
<label>4.1</label>
<title>Clinical sample collection and patient inclusion/exclusion criteria</title>
<p>For NGS analysis, six LUAD-BM and six control brain tissue samples were included. The miRNA expression profiles were previously published and retrieved from the Gene Expression Omnibus database (accession numbers GSE284777 and GSE244332) (<xref ref-type="bibr" rid="B64">Torner et al., 2025</xref>; <xref ref-type="bibr" rid="B14">G&#xe9;czi et al., 2025</xref>). All samples were obtained from surgical resections. In addition, 30 patients from the same cohort in each group were included for validation. LUAD-BM brain tissue samples and peritumoral brain tissue samples from patients with low-grade glioma (WHO grade I&#x2013;II), used as controls, were collected during surgery. Patients were identified between 2010 and 2024&#xa0;at the Department of Neurosurgery, University of Debrecen, Faculty of Medicine. The samples were flash frozen immediately after re-section and stored at &#x2212;80&#xa0;&#xb0;C until analysis. The samples were obtained from the right frontal or temporal lobes, and the diagnoses were confirmed by histopathological examination. None of the patients received chemotherapy or radiotherapy prior to surgery. LUAD-BM and control groups were age-matched, with an average age of 61.46 years in the LUAD-BM cohort, and the gender distribution was balanced across both groups (<xref ref-type="table" rid="T6">Table 6</xref>). The study was approved by the Scientific and Research Ethics Committee of the Medical Research Council of the Ministry of Health, Budapest, Hungary (ETT TUKEB; project identification code: IV/1753-/2021/EKU) and was conducted in accordance with the Declaration of Helsinki, and all participants signed a consent form.</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Summary of the characteristics of the 6 LUAD-BM and 6 normal brain control patients selected for NGS analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Characteristic</th>
<th align="center">Gender</th>
<th align="center">Age</th>
<th align="center">Immunohistochemical characteristics</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">LUAD-BM_1</td>
<td align="center">M</td>
<td align="center">67</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">LUAD-BM_2</td>
<td align="center">M</td>
<td align="center">71</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">LUAD-BM_3</td>
<td align="center">M</td>
<td align="center">73</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">LUAD-BM_4</td>
<td align="center">F</td>
<td align="center">66</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">LUAD-BM_5</td>
<td align="center">F</td>
<td align="center">71</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">LUAD-BM_6</td>
<td align="center">F</td>
<td align="center">59</td>
<td align="center">CK7 &#x2b;, TTF-1&#x2b;, CD &#xd7; 2 &#x2212;</td>
</tr>
<tr>
<td align="center">Controll_1</td>
<td align="center">M</td>
<td align="center">70</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Controll_2</td>
<td align="center">M</td>
<td align="center">52</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Controll_3</td>
<td align="center">M</td>
<td align="center">52</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Controll_4</td>
<td align="center">F</td>
<td align="center">71</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Controll_5</td>
<td align="center">F</td>
<td align="center">80</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Controll_6</td>
<td align="center">F</td>
<td align="center">61</td>
<td align="center">-</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s4-2">
<label>4.2</label>
<title>Small RNA library preparation and NGS</title>
