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<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
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<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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<article-id pub-id-type="publisher-id">1666142</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2026.1666142</article-id>
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<subject>Original Research</subject>
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<title-group>
<article-title>Structural pharmacogenomics of drug-associated SNPs in oral squamous cell carcinoma</article-title>
<alt-title alt-title-type="left-running-head">Sunil 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.2026.1666142">10.3389/fgene.2026.1666142</ext-link>
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<name>
<surname>Sunil</surname>
<given-names>Punya</given-names>
</name>
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<sup>&#x2020;</sup>
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<name>
<surname>Kumar</surname>
<given-names>K. C. Ananth</given-names>
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<sup>&#x2020;</sup>
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<surname>Ananthakrishnan</surname>
<given-names>A. I.</given-names>
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<surname>Gupta</surname>
<given-names>Sebanti</given-names>
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<surname>Das</surname>
<given-names>Ranajit</given-names>
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<aff id="aff1">
<institution>Division of Data Analytics, Bioinformatics and Structural Biology (DABS), Yenepoya Research Centre, Yenepoya (Deemed to be University)</institution>, <city>Mangalore</city>, <state>Karnataka</state>, <country country="IN">India</country>
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<author-notes>
<corresp id="c001">
<label>&#x2a;</label>Correspondence: Ranajit Das, <email xlink:href="mailto:das.ranajit@gmail.com">das.ranajit@gmail.com</email>; Sebanti Gupta, <email xlink:href="mailto:sebantigupta@gmail.com">sebantigupta@gmail.com</email>
</corresp>
<fn fn-type="equal" id="fn001">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work and share first authorship</p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-12">
<day>12</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>1666142</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>07</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>12</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>12</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Sunil, Kumar, Ananthakrishnan, Gupta and Das.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Sunil, Kumar, Ananthakrishnan, Gupta and Das</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-12">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>Pharmacogenomics enables the interpretation of how genetic variation influences drug response, offering a route toward precision oncology. Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy characterized by marked inter-individual variability in response to chemotherapy and radiotherapy, particularly in South Asian populations. However, the mechanism through which OSCC-associated genetic variants alter protein structure and drug interactions remains poorly understood.</p>
</sec>
<sec>
<title>Methods</title>
<p>To address this, we integrated OSCC-specific variants from a Southern Indian patient cohort with pharmacogenomic annotations from ClinPGx. First, OSCC variants were mapped to drug-associated single-nucleotide proteins, yielding 22 protein-altering variants. Second, structural availability and functional relevance were used to prioritize eight variants for detailed analysis. Third, homology modeling and molecular docking were applied to evaluate how these variants influence protein conformation and drug binding.</p>
</sec>
<sec>
<title>Result</title>
<p>This multistep pipeline identified variants in <italic>FLT3, ATM, MUTYH, XRCC1, XPC</italic>, and <italic>MSH3</italic> that affect DNA repair, signaling, and drug interaction interfaces. The highly prevalent <italic>FLT3</italic> T227M (rs1933437) variant was predicted to alter receptor dimerization and potentially modulate sunitinib binding. The <italic>MUTYH</italic> Q310H (rs3219489) variant, which is located near a zinc-binding motif in the interdomain connector, was predicted to perturb metal coordination and enzyme architecture, which indicates impaired base-excision repair.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>These findings demonstrate how pharmacogenomics-guided structural analysis can reveal mechanistic links between OSCC-associated variants and therapeutic response. While our results are based on <italic>in silico</italic> modeling, they provide a biologically grounded framework for prioritizing variants for experimental validation and for advancing population-specific precision oncology in OSCC.</p>
</sec>
</abstract>
<kwd-group>
<kwd>chemoresistance</kwd>
<kwd>DNA repair genes</kwd>
<kwd>oral squamous cell carcinoma</kwd>
<kwd>personalized medicine</kwd>
<kwd>pharmacogenomics</kwd>
<kwd>single-nucleotide polymorphisms</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. The Article Processing Charges (APC) was covered by Yenepoya (Deemed to be University), Mangalore, Karnataka, India.</funding-statement>
</funding-group>
<counts>
<fig-count count="10"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="111"/>
<page-count count="21"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Pharmacogenetics and Pharmacogenomics</meta-value>
</custom-meta>
</custom-meta-group>
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</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Pharmacogenomics, the study of how genetic variations influence drug responses, is revolutionizing the field of medicine by enabling personalized treatment strategies tailored to an individual patient&#x2019;s genetic profile (<xref ref-type="bibr" rid="B28">Evans and Relling, 1999</xref>; <xref ref-type="bibr" rid="B90">Stefanicka-Wojtas and Kurpas, 2023</xref>). This approach replaces the prevalent medical practice of the &#x201c;one-size-fits-all&#x201d; approach, optimizing therapeutic efficacy while minimizing the occurrence of adverse drug reactions (<xref ref-type="bibr" rid="B98">Wang and Wang, 2023</xref>). The integration of pharmacogenomics into clinical practice has demonstrated significant potential across a range of medical disciplines, particularly in oncology, where it addresses the complexities of cancer treatment and drug resistance mechanisms (<xref ref-type="bibr" rid="B8">Anghel et al., 2024</xref>; <xref ref-type="bibr" rid="B23">Crews et al., 2012</xref>).</p>
<p>Cancer, characterized by its genetic and molecular heterogeneity, is a prime target for pharmacogenomic applications. Variations such as single-nucleotide polymorphisms (SNPs), copy number alterations, and somatic mutations profoundly influence the efficacy of chemotherapeutic agents [cisplatin (<xref ref-type="bibr" rid="B80">Rocha et al., 2018</xref>)], targeted therapy [vemurafenib (<xref ref-type="bibr" rid="B20">Chapman et al., 2011</xref>)], and immunotherapy [pembrolizumab (<xref ref-type="bibr" rid="B58">Marcus et al., 2019</xref>; <xref ref-type="bibr" rid="B47">Konda et al., 2023</xref>)]. For example, polymorphisms in genes encoding drug-metabolizing enzymes such as CYP2D6, DPYD, and UGT1A1 are known to affect the metabolism of widely used anticancer agents tamoxifen, fluoropyrimidines, and irinotecan, respectively (<xref ref-type="bibr" rid="B83">S&#xe1;nchez-Bayona et al., 2025</xref>; <xref ref-type="bibr" rid="B96">Varughese et al., 2020</xref>). Understanding how structural changes in mutated proteins affect their function enables clinicians to personalize drug selection and dosing, thus reducing toxicity and improving outcomes (<xref ref-type="bibr" rid="B102">Weinshilboum and Wang, 2017</xref>).</p>
<p>The integration of pharmacogenomics into cancer treatment is also transforming immunotherapy, particularly in the context of immune checkpoint inhibitors. Polymorphisms in immune regulatory genes, such as those encoding PD-L1 and CTLA-4, have been associated with variability in the response to checkpoint inhibitors such as pembrolizumab and ipilimumab (<xref ref-type="bibr" rid="B16">Buchbinder and Desai, 2016</xref>). These insights not only optimize patient selection but also pave the way for the development of combination therapies that enhance therapeutic efficacy.</p>
<p>Chemotherapy resistance is a formidable challenge in oncology, accounting for up to 90% of cancer-related deaths (<xref ref-type="bibr" rid="B5">Alfarouk et al., 2015</xref>). Both intrinsic and acquired resistance mechanisms, often driven by genetic and epigenetic alterations, contribute to treatment failure and disease recurrence (<xref ref-type="bibr" rid="B25">Dhanyamraju, 2024</xref>). For instance, mutations in the <italic>EGFR</italic> and <italic>KRAS</italic> genes are associated with resistance to tyrosine kinase inhibitors and cetuximab, respectively, highlighting the importance of genetic profiling in selecting appropriate targeted therapies (<xref ref-type="bibr" rid="B53">Li et al., 2018</xref>). Pharmacogenomic studies have also elucidated the role of the ABC transporter family in multidrug resistance, where overexpression of P-glycoprotein reduces the intracellular accumulation of chemotherapeutic agents (<xref ref-type="bibr" rid="B33">Gottesman et al., 2002</xref>). Addressing these resistance mechanisms through genetic insights has become a focal point of precision oncology.</p>
<p>Oral squamous cell carcinoma (OSCC), a subset of head and neck squamous cell carcinoma (HNSCC), underscores the critical need for implementing pharmacogenomic approaches in cancer. Representing over 90% of oral-cavity cancers, OSCC is the sixth- most common malignancy worldwide, with the annual incidence exceeding 500,000 cases (<xref ref-type="bibr" rid="B31">Giovannacci et al., 2016</xref>). OSCC, which accounts for more than 90% of malignancies of the oral cavity, represents a major clinical challenge due to late-stage diagnosis, aggressive local invasion, and frequent resistance to chemo-radiotherapy. OSCC is particularly prevalent in South and Southeast Asia, where the concurrent use of tobacco, smoking, alcohol, and betel nut acts synergistically with genetic susceptibility to drive tumorigenesis. Despite standardized treatment regimens combining surgery, radiotherapy, and platinum-based chemotherapy, patient outcomes remain highly variable, highlighting the need for genetically informed therapeutic stratification.</p>
<p>Recent genomic studies have identified recurrent variants in DNA repair and signaling genes such as <italic>FLT3, XRCC1, XPC, MUTYH, ATM, ERCC5, EGFR,</italic> and <italic>MSH3</italic> in OSCC patients (<xref ref-type="bibr" rid="B75">Paul et al., 2025</xref>), many of which are also implicated in drug sensitivity, toxicity, and resistance. Several of these genes interact directly or indirectly with widely used agents, including cisplatin, doxorubicin, sunitinib, and radiotherapy-induced DNA damage, making them particularly relevant for pharmacogenomic evaluation. Understanding how OSCC-specific variants alter the protein structure and drug-binding behavior is, therefore, essential for predicting the treatment response and minimizing toxicity in this malignancy.</p>
<p>To understand how cancer-associated SNPs contribute to disease onset and therapeutic resistance, it is essential to evaluate their impact at the protein structural and functional levels. Recent advancements in bioinformatics and structural biology have substantially improved our ability to analyze how genetic mutations influence drug-binding sites and protein stability, thereby regulating therapeutic outcomes (<xref ref-type="bibr" rid="B6">Ammar et al., 2023</xref>). Sequence- and structure-based databases, homology modeling (e.g., SWISS-MODEL), molecular docking, and molecular dynamics simulations facilitate the prediction of mutation-induced conformational changes that affect drug interactions and resistance (<xref ref-type="bibr" rid="B47">Konda et al., 2023</xref>). For example, identification of BRAF V600E mutations in melanoma led to the development of vemurafenib, demonstrating how structural pharmacogenomics can guide drug discovery (<xref ref-type="bibr" rid="B20">Chapman et al., 2011</xref>).</p>
<p>Despite its transformative potential, challenges still remain in incorporating pharmacogenomics into routine oncology practice. Variability in drug metabolism, limited access to genetic testing, and the need for robust clinical guidelines are significant barriers (<xref ref-type="bibr" rid="B28">Evans and Relling, 1999</xref>). However, ongoing research and technological advancements are addressing these limitations, fostering a future where cancer treatments are safer, more effective, and personalized to the genetic profiles of individual patients (<xref ref-type="bibr" rid="B90">Stefanicka-Wojtas and Kurpas, 2023</xref>).</p>
<p>To address the gap in OSCC-specific pharmacogenomic insights and understand how genetic alterations prevalent in the Southwest Indian population may influence therapeutic responses, we integrated mutational profiling of genes identified in a recent study on 33 oral cell carcinoma patients (<xref ref-type="bibr" rid="B75">Paul et al., 2025</xref>) with structural and drug-interaction analyses. By annotating clinically relevant SNPs with known chemotherapeutic associations and evaluating their potential impact on protein conformation and drug binding, this study aims to identify molecular determinants that may underlie treatment resistance or toxicity in OSCC. The aim of this study was to integrate OSCC-specific genetic variation with pharmacogenomic knowledge and structural modeling in order to identify drug-associated SNPs that may influence the protein function, treatment response, and therapeutic vulnerability in OSCC.</p>
</sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<p>We curated cancer-associated drug&#x2013;variant annotations from ClinPGx and OSCC-specific variants from a Southern Indian patient cohort. These datasets were integrated to identify pharmacogenomically relevant SNPs in OSCC. The variants were then filtered based on functional and structural criteria to select proteins that are amenable to modeling. Finally, homology modeling and molecular docking were performed to evaluate how prioritized SNPs alter the protein structure and drug interactions. <xref ref-type="fig" rid="F1">Figure 1</xref> provides an overview of the integrated analytical workflow, illustrating the sequential steps from pharmacogenomic and OSCC variant curation through SNP filtering, protein structure modeling, and molecular docking to identify drug-associated structural effects in OSCC.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Summary of the methods used in this study.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g001.tif">
<alt-text content-type="machine-generated">Summary of the methods used in this study.</alt-text>
</graphic>
</fig>
<sec id="s2-1">
<title>Pharmacological and pharmacogenomics data Collection</title>
<p>Drug and variant data related to cancer were retrieved from the ClinPGx database (formerly PharmGKB, last accessed on 16th April 2025) (<ext-link ext-link-type="uri" xlink:href="https://www.clinpgx.org/">https://www.clinpgx.org</ext-link>) and utilized for further analysis. Similarly, data on chemotherapeutic drugs for cancer were curated. A total of 909 pharmacogenomic SNPs were identified as being associated with chemotherapeutic responses in cancer. The drug associations and their directions of effect were further obtained from ClinPGx, revealing that different drugs exhibit varying impacts on oral cancer. These 909 SNPs were found to be associated with 22 drugs based on ClinPGx annotations.</p>
<p>Here, we note that while the primary focus of this study is on OSCC biological mechanisms, ClinPGx curates pharmacogenomic associations across multiple cancer types and therapeutic contexts. Therefore, all cancer-related drug&#x2013;variant annotations were considered during the initial data integration in order to capture variants with established effects on chemotherapy response, toxicity, or resistance that may also be relevant to OSCC. These multicancer annotations were subsequently mapped onto the OSCC-specific variant dataset derived from <xref ref-type="bibr" rid="B75">Paul et al. (2025)</xref>, enabling disease-focused but pharmacogenomically comprehensive analysis.</p>
</sec>
<sec id="s2-2">
<title>OSCC data curation</title>
<p>Genes and genetic variants identified from the OSCC cohort recruited from Southern India (<xref ref-type="bibr" rid="B75">Paul et al., 2025</xref>; data available at <ext-link ext-link-type="uri" xlink:href="https://bcga.iitm.ac.in/cbioportal/study/summary?id=HNSCC_Yenepoya_2025">https://bcga.iitm.ac.in/cbioportal/study/summary?id&#x3d;HNSCC_Yenepoya_2025</ext-link>) were annotated with drug&#x2013;variant information using ClinPGx (<xref ref-type="table" rid="T1">Table 1</xref>). ClinPGx integrates pharmacogenomic evidence from multiple ethnicities and global populations, enabling comparison of locally observed OSCC variants with internationally curated drug-response data. Through this integration, 34 SNPs mapping to 28 pharmacogenomically relevant genes were identified as being associated with at least one chemotherapeutic agent, with annotations including drug response and direction of effect. These gene&#x2013;drug&#x2013;variant relationships form the basis for subsequent structural and functional analyses.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Summary of genes identified from a Southern Indian OSCC cohort (<xref ref-type="bibr" rid="B75">Paul et al., 2025</xref>) that harbor SNPs associated with chemotherapeutic drug-response according to ClinPGx. For each gene, the corresponding drug(s) and associated SNPs are shown, representing the initial gene&#x2013;drug&#x2013;variant space from which the structurally analyzable SNPs were subsequently filtered.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Genes<xref ref-type="table-fn" rid="Tfn1">
<sup>a</sup>
</xref>
</th>
<th align="center">SNPs</th>
<th align="center">Drugs</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<italic>EGFR</italic>
</td>
<td align="left">rs2293347and rs2227983</td>
<td align="left">Imatinib, cetuximab, EGFR inhibitors, fluorouracil, and gefitinib</td>
</tr>
<tr>
<td align="left">
<italic>TP53</italic>
</td>
<td align="left">rs1042522</td>
<td align="left">Platinum compounds, antineoplastic agents, capecitabine, paclitaxel, cisplatin, and oxaliplatin</td>
</tr>
<tr>
<td align="left">
<italic>ERCC5</italic>
</td>
<td align="left">rs1047768and rs17655</td>
<td align="left">Fluorouracil, platinum compounds, radiotherapy, and platinum</td>
</tr>
<tr>
<td align="left">
<italic>ERCC2</italic>
</td>
<td align="left">rs1799793</td>
<td align="left">Bevacizumab, bleomycin, capecitabine, cisplatin, docetaxel, epirubicin, etoposide, fluorouracil, gemcitabine, oxaliplatin, platinum compounds, radiotherapy, and trastuzumab</td>
</tr>
<tr>
<td align="left">
<italic>DHFR/MSH3</italic>
</td>
<td align="left">rs1650697</td>
<td align="left">Pemetrexed</td>
</tr>
<tr>
<td align="left">
<italic>RET</italic>
</td>
<td align="left">rs1799939</td>
<td align="left">Imatinib</td>
</tr>
<tr>
<td align="left">
<italic>ATM</italic>
</td>
<td align="left">rs1801516</td>
<td align="left">Cyclophosphamide, doxorubicin, and fluorouracil</td>
</tr>
<tr>
<td align="left">
<italic>KDR</italic>
</td>
<td align="left">rs1870377 and rs2305948</td>
<td align="left">Cisplatin, fluorouracil, oxaliplatin, imatinib, sorafenib, and sunitinib</td>
</tr>
<tr>
<td align="left">
<italic>FLT3</italic>
</td>
<td align="left">rs1933437</td>
<td align="left">Imatinib and sunitinib</td>
</tr>
<tr>
<td align="left">
<italic>XPC</italic>
</td>
<td align="left">rs2228000 and rs2228001</td>
<td align="left">Platinum compounds</td>
</tr>
<tr>
<td align="left">
<italic>ERBB2</italic>
</td>
<td align="left">rs1136201</td>
<td align="left">Carboplatin, docetaxel, and trastuzumab</td>
</tr>
<tr>
<td align="left">
<italic>ERBB3</italic>
</td>
<td align="left">rs773123</td>
<td align="left">Carboplatin, docetaxel, and trastuzumab</td>
</tr>
<tr>
<td align="left">
<italic>XRCC1</italic>
</td>
<td align="left">rs1799782 and rs25487</td>
<td align="left">Carboplatin, cisplatin, cyclophosphamide, doxorubicin, fluorouracil, irinotecan, leucovorin, oxaliplatin, platinum, platinum compounds, and radiotherapy</td>
</tr>
<tr>
<td align="left">
<italic>MUTYH</italic>
</td>
<td align="left">rs3219489</td>
<td align="left">Cisplatin, fluorouracil, and radiotherapy</td>
</tr>
<tr>
<td align="left">
<italic>FGFR4</italic>
</td>
<td align="left">rs351855</td>
<td align="left">Cyclophosphamide, fluorouracil, methotrexate, and everolimus</td>
</tr>
<tr>
<td align="left">
<italic>PDGFRA</italic>
</td>
<td align="left">rs35597368</td>
<td align="left">Imatinib</td>
</tr>
<tr>
<td align="left">
<italic>FLT1</italic>
</td>
<td align="left">rs7993418</td>
<td align="left">Cisplatin, fluorouracil, oxaliplatin, and imatinib</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="Tfn1">
<label>
<sup>a</sup>
</label>
<p>Genes were curated from <xref ref-type="bibr" rid="B75">Paul et al.(2025)</xref> (data available at <ext-link ext-link-type="uri" xlink:href="https://bcga.iitm.ac.in/cbioportal/study/summary?id=HNSCC_Yenepoya_2025">https://bcga.iitm.ac.in/cbioportal/study/summary?id&#x3d;HNSCC_Yenepoya_2025</ext-link>) and annotated with drug-variant information from ClinPGx.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2-3">