<p>To investigate the miRNA expression profiles of LUAD-BM and control brain tissue samples, small-RNA-seq analysis was conducted on 12 selected brain specimens in collaboration with the Genomic Medicine and Bioinformatics Core Facility (Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen). Library preparation was performed using the NEBNext Multiplex Small RNA Prep Set for Illumina (<xref ref-type="bibr" rid="B42">Molina et al., 2008</xref>; <xref ref-type="bibr" rid="B20">Herbst et al., 2018</xref>; <xref ref-type="bibr" rid="B38">Lukas et al., 2014</xref>; <xref ref-type="bibr" rid="B60">Sperduto et al., 2020</xref>; <xref ref-type="bibr" rid="B31">Le Rhun et al., 2021</xref>; <xref ref-type="bibr" rid="B44">Najjary et al., 2023</xref>; <xref ref-type="bibr" rid="B51">Remon and Besse, 2018</xref>; <xref ref-type="bibr" rid="B57">Smolarz et al., 2022</xref>; <xref ref-type="bibr" rid="B59">Souza et al., 2023</xref>; <xref ref-type="bibr" rid="B58">Sol&#xe9; and Lawrie, 2019</xref>; <xref ref-type="bibr" rid="B26">Kim et al., 2018</xref>; <xref ref-type="bibr" rid="B63">Tominaga et al., 2015</xref>; <xref ref-type="bibr" rid="B52">Remon et al., 2016</xref>; <xref ref-type="bibr" rid="B30">Koh et al., 2024</xref>; <xref ref-type="bibr" rid="B79">Zhang L. et al., 2022</xref>; <xref ref-type="bibr" rid="B8">Daugaard et al., 2017</xref>; <xref ref-type="bibr" rid="B9">Dluzen et al., 2016</xref>; <xref ref-type="bibr" rid="B46">Nassar et al., 2017</xref>; <xref ref-type="bibr" rid="B50">Rawlings-Goss et al., 2014</xref>; <xref ref-type="bibr" rid="B82">Zhou et al., 2021</xref>; <xref ref-type="bibr" rid="B27">Kim et al., 2020</xref>; <xref ref-type="bibr" rid="B65">Tsakonas et al., 2022</xref>; <xref ref-type="bibr" rid="B24">Hudson et al., 2024</xref>; <xref ref-type="bibr" rid="B74">Yayan et al., 2024</xref>; <xref ref-type="bibr" rid="B54">Riley et al., 2019</xref>; <xref ref-type="bibr" rid="B7">Chopin et al., 2016</xref>; <xref ref-type="bibr" rid="B18">Guo et al., 2025</xref>; <xref ref-type="bibr" rid="B29">Klicka et al., 2022</xref>; <xref ref-type="bibr" rid="B35">Liu et al., 2017</xref>; <xref ref-type="bibr" rid="B62">Teplyuk et al., 2012</xref>; <xref ref-type="bibr" rid="B33">Liang et al., 2007</xref>; <xref ref-type="bibr" rid="B15">Gregory et al., 2008</xref>; <xref ref-type="bibr" rid="B32">Lei et al., 2018</xref>; <xref ref-type="bibr" rid="B69">Wang et al., 2020</xref>; <xref ref-type="bibr" rid="B55">Sang et al., 2025</xref>; <xref ref-type="bibr" rid="B53">Ren et al., 2019</xref>; <xref ref-type="bibr" rid="B22">Hu et al., 2019</xref>; <xref ref-type="bibr" rid="B45">Nakayama et al., 2013</xref>; <xref ref-type="bibr" rid="B12">Gao et al., 2024</xref>; <xref ref-type="bibr" rid="B78">Zaravinos et al., 2012</xref>; <xref ref-type="bibr" rid="B72">Xiao et al., 2014</xref>; <xref ref-type="bibr" rid="B40">Ma et al., 2007</xref>; <xref ref-type="bibr" rid="B43">Moriarty et al., 2010</xref>; <xref ref-type="bibr" rid="B68">Wang et al., 2015</xref>; <xref ref-type="bibr" rid="B56">Singh et al., 2025</xref>) kit (New England BioLabs, Ipswich, MA, United States). The integrity and quality of total RNA were assessed with the Eukaryotic Total RNA Nano Assay on an Agilent BioAnalyzer (Agilent Technologies, Santa Clara, CA, United States). For library construction, 1&#xa0;&#xb5;g of total RNA with a RIN value above 7 was used. Fragment size distribution and library molarity were verified on the Agilent BioAnalyzer DNA1000 chip (Agilent Technologies, Santa Clara, CA, United States). Sequencing was carried out on the Illumina NextSeq 500 platform (Illumina, San Diego, CA, United States) generating 50&#xa0;bp single-end reads. Raw sequence data were aligned to the human reference genome (GRCh38) using the Novoalign algorithm, optimized for short reads and miRNA-seq data. Prior to alignment, 3&#x2032; adapter sequences were removed using Novoalign&#x2019;s built-in adapter-trimming function. Both default Illumina adapter sequences and custom adapter sequences were specified to ensure accurate trimming. Reads shorter than 17 nucleotides after adapter removal were filtered out as