<title>SNP data curation</title>
<p>Data for each SNP were retrieved from the dbSNP web server (<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/snp/">https://www.ncbi.nlm.nih.gov/snp/</ext-link>). The reference and alternative alleles were noted. The wild-type and mutant amino acids associated with each SNP were also recorded and used for downstream protein structural analysis.</p>
</sec>
<sec id="s2-4">
<title>Screening of proteins based on structural and functional criteria</title>
<p>Genes identified in the Southern Indian OSCC cohort (<xref ref-type="bibr" rid="B75">Paul et al., 2025</xref>) were systematically filtered based on the mutation type, prevalence, and structural or functional relevance. These genes and their associated variants were then cross-referenced with ClinPGx to identify SNPs with documented or predicted associations to chemotherapeutic drug responses. This integration yielded 34 pharmacogenomically relevant SNPs distributed across 28 genes. Variants resulting in synonymous substitutions or noncoding transcript changes (12 SNPs) were excluded from structural analyses, leaving 22 protein-altering SNPs for downstream analysis, as summarized in <xref ref-type="table" rid="T1">Table 1</xref>.</p>
</sec>
<sec id="s2-5">
<title>Structural and functional evaluation of the selected SNPs</title>
<p>The SNP set was narrowed from 22 to 8 based on the availability of experimentally determined protein structures in the RCSB Protein Data Bank (PDB) and UniProt structural repositories, which were required for reliable downstream modeling and docking. These eight SNPs were thoroughly analyzed using UniProt (<xref ref-type="bibr" rid="B93">The UniProt Consortium, 2025</xref>), RCSB PDB (<xref ref-type="bibr" rid="B18">Burley et al., 2025</xref>; <xref ref-type="bibr" rid="B78">RCSB, 2025</xref>), and dbSNP (<xref ref-type="bibr" rid="B88">Sherry et al., 2001</xref>) for the identification of the mutations present, the potential functional effects on cancer biology, the prevalence of the particular mutation in cancer, and finally, their structural characterization. Varying effects of these mutations in different populations were further evaluated from the existing literature.</p>
<p>
<xref ref-type="table" rid="T2">Table 2</xref> summarizes the eight OSCC-associated genes and variants selected for detailed structural analysis after ClinPGx-based filtering. For each entry, the table lists the corresponding SNP (rsID), amino-acid substitution, UniProt accession, availability of experimental or modeled protein structures, and the source references used to support the functional and structural relevance. These data were compiled from UniProt, RCSB Protein Data Bank (PDB), dbSNP, and published literature and served as the basis for selecting variants that are suitable for homology modeling, docking, and structural interpretation.</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Structural and functional annotation of the eight OSCC-associated variants selected for downstream modeling and docking. For each gene, the corresponding SNP (rsID), amino-acid substitution, and PDB ID are shown. References indicate the primary literature that determined the structure of these proteins experimentally.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Protein</th>
<th align="left">PDB ID</th>
<th align="left">Variant (rsID)</th>
<th align="left">Amino acid substitution</th>
<th align="left">References</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">ATM</td>
<td align="left">7SIC</td>
<td align="left">rs1801516</td>
<td align="left">D1853N</td>
<td align="left">
<xref ref-type="bibr" rid="B100">Warren and Pavletich (2022)</xref>
</td>
</tr>
<tr>
<td align="left">XRCC1</td>
<td align="left">2D8M</td>
<td align="left">rs1799782 and rs25487</td>
<td align="left">R194W and Q399R</td>
<td align="left">
<xref ref-type="bibr" rid="B66">Nagashima et al. (2006)</xref>
</td>
</tr>
<tr>
<td align="left">FLT3</td>
<td align="left">3QS7</td>
<td align="left">rs1933437</td>
<td align="left">T227M</td>
<td align="left">
<xref ref-type="bibr" rid="B97">Verstraete et al. (2011)</xref>
</td>
</tr>
<tr>
<td align="left">MUTYH</td>
<td align="left">8FAY</td>
<td align="left">rs3219489</td>
<td align="left">Q310H</td>
<td align="left">
<xref ref-type="bibr" rid="B94">Trasvina-Arenas et al. (2024)</xref>
</td>
</tr>
<tr>
<td align="left">XPC</td>
<td align="left">8EBU</td>
<td align="left">rs2228000 and rs2228001</td>
<td align="left">A499V and Q939K</td>
<td align="left">
<xref ref-type="bibr" rid="B46">Kim et al. (2023)</xref>
</td>
</tr>
<tr>
<td align="left">MSH3</td>
<td align="left">8OLX</td>
<td align="left">rs1650697</td>
<td align="left">I79V</td>
<td align="left">
<xref ref-type="bibr" rid="B51">Lee et al. (2024)</xref>
</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s2-6">
<title>Structural modeling and interaction visualization</title>
<p>Because MUTYH plays a critical role in base excision repair through a zinc-dependent inter-domain connector that stabilizes DNA binding and enzymatic activity, we specifically examined how the OSCC-associated Q310H substitution (rs3219489) might perturb zinc coordination and protein architecture. To study the altered networking of zinc in the mutated MUTYH protein, mouse MUTYH is used as a template to generate the structure of human MUTYH by using the SWISS-MODEL (<xref ref-type="bibr" rid="B101">Waterhouse et al., 2018</xref>) server in the presence of the Zn coordinate. The docking of FLT3 and sunitinib was performed to generate a representative binding model for the visualization of protein&#x2013;ligand interaction on AutoDock (<xref ref-type="bibr" rid="B65">Morris et al., 2009</xref>) using different docking parameters (<xref ref-type="bibr" rid="B48">Kumar et al., 2025</xref>). Among the drug&#x2013;protein interaction complexes generated, the complex that best reproduced the previously reported interaction (<xref ref-type="bibr" rid="B3">Acharya et al., 2022</xref>) pattern details was selected. The final model-figures were generated using PyMOL software (<xref ref-type="bibr" rid="B86">Schr&#xf6;dinger, 2023</xref>).</p>
</sec>
</sec>
<sec sec-type="results|discussion" id="s3">
<title>Results and discussion</title>
<sec id="s3-1">
<title>Analysis of genes and genetic variants</title>
<p>The variants discussed in this section represent the OSCC-associated SNPs derived from the Southern Indian cohort of <xref ref-type="bibr" rid="B75">Paul et al. (2025)</xref> that were retained after cross-referencing with ClinPGx to identify pharmacogenomically annotated drug-response variants. We shortlisted the SNPs that are present in at least 70% of the recruited patients. These mutations were further assessed to explore their implications in cancer. Importantly, all the SNPs described below are those with documented or predicted drug-response associations in PharmGKB rather than the complete set of variants detected in the OSCC cohort.</p>
<p>The SNP rs7993418, which is present among all recruited patients and is located in exon 28 of the <italic>VEGFR1</italic> gene, has previously been associated with progression-free survival (PFS) in metastatic renal cell carcinoma (mRCC) patients undergoing bevacizumab treatment. This synonymous SNP affects the tyrosine 1,213 residue in the VEGFR1 tyrosine-kinase domain, altering codon usage and increasing <italic>VEGFR1</italic> expression and signaling. Studies, including the AVOREN trial, demonstrated a correlation between rs7993418 genotypes and treatment outcomes, with patients harboring the TT genotype showing the longest PFS. This SNP&#x2019;s predictive value underscores its potential as a biomarker for guiding anti-angiogenic therapy in mRCC patients. Furthermore, the rs7993418 SNP (Tyr1213Ter) leads to a shift in codon usage, enhancing <italic>VEGFR1</italic> expression and downstream signaling, which is associated with a higher risk of disease progression and increased mortality (<xref ref-type="bibr" rid="B15">Beuselinck et al., 2014</xref>; <xref ref-type="bibr" rid="B22">Claes et al., 2012</xref>; <xref ref-type="bibr" rid="B73">Pallaud et al., 2014</xref>).</p>
<p>The <italic>CCND1</italic> gene, which regulates the normal cell cycle, was also implicated, particularly the rs9344 polymorphism located at the exon 4&#x2013;intron boundary. This SNP has been linked to various cancers, including cervical, prostate, colorectal, and head and neck squamous cell carcinoma. The <italic>CCND1</italic> G870A polymorphism (rs9344) has shown potential as a prognostic biomarker for cancer, especially in the Indian population, where it may help predict the risk of cancers such as breast, esophageal, and colorectal cancer (<xref ref-type="bibr" rid="B92">Thakur et al., 2018</xref>).</p>
<p>Another significant SNP, rs1933437, has been associated with toxicity in patients treated with sunitinib, highlighting its potential to influence treatment outcomes (<xref ref-type="bibr" rid="B4">Alarc&#xf3;n-Payer et al., 2022</xref>). The <italic>DHFR</italic> gene, with its rs1650697 (G&#x3e;A) variant, has also been identified as a favorable prognostic marker in non-small-cell lung cancer (NSCLC), indicating its role in risk stratification and personalized treatment (<xref ref-type="bibr" rid="B42">Jin et al., 2010</xref>; <xref ref-type="bibr" rid="B105">Xu et al., 2015</xref>).</p>
<p>The rs2228001 SNP in the <italic>XPC</italic> gene and, specifically, the AC/CC variants were significantly associated with skin dermatitis and elevated C-reactive protein (CRP) levels, indicating that mutations in <italic>XPC</italic> impair nucleotide excision repair mechanisms and increase the risk of severe skin reactions and systemic inflammation (<xref ref-type="bibr" rid="B57">Ma&#x107;kowiak et al., 2024</xref>).</p>
<p>The <italic>ERCC1</italic> rs11615 polymorphism was significantly associated with an increased risk of breast cancer, particularly in Asian populations, underlining its potential role as a risk factor for breast cancer. Additionally, the synonymous rs7121 SNP in the <italic>GNAS</italic> gene, which plays a role in G-protein receptor signaling, was found to have implications for tumor progression and prolonged survival, thus showing promise as a biomarker for predicting therapy response (<xref ref-type="bibr" rid="B53">Li et al., 2018</xref>; <xref ref-type="bibr" rid="B63">M&#xf6;hlendick et al., 2019</xref>).</p>
<p>High <italic>XRCC1</italic> expression, which is associated with poor overall survival and minor treatment response, was linked to the rs25487 polymorphism. This variant was associated with a reduced risk of minor treatment responses in esophageal cancer and an increased risk of high-grade side effects in head and neck cancer (<xref ref-type="bibr" rid="B32">Gong et al., 2021</xref>; <xref ref-type="bibr" rid="B106">Xu et al., 2018</xref>).</p>
</sec>
<sec id="s3-2">
<title>Structural analysis</title>
<p>The proteins housing the 22 extracted SNPs (see Methods) are either involved in DNA repairing; cell proliferation, migration, and survival; or in cell signaling. While most of them have already been identified to be involved in different human carcinoma (<xref ref-type="bibr" rid="B62">McDuff et al., 2021</xref>; <xref ref-type="bibr" rid="B69">Narita et al., 2009</xref>; <xref ref-type="bibr" rid="B82">Sahu et al., 2016</xref>; <xref ref-type="bibr" rid="B104">Xiao et al., 2017</xref>), only a handful of these, namely, <italic>MUTYH</italic> [rs3219489 (Q310H)] (<xref ref-type="bibr" rid="B87">Senghore et al., 2020</xref>), <italic>XRCC1</italic> [rs1799782 (R194W); rs25487 (Q399R)] (<xref ref-type="bibr" rid="B87">Senghore et al., 2020</xref>), <italic>ERCC5</italic> [rs1047768 (H46Q); rs17655 (D1104H)] (<xref ref-type="bibr" rid="B111">Zuo et al., 2022</xref>), <italic>EGFR</italic> [rs2293347 (D994E); rs2227983 (R521K)] (<xref ref-type="bibr" rid="B85">Saravani et al., 2020</xref>), <italic>XPC</italic> [rs2228000 (A499V); rs2228001 (Q939K)] (<xref ref-type="bibr" rid="B70">Nigam et al., 2019</xref>), and <italic>FGFR4</italic> [rs351855 (G388R)] (<xref ref-type="bibr" rid="B21">Chou et al., 2017</xref>), have been reported to be associated with OSCC. In the current study, for the first time, we report variants such as <italic>DHFR/MSH3</italic> [rs1650697 (I79V)], <italic>ATM</italic> [rs1801516 (D1853N)], <italic>FLT3</italic> [rs1933437 (T227M)], <italic>ERBB2</italic> [rs1136201 (I655V)], <italic>ERCC2</italic> [rs1799793 (D312N)], <italic>RET</italic> [rs1799939 (G691S)], <italic>KDR</italic> [rs1870377 (Q472H) and rs2305948 (V297I)], <italic>PDGFRA</italic> [rs35597368 (S478P)], <italic>ERBB3</italic> [rs773123 (S1119C)], and <italic>FLT1</italic>[rs7993418 (T1213Stop)] from the southwest coast of India. Among them, eight SNPs were selected for further downstream analysis (<xref ref-type="table" rid="T3">Table 3C</xref>), and the remaining could not be utilized because of the unavailability of their structural details.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>A. Genes identified based on similarities between that curated from <xref ref-type="bibr" rid="B75">Paul et al. (2025)</xref> and from the ClinPGx database. B. Genes filtered in after excluding genes with synonymous substitutions and non-coding variants. C. Genes selected for structural and mutational analysis based on the availability of corresponding protein structures.</p>
</caption>
<table>
<tbody valign="top">
<tr>
<td align="left">
<graphic xlink:href="fgene-17-1666142-fx1.tif">
<alt-text content-type="machine-generated">Diagram with three labeled columns, A, B, and C. Column A lists 14 gene names in bold. Column B lists the same genes with specific variants in parentheses, including gene names and single nucleotide polymorphisms (SNPs), in regular and italicized text. Column C highlights a subset of these genes and variants in bold and italics, suggesting a final selection. Arrows connect A to B and B to C, indicating a selection or filtering process.</alt-text>
</graphic>
</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>