low-quality sequences. A homopolymer filter was applied to further remove low-quality reads. During mapping to the human reference genome, only one mismatch per read was allowed, and in the case of multiple mapping locations, the alignment with the best score was retained while other potential alignments were discarded. Subsequent data processing was performed with StrandNGS v4.0 software (<ext-link ext-link-type="uri" xlink:href="http://www.strand-ngs.com">www.strand-ngs.com</ext-link>, accessed on 3 March 2021). Normalization of expression data was conducted using the DESeq algorithm, and DE miRNAs were identified by applying a moderated t-test.</p>
</sec>
<sec id="s4-3">
<label>4.3</label>
<title>Differential expression analysis</title>
<p>MiRNA expression data were analyzed using the iDEP 2.01 web-based platform (<ext-link ext-link-type="uri" xlink:href="https://bioinformatics.sdstate.edu/idep/">https://bioinformatics.sdstate.edu/idep/</ext-link>(accessed on 19 April 2024)). Hierarchical clustering was conducted by applying a Z-score cutoff of 3 to filter the data, and K-Means clustering was subsequently performed on the 100 most variable miRNAs to explore expression patterns. Principal component analysis was also utilized to visualize sample distribution based on expression profiles. Differential gene expression analysis was carried out using the DESeq2 software package, integrated within the iDEP 2.01 pipeline. The differential gene expression analysis employed an FDR threshold of 0.01 and a minimum FC of 2 to identify DE miRNAs. MiRNAs were considered significantly upregulated if their FC was &#x2265;2 with an FDR &#x2264;0.05, while miRNAs with an FC &#x2264; &#x2212;2 and an FDR &#x2264;0.05 were classified as significantly downregulated (<xref ref-type="bibr" rid="B13">Ge et al., 2018</xref>).</p>
</sec>
<sec id="s4-4">
<label>4.4</label>
<title>Bioinformatical analysis</title>
<p>To identify experimentally validated target genes of the DE miRNAs, the miRNet platform (<ext-link ext-link-type="uri" xlink:href="https://www.mirnet.ca">https://www.mirnet.ca</ext-link> (accessed on 27 June 2024)) was utilized in combination with the miRTarBase v9.0 database (<ext-link ext-link-type="uri" xlink:href="https://awi.cuhk.edu.cn/%7EmiRTarBase/miRTarBase_2025/php/index.php">https://awi.cuhk.edu.cn/&#x223c;miRTarBase/miRTarBase_2025/php/index.php</ext-link> (accessed on 15 September 2021)) for interaction network construction.</p>
<p>KEGG pathway enrichment analysis was performed using the integrated KEGG resources available within the miRNet environment, and pathways with a p-value below 0.05 were considered statistically significant. Shared target genes for the validated miRNAs were also identified based on the miRTarBase v9.0 dataset.</p>
</sec>
<sec id="s4-5">
<label>4.5</label>
<title>Tissue disruption, RNA extraction, and RT-qPCR-based validation of DE miRNAs</title>
<p>For total RNA isolation, 30&#xa0;mg of flash-frozen tissue per sample was dissected on ice. Tissue disruption and homogenization were carried out using a MagNa Lyser instrument (Roche Ltd., Basel, Switzerland) using Qiazol lysis reagent and stainless-steel beads. MiRNA-enriched total RNA was extracted using the miRNeasy Mini Kit (Qiagen, Hilden, Germany), following the manufacturer&#x2019;s protocol. The concentration and purity of the extracted RNA were assessed with a Nanodrop spectrophotometer (Thermo Scientific, Waltham, MA, United States). For the validation phase, total RNA isolated from 30 control and 30 LUAD-BM patient samples was reverse-transcribed into cDNA using the miRCURY LNA RT Kit (Qiagen, Hilden, Germany) following the manufacturer&#x2019;s protocol, with incubation at 42&#xa0;&#xb0;C for 60&#xa0;min and a final step at 95&#xa0;&#xb0;C for 5&#xa0;min to terminate the reaction. The expression levels of miR-196b-5p, miR-130b-3p, miR-200c-3p, miR-210-3p, miR-503-5p, miR-195-5p, and miR-138-2-3p were quantified by real-time PCR using the LightCycler&#xae; 96 System (Roche Ltd., Pleasanton, CA, United States) and the miRCURY LNA SYBR Green PCR Kit (Qiagen, Hilden, Germany), according to the manufacturer&#x2019;s instructions.</p>
<p>PCR conditions were set as follows: initial denaturation at 95&#xa0;&#xb0;C for 2&#xa0;min, followed by 45 cycles of denaturation at 95&#xa0;&#xb0;C for 10&#xa0;s, and annealing/extension in combination at 56&#xa0;&#xb0;C for 60&#xa0;s. Melting curves were created by fluorescent measurements in three steps (95&#xa0;&#xb0;C for 20&#xa0;s, 40&#xa0;&#xb0;C for 20&#xa0;s, and 85&#xa0;&#xb0;C for 1&#xa0;s), followed by a final cooling step at 37&#xa0;&#xb0;C for 30&#xa0;s. Relative miRNA expression levels were calculated using the comparative cycle threshold (&#x394;&#x394;Ct) method, with miR-103a-3p serving as the internal reference (&#x394;Ct &#x3d; Ct<sub>target miRNA</sub> &#x2212; Ct<sub>miR-103a-3p</sub>) (<xref ref-type="bibr" rid="B66">Veryaskina et al., 2022</xref>). All reactions were performed in triplicate to ensure reproducibility.</p>
</sec>
<sec id="s4-6">
<label>4.6</label>
<title>Statistical analysis</title>
<p>Data normality was assessed using the Shapiro&#x2013;Wilk test. Statistical analyses were performed using the non-parametric Mann&#x2013;Whitney U test in GraphPad Prism 7, and differences in miRNA expression level were considered statistically significant at <italic>p</italic> &#x3c; 0.05. ROC curve analysis was carried out using the easyROC 1.3.1 web-based tool [<ext-link ext-link-type="uri" xlink:href="http://biosoft.erciyes.edu.tr/app/easyROC/">http://biosoft.erciyes.edu.tr/app/easyROC/</ext-link>(accessed on 25 July 2016)] and 95% confidence intervals were calculated using the DeLong method. The AUC was calculated to evaluate diagnostic performance, and the optimal cut-off value was selected based on the balance between sensitivity and specificity. Optimal cut-off was determined using Youden&#x2019;s index.</p>
</sec>
<sec id="s4-7">
<label>4.7</label>
<title>TCGA-based comparative analysis</title>
<p>In addition, miRNA expression and corresponding clinical data from 20 LUAD and 15 lung control samples were obtained from TCGA database [<ext-link ext-link-type="uri" xlink:href="http://cancergenome.nih.gov/">http://cancergenome.nih.gov/</ext-link>(accessed on 7 May 2025)]. Only cases meeting the following criteria were included in the analysis: i) histopathologically confirmed LUAD; ii) absence of any other malignancy; iii) age between 50&#x2013;75 years; iv) classification as White as defined by the TCGA race variable; v) tumor grade II&#x2013;IV. The miRNA expression profiles of normal lung tissue samples were obtained from adjacent, non-tumorous lung tissues. Both miRNA expression data and clinical data are publicly available and open access (<xref ref-type="table" rid="T7">Table 7</xref>).</p>
<table-wrap id="T7" position="float">
<label>TABLE 7</label>
<caption>
<p>Summary of the characteristics of the 20 LUAD and 15 normal lung control patients selected from TCGA database.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">Characteristic</th>
<th align="center">Gender</th>
<th align="center">Age</th>
<th align="center">TNM staging</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">LUAD1</td>
<td align="center">F</td>
<td align="center">59</td>
<td align="center">N2</td>
</tr>
<tr>
<td align="center">LUAD2</td>
<td align="center">F</td>
<td align="center">61</td>
<td align="center">T2, N0</td>
</tr>
<tr>
<td align="center">LUAD3</td>
<td align="center">F</td>
<td align="center">58</td>
<td align="center">N0</td>
</tr>
<tr>
<td align="center">LUAD4</td>
<td align="center">F</td>
<td align="center">54</td>
<td align="center">N0</td>
</tr>
<tr>
<td align="center">LUAD5</td>
<td align="center">M</td>
<td align="center">59</td>
<td align="center">T2, N0</td>
</tr>
<tr>
<td align="center">LUAD6</td>
<td align="center">M</td>