<xref ref-type="table" rid="T4">Table 4</xref> summarizes the prevalence of the eight selected SNPs among the patients, their global and South Asian allele frequencies, and the reported clinical relevance in cancer. Several variants such as <italic>FLT3</italic> T227M<italic>, XRCC1</italic> Q399R<italic>, MSH3</italic> I79V, and <italic>XPC</italic> Q939K were highly prevalent (&#x3e;80%) in our study population and occur at notably higher frequencies in South Asian populations compared to global averages, underscoring potential ancestry-specific pharmacogenomic implications. Most of these SNPs are located in genes involved in DNA repair and cell-cycle regulation, with prior evidence linking them to carcinogenesis, treatment response, or radiation and chemotherapy toxicity. These mutations reinforce the importance of population-specific genetic profiling to better understand therapeutic outcomes and optimize personalized treatment strategies for OSCC patients.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Comparison of minor allele frequencies (MAF) of the eight SNPs utilized for structure&#x2013;function association analysis among global datasets (<ext-link ext-link-type="uri" xlink:href="https://gnomad.broadinstitute.org/">https://gnomad.broadinstitute.org</ext-link>).</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Gene</th>
<th align="left">Variant (rsID; AA change)</th>
<th align="left">Percentage of recruited patients with this mutation</th>
<th align="left">gnomAD global MAF (approx.)</th>
<th align="left">South Asian MAF (approx.; gnomAD/1000G/IndiGen)</th>
<th align="left">Cancer/Clinical associations (concise)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">
<italic>FLT3</italic>
</td>
<td align="left">rs1933437 (T227M)</td>
<td align="left">&#x3e;80%</td>
<td align="left">&#x223c;0.52 (alt allele) &#x2013; common missense polymorphism in <italic>FLT3</italic>
</td>
<td align="left">&#x223c;0.65&#x2013;0.70 (IndiGen &#x223c;0.675; higher than global)</td>
<td align="left">Germline missense in FLT3 (oncogenic kinase). Reported association with sunitinib toxicity and other TKI-related adverse effects; considered a pharmacogenomic variant in Indian cohorts. Additionally, it appears in GWAS signals linked to blood pressure and Tourette&#x2019;s, but is not a classic high-penetrance cancer driver.</td>
</tr>
<tr>
<td align="left">
<italic>XRCC1</italic>
</td>
<td align="left">rs1799782 (R194W)</td>
<td align="left">&#x223c;5%</td>
<td align="left">&#x223c;0.05&#x2013;0.10 globally (low-frequency; varies widely by ancestry, but consistently &#x3e;0.05 in many datasets)</td>
<td align="left">Variable; &#x223c;0.10&#x2013;0.25 overall, with reports from &#x223c;0.09 in some North Indian groups to &#x3e;0.25&#x2013;0.30</td>
<td align="left">Base-excision repair variant. Repeatedly studied in lung, colorectal, breast, and head and neck cancers with mixed but suggestive associations with risk. Several studies (including OSCC) indicate impact on response/toxicity to platinum chemo-radiotherapy via altered BER capacity.</td>
</tr>
<tr>
<td align="left">
<italic>XRCC1</italic>
</td>
<td align="left">rs25487 (Q399R)</td>
<td align="left">&#x3e;80%</td>
<td align="left">Alt allele AF &#x223c;0.65&#x2013;0.70 in gnomAD/ExAC (very common)</td>
<td align="left">South Asian MAF typically &#x223c;0.35&#x2013;0.45 (e.g., A-allele &#x223c;0.42 in the South Indian cohort)</td>
<td align="left">One of the best-studied DNA-repair SNPs. Associated (sometimes inconsistently) with risk of lung, breast, colorectal, ovarian, OSCC, and others and with platinum and cyclophosphamide efficacy/toxicity (e.g., better or worse response depending on the genotype). Frequently appears in pharmacogenomic panels for platinum therapy.</td>
</tr>
<tr>
<td align="left">
<italic>DHFR/MSH3</italic>
</td>
<td align="left">rs1650697 (I79V)</td>
<td align="left">&#x3e;80%</td>
<td align="left">Global MAF &#x2248;0.23 (1000G; &#x201c;T&#x201d;/variant allele) &#x2013; common coding variant</td>
<td align="left">SAS MAF &#x2248;0.32 (1000G South Asian; variant allele)</td>
<td align="left">Located in the DHFR/MSH3 locus (5&#x2032; UTR in DHFR, missense I79V in MSH3). Acts as an eQTL for MSH3 and DHFR and modifies somatic repeat instability and age at onset in Huntington&#x2019;s disease and myotonic dystrophy. Additionally, it is studied as a predictor of platinum chemotherapy response via MSH3-mediated mismatch repair.</td>
</tr>
<tr>
<td align="left">
<italic>XPC</italic>
</td>
<td align="left">rs2228000 (A499V)</td>
<td align="left">&#x223c;30%</td>
<td align="left">Global MAF &#x223c;0.20&#x2013;0.25 (common; gnomAD reports frequency &#x3e;0.01, Japanese &#x2248;0.23)</td>
<td align="left">Likely in similar range (&#x223c;0.20&#x2013;0.30) in SAS but with inter-study variation (exact gnomAD SAS value depends on release)</td>
<td align="left">NER gene variant. Reported associations with breast cancer, lung cancer, and other solid tumors, especially in Asian cohorts, often in combination with rs2228001. Generally considered a low-penetrance cancer risk modifier affecting nucleotide-excision repair efficiency.</td>
</tr>
<tr>
<td align="left">
<italic>XPC</italic>
</td>
<td align="left">rs2228001 (Q939K)</td>
<td align="left">&#x3e;80%</td>
<td align="left">Global MAF &#x223c;0.30&#x2013;0.40; Japanese frequency &#x223c;0.60 for the K allele, indicating it is common worldwide</td>
<td align="left">SAS MAF probably &#x223c;0.30&#x2013;0.40 (common; precise value varies by cohort)</td>
<td align="left">Another XPC missense variant; associated with risk of prostate cancer, bladder cancer, and other tumors, particularly in Asian populations, and sometimes with altered DNA repair capacity or therapeutic outcomes. Often analyzed in haplotype with rs2228000 in NER-cancer association studies.</td>
</tr>
<tr>
<td align="left">
<italic>MUTYH</italic>
</td>
<td align="left">rs3219489 (Q310H)</td>
<td align="left">&#x223c;40%</td>
<td align="left">gnomAD/ExAC global AF &#x2248;0.25&#x2013;0.27 (common missense polymorphism)</td>
<td align="left">SAS MAF around 0.30&#x2013;0.35 in several cohorts (e.g., supplementary tables report &#x223c;0.30&#x2013;0.38)</td>
<td align="left">Common BER gene variant. ClinVar usually classifies it as benign for classic MUTYH-associated polyposis, but multiple studies link it to modified risk for colorectal, rectal, lung, and other cancers and to outcomes in OSCC chemo-radiotherapy. Best seen as a low-effect risk/response modifier and not a high-penetrance pathogenic mutation.</td>
</tr>
<tr>
<td align="left">
<italic>ATM</italic>
</td>
<td align="left">rs1801516 (D1853N)</td>
<td align="left">&#x223c;20%</td>
<td align="left">gnomAD freq &#x2248;0.11 (alt allele)</td>
<td align="left">South Asian MAF in 1000G &#x2248; 0.14; some South Asian cohorts (e.g., Sri Lankan) report higher variant allele freq (&#x223c;0.42), showing strong population heterogeneity</td>
<td align="left">Key radiogenomic SNP in ATM. Numerous studies/meta-analyses link the A (Asn) allele with increased risk of late radiation toxicity (fibrosis, telangiectasia, pneumonitis, and rectal toxicity) after radiotherapy for breast, lung, prostate, and other cancers. The overall association with cancer risk <italic>per se</italic> is weak/inconsistent, but some data suggest reduced breast cancer risk for the AA genotype. Additionally, it appears in pharmacogenomic frameworks as a marker of chemo- and radio-toxicity (e.g., with cyclophosphamide/doxorubicin/5-FU).</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Complete docking results, including the full AutoDock 4.2 log file (random seeds, scoring functions, binding energies, RMSD clustering, and ranked poses) for FLT3 and sunitinib, are provided in <xref ref-type="sec" rid="s12">Supplementary Table</xref>.</p>