<td align="center">59</td>
<td align="center">T2, N0</td>
</tr>
<tr>
<td align="center">LUAD7</td>
<td align="center">M</td>
<td align="center">56</td>
<td align="center">T3, N0</td>
</tr>
<tr>
<td align="center">LUAD8</td>
<td align="center">M</td>
<td align="center">70</td>
<td align="center">T3, N0</td>
</tr>
<tr>
<td align="center">LUAD9</td>
<td align="center">M</td>
<td align="center">73</td>
<td align="center">T3, N0</td>
</tr>
<tr>
<td align="center">LUAD10</td>
<td align="center">M</td>
<td align="center">69</td>
<td align="center">T2b, N0</td>
</tr>
<tr>
<td align="center">LUAD11</td>
<td align="center">F</td>
<td align="center">70</td>
<td align="center">T2b, N2</td>
</tr>
<tr>
<td align="center">LUAD12</td>
<td align="center">F</td>
<td align="center">56</td>
<td align="center">T1, N2</td>
</tr>
<tr>
<td align="center">LUAD13</td>
<td align="center">M</td>
<td align="center">59</td>
<td align="center">T2, N2</td>
</tr>
<tr>
<td align="center">LUAD14</td>
<td align="center">F</td>
<td align="center">53</td>
<td align="center">T1, N2</td>
</tr>
<tr>
<td align="center">LUAD15</td>
<td align="center">F</td>
<td align="center">74</td>
<td align="center">T1b, N2</td>
</tr>
<tr>
<td align="center">LUAD16</td>
<td align="center">F</td>
<td align="center">58</td>
<td align="center">T1, N2</td>
</tr>
<tr>
<td align="center">LUAD17</td>
<td align="center">M</td>
<td align="center">72</td>
<td align="center">T3, N2</td>
</tr>
<tr>
<td align="center">LUAD18</td>
<td align="center">M</td>
<td align="center">70</td>
<td align="center">T1, N2</td>
</tr>
<tr>
<td align="center">LUAD19</td>
<td align="center">M</td>
<td align="center">69</td>
<td align="center">T2, N2</td>
</tr>
<tr>
<td align="center">LUAD20</td>
<td align="center">M</td>
<td align="center">61</td>
<td align="center">T2, N2</td>
</tr>
<tr>
<td align="center">Normal lung1</td>
<td align="center">F</td>
<td align="center">68</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung2</td>
<td align="center">F</td>
<td align="center">52</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung3</td>
<td align="center">M</td>
<td align="center">58</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung4</td>
<td align="center">F</td>
<td align="center">52</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung5</td>
<td align="center">M</td>
<td align="center">60</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung6</td>
<td align="center">M</td>
<td align="center">60</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung7</td>
<td align="center">M</td>
<td align="center">72</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung8</td>
<td align="center">M</td>
<td align="center">72</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung9</td>
<td align="center">M</td>
<td align="center">61</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung10</td>
<td align="center">M</td>
<td align="center">70</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung11</td>
<td align="center">F</td>
<td align="center">69</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung12</td>
<td align="center">M</td>
<td align="center">70</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung13</td>
<td align="center">F</td>
<td align="center">64</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung14</td>
<td align="center">F</td>
<td align="center">72</td>
<td align="center">-</td>
</tr>
<tr>
<td align="center">Normal lung15</td>
<td align="center">F</td>
<td align="center">71</td>
<td align="center">-</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec sec-type="conclusion" id="s5">
<label>5</label>
<title>Conclusion</title>