</sec>
<sec id="s3-3">
<title>Structure&#x2013;function correlation of cancer onset</title>
<p>Fms-like tyrosine kinase receptor 3 [FLT3] (PDB ID: 3QS7) carrying T227M (rs1933437) alteration is identified among 80% of the OSCC patients. FLT3 regulates the proliferation, differentiation, and survival of hematopoietic progenitor cells (<xref ref-type="bibr" rid="B34">Griffith et al., 2004</xref>; <xref ref-type="bibr" rid="B43">Kazi and R&#xf6;nnstrand, 2019</xref>). FLT3 is 993 amino acids long and consists of one extracellular domain [27&#x2013;541] with five Ig-like subdomains [D1&#x2013;D5], one transmembrane domain [542&#x2013;564], one juxta membrane domain [565&#x2013;609], and two tyrosine kinase domains[TKD] [610&#x2013;944], followed by a C-terminal tail [945&#x2013;993] (<xref ref-type="bibr" rid="B43">Kazi and R&#xf6;nnstrand, 2019</xref>; <xref ref-type="bibr" rid="B97">Verstraete et al., 2011</xref>). Interaction with the FLT3 ligand causes dimerization of the protein, which triggers autophosphorylation at the cytosolic domains followed by downstream signaling (<xref ref-type="bibr" rid="B34">Griffith et al., 2004</xref>). Most of the carcinogenic mutations in FLT3 cause activation of TKD, resulting in aberrant proliferation 63 (<xref ref-type="bibr" rid="B24">Daver et al., 2019</xref>; <xref ref-type="bibr" rid="B34">Griffith et al., 2004</xref>; <xref ref-type="bibr" rid="B77">Rataj et al., 2025</xref>; <xref ref-type="bibr" rid="B91">Tecik and Adan, 2022</xref>). The T227M alteration in FLT3 is being isolated from OSCC patients for the first time (<xref ref-type="fig" rid="F2">Figure 2</xref>). The D2 subdomain of FLT3 accommodates the 227 substitution, which is in close proximity to the interaction plane between D3 and FL. This may affect its interaction between D2 and D3 and alter the affinity for FL.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutation of the human FLT3. <bold>(B)</bold> Structural representation of the mutation.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g002.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutation of the human FLT3. (B) Structural representation of the mutation</alt-text>
</graphic>
</fig>
<p>The Xeroderma pigmentosum complementation group C (XPC) (PDB ID: 8EBU) with either A499V (rs2228000) or Q939K (rs2228001) is distinguished for 30%&#x2013;80% patient pool. XPC plays a crucial role in nucleotide excision repair [NER],base excision repair [BER], and in transcriptional regulation (<xref ref-type="bibr" rid="B17">Bunick et al., 2006</xref>). It contains 940 residues with the transglutaminase homology domain (TGD) and 3 &#x3b2;-hairpin domains 1&#x2013;3 (BH) (<xref ref-type="bibr" rid="B108">Zhang et al., 2015</xref>). While XPC uses its TGD domain for communication with RAD23B, it retains its contact with CETN2 through BH domains (<xref ref-type="bibr" rid="B46">Kim et al., 2023</xref>; <xref ref-type="bibr" rid="B108">Zhang et al., 2015</xref>). The transcription regulation is catalyzed by XPC in complex with the seven-subunit TFIIH core complex (Core-7; seven subunits, namely, XPB, XPD, p62, p52, p44, p34, and p8). Two long helices, long helix N-terminal [LHN] spanning amino acid (aa) 167&#x2013;224 and long helix C-terminal [LHC] spanning aa 815&#x2013;866, which are 45&#x212b; apart, extend from XPC toward the TFIIH core. LHN contacts with the p62 domain of Core-7 when LHC ensures the stability of the complex with XPB. The subsequent three short helices L, M, and N encompassing the last 50 aa remain involved with XPB, p52, p8, and p34 (<xref ref-type="bibr" rid="B30">Gaur et al., 2018</xref>; <xref ref-type="bibr" rid="B46">Kim et al., 2023</xref>; <xref ref-type="bibr" rid="B108">Zhang et al., 2015</xref>). A499V mutation affects the XPC-Rad23 interaction, reducing DNA repair efficiency, and it has been reported with a higher risk of developing acute myeloid leukemia, bladder, and breast cancer (<xref ref-type="bibr" rid="B37">He et al., 2013</xref>; <xref ref-type="bibr" rid="B50">Lakkireddy et al., 2021</xref>) (<xref ref-type="fig" rid="F3">Figure 3</xref>). Q939 caps the C-terminal helix of XPC and is important for communication with Core-7. Polymorphic variation at the 939 position has been reported to be associated with bladder, lung, and colorectal cancer (<xref ref-type="bibr" rid="B37">He et al., 2013</xref>; <xref ref-type="bibr" rid="B50">Lakkireddy et al., 2021</xref>; <xref ref-type="bibr" rid="B84">Sankhwar et al., 2016</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutations of the human XPC. <bold>(B)</bold> Structural representation of the mutation in the XPC complex.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g003.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutations of the human XPC. (B) Structural representation of the mutation in the XPC complex.</alt-text>
</graphic>
</fig>
<p>X-ray cross-complementing group 1 (XRCC1) (PDB ID: 2D8M) accommodating R194W (rs1799782) or Q399R (rs25487) correlates with 5%&#x2013;80% OSCC patients. XRCC1 functions to repair the single-stranded DNA nicks in complexes with DNA ligase III, DNA polymerase b (b-Pol), and poly(ADP-ribose) polymerase (PARP) (<xref ref-type="bibr" rid="B55">London, 2015</xref>; <xref ref-type="bibr" rid="B59">Marintchev et al., 1999</xref>). XRCC1 is 633-aa-long and consists of three major domains, namely, N-terminal domain [NTD (1&#x2013;183)], central BRCT domain [BRCT I (310&#x2013;406)], and C-terminal BRCT domain [BRCT II (534&#x2013;633)]. NTD retains a beta sandwich structure, facilitating interactions with DNA (<xref ref-type="bibr" rid="B59">Marintchev et al., 1999</xref>; <xref ref-type="bibr" rid="B64">Mok et al., 2019</xref>). BRCT I facilitates the recruitment of XRCC1 to the DNA damage site through the interaction with PARP1 while BRCT II remains in communication with DNA ligase III to promote the final DNA end ligation step (<xref ref-type="bibr" rid="B19">Caldecott, 2019</xref>; <xref ref-type="bibr" rid="B55">London, 2015</xref>; <xref ref-type="bibr" rid="B59">Marintchev et al., 1999</xref>; <xref ref-type="bibr" rid="B107">Zhang et al., 1998</xref>). These functional domains are interconnected via two linkers, XL1 [155 to 309] and XL2 [406 to 528]. R194T occurs due to a C to T substitution in exon 6. This region of the protein is crucial for maintaining genomic integrity and the BER pathway through coordination with PCNA-like proteins (<xref ref-type="bibr" rid="B40">Hu et al., 2005</xref>). Hence, this substitution can lead to reduced efficacy of DNA repair, increased susceptibility to oxidative stress, and finally, increased risk of cancer (<xref ref-type="bibr" rid="B39">Hsueh et al., 2017</xref>) (<xref ref-type="fig" rid="F4">Figures 4</xref>, <xref ref-type="fig" rid="F5">5</xref>). Notably, according to most of the publications until now, the R to T substitution at 194 is more protective than the wild-type Arg/Arg genotype. This mutation occurs in the BRCT 1/a domain and is reported with reduced DNA repair efficacy (<xref ref-type="bibr" rid="B60">Matsuo et al., 2004</xref>). The structural analysis of the BRCT domain exhibits high density of the positively charged population in the region close to 399, and Q-to-R substitution will increase the degree further (<xref ref-type="fig" rid="F6">Figure 6</xref>). As expected, homozygous Arg/Arg or heterozygous Arg/Gln has been found to have a significant correlation with cancer onset (<xref ref-type="bibr" rid="B81">Saad et al., 2021</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutations of the human XRCC1. <bold>(B)</bold> Structural representation of the mutation.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g004.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutations of the human XRCC1. (B) Structural representation of the mutation.</alt-text>
</graphic>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Electrostatic surface representation highlighting the positively charged region.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g005.tif">
<alt-text content-type="machine-generated">Electrostatic surface representation highlighting the positively charged region.</alt-text>
</graphic>
</fig>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutation of the human XRCC1. <bold>(B)</bold> Structural representation of the solved structure.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g006.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutation of the human XRCC1. (B) Structural representation of the solved structure.</alt-text>
</graphic>
</fig>
<p>The MutS homologs (MSH) (PDB ID: 8OLX) are members of the DNA mismatch repair system, and the Msh2&#x2013;Msh6 and Msh2&#x2013;Msh3 forms are reported in humans (<xref ref-type="bibr" rid="B72">Pal et al., 2020</xref>). Human MSH3 is a 1,137-aa-long multi-domain protein. The N-terminal domain remained structurally unsolved yet, while the mismatch binding domain [MMBD] played the leading role in the identification of insertion&#x2013;deletion loops [IDLs]. The clamp domain is responsible for DNA-binding, and two connector domains serve to link the DNA binding and ATPase domains. The C-terminal dimerization domain (DMD) ensures the stability of the heterodimer complex. Specific structural bending is triggered on IDLs by MSH3 (<xref ref-type="bibr" rid="B36">Gupta et al., 2011</xref>). The I 79 is located in the NTD and is important for crosstalk with EXO1, the master of DNA mismatch repair, which corresponds to 80% of patients with OSCC. In consequence, the I79V substitution (rs1650697) is found to be associated with increased risk of several types of cancers (<xref ref-type="bibr" rid="B68">Nam et al., 2021</xref>).</p>
<p>The ataxia&#x2013;telangiectasia mutated (ATM) (PDB ID: 7SIC) is a protein kinase that functions as a master regulator of the DNA damage response (<xref ref-type="bibr" rid="B29">Garc&#xed;a et al., 2022</xref>; <xref ref-type="bibr" rid="B76">Phan and Rezaeian, 2021</xref>; <xref ref-type="bibr" rid="B95">Ueno et al., 2022</xref>). Inactivated ATM remains in the butterfly-shaped dimeric state, and on interaction with the Mre11&#x2013;Rad50&#x2013;Nbs1 (MRN) complex due to DNA breaks or oxidation, it retains the monomeric status (<xref ref-type="bibr" rid="B11">Bareti&#x107; et al., 2017</xref>; <xref ref-type="bibr" rid="B100">Warren and Pavletich, 2022</xref>). ATM is a multi-domain protein of molecular weight 350&#xa0;KDa with 3,056 aa, which can be divided into the N-solenoid and FATKIN regions. N-solenoid comprises the spiral (residues 1&#x2013;1,166) and pincer (1,167&#x2013;1,898) domains with multiple HEAT repeats. The residues from 90 to 97 in the spiral domain play an important role in the interaction with p53, BRCA1, and LKB1. Nbs1 of the MRN complex binds at the interface between the spiral and pincer domains (<xref ref-type="bibr" rid="B11">Bareti&#x107; et al., 2017</xref>; <xref ref-type="bibr" rid="B100">Warren and Pavletich, 2022</xref>). The FATKIN consists of FAT (1,899&#x2013;2,613), kinase domain (2,614&#x2013;3,026), and FATC (3,027&#x2013;3,056) domains. Three tetratricopeptide repeat domains TRD1 (1,899&#x2013;2,025), TRD2 (2,026&#x2013;2,192), TRD3 (2,193&#x2013;2,479), and HRD (2,480&#x2013;2,613) subdomains encompass the FAT region, while the N-lobe (2,614&#x2013;2,770), C-lobe (2,771&#x2013;2,957), and PRD (2,958&#x2013;3,026) subdomains add up to the kinase domain (<xref ref-type="fig" rid="F7">Figure 7</xref>). The FAT domain is responsible for ATM dimerization, while autophosphorylation on S1981 at TRD1 is reported to be pivotal for the monomeric activation status of the ATM (<xref ref-type="bibr" rid="B10">Bakkenist and Kastan, 2003</xref>). Once activated, ATM associates with the phosphorylation of downstream targets that involves approximately 905 phosphorylation sites over 700 proteins to initiate a signaling cascade through the HR pathway (<xref ref-type="bibr" rid="B29">Garc&#xed;a et al., 2022</xref>; <xref ref-type="bibr" rid="B61">Matsuoka et al., 2007</xref>). D1853N substitution has been identified for OSCC for the first time in India. D1853N (rs1801516) is present at the pincer domain, which is the binding site of the MRN complex. As expected, this alteration has been reported to be associated with cancer (<xref ref-type="bibr" rid="B52">Li et al., 2009</xref>).</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutation of the human ATM. <bold>(B)</bold> Structural representation of the mutation.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g007.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutation of the human ATM. (B) Structural representation of the mutation.</alt-text>
</graphic>
</fig>
<p>Human Mut Y homolo [MUTYH] (PDB ID: 8FAY) is involved in the excision of adenine inserted to complement 8-oxoguanine (<xref ref-type="bibr" rid="B71">Nu&#xf1;ez et al., 2018</xref>). MUTYH is a 546-aa-long protein. The N-terminal domain [NTD] encompasses a six-helix barrel with [4Fe&#x2013;4S] cluster for metal binding, while the C-terminal domain undergoes crosstalk with AP:8-oxoG in DNA (<xref ref-type="bibr" rid="B67">Nakamura et al., 2021</xref>). The inter-domain connector [IDC] of MUTYH traverses through the DNA major grooves and coordinates the DNA repair/apoptosis process through an interaction with several effector proteins (<xref ref-type="bibr" rid="B56">Luncsford et al., 2010</xref>; <xref ref-type="bibr" rid="B67">Nakamura et al., 2021</xref>). The IDC region communicates with the Rad9&#x2013;Rad1&#x2013;Hus1 complex (9&#x2013;1&#x2013;1) and SIRT6 in response to oxidative DNA damage (<xref ref-type="bibr" rid="B54">Lim et al., 2015</xref>; <xref ref-type="bibr" rid="B56">Luncsford et al., 2010</xref>; <xref ref-type="bibr" rid="B89">Shi et al., 2006</xref>). The 4Fe&#x2013;4S cluster with four conserved Cys moieties is essential for mismatch recognition and has a redox cofactor role (<xref ref-type="bibr" rid="B12">Bartels et al., 2017</xref>; <xref ref-type="bibr" rid="B71">Nu&#xf1;ez et al., 2018</xref>). The second putative metal [Zn&#x2b;]-binding motif is located in the extended IDC region of mammalian MUTYH, which is constituted with three conserved Cys residues [ Cys 318, Cys 325, and Cys 328], and demonstrates control over adenine glycosylase activity (<xref ref-type="bibr" rid="B12">Bartels et al., 2017</xref>; <xref ref-type="bibr" rid="B27">Engstrom et al., 2014</xref>). Despite contradiction regarding the amino acid satisfying the