<p>The identification of DE miRNAs holds promising potential for improving the diagnosis and treatment of various cancers. In our study, we identified a panel of ten miRNAs (miR-200c-3p, miR-210-3p, miR-10a-5p, miR-130b-3p, miR-146b-5p, miR-503-5p, miR-196b-5p, and miR-3934-5p) using NGS and RT-qPCR methods. This panel was able to distinguish LUAD-BM from normal brain tissue samples with excellent sensitivity and specificity in the Hungarian population. Differential expression analysis of 20 LUAD and 15 normal lung samples (selected from the TCGA database representing European populations), together with LUAD-BM samples, confirmed the dysregulation of six of the ten validated miRNAs. Among these, miR-200c-3p, miR-146b-5p, and miR-3934-5p differed significantly between primary LUAD and LUAD-BM, while miR-10a-5p, miR-210-3p, and miR-130b-3p showed progressive dysregulation along the normal lung&#x2013;LUAD&#x2013;LU-AD-BM axis. This study is the first to integrate brain tissue samples in the analysis of miRNA expression in LUAD-BM while simultaneously comparing LUAD-BM with nor-mal brain, primary LUAD, and normal lung tissues. Our findings are consistent with previously published data and support the crucial role of these miRNAs in the development of BM.</p>
<p>A limitation of this study is the relatively small sample size, highlighting the need for validation in larger independent cohorts. In addition, the lack of primary lung tumor samples restricted direct comparison between primary LUAD and LUAD brain metastases, which should be addressed in future studies using matched tumor pairs.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The datasets analysed for this study can be found in the NCBI Gene Expression Omnibus (GEO) database at <ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</ext-link> and can be accessed with the accession number GSE284777 and GSE244332.</p>
</sec>
<sec sec-type="ethics-statement" id="s7">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Scientific and Research Ethics Committee of the Medical Research Council of the Ministry of Health, Budapest, Hungary (ETT TUKEB; project identification code: IV/1753-/2021/EKU) date of approval: 24 March 2021) and was conducted in accordance with the Declaration of Helsinki, and each patient signed the consent form. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s8">
<title>Author contributions</title>
<p>BT: Formal Analysis, Project administration, Visualization, Data curation, Validation, Investigation, Writing &#x2013; review and editing, Writing &#x2013; original draft, Software. &#xc1;K: Resources, Writing &#x2013; review and editing, Funding acquisition, Conceptualization. IB: Methodology, Resources, Writing &#x2013; review and editing, Conceptualization. AP: Methodology, Writing &#x2013; review and editing, Software, Visualization. DG: Data curation, Software, Writing &#x2013; original draft. TG: Investigation, Writing &#x2013; original draft. GG: Investigation, Writing &#x2013; original draft. ZB: Supervision, Methodology, Writing &#x2013; review and editing, Conceptualization, Investigation, Writing &#x2013; original draft, Formal Analysis, Software.</p>
</sec>
<ack>
<title>Acknowledgements</title>
<p>The results published here are in part based upon data generated by the TCGA Research Network: <ext-link ext-link-type="uri" xlink:href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</ext-link>. We are grateful to Szil&#xe1;rd P&#xf3;liska and UD GenoMed Medical Ge-nomic Technologies Kft. for their valuable contributions to this work.</p>
</ack>
<sec sec-type="COI-statement" id="s10">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s11">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s12">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="s13">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fgene.2026.1769972/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2026.1769972/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.docx" id="SM1" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3273822/overview">Jennifer Sally Samson</ext-link>, Sri Ramachandra Institute of Higher Education and Research, India</p>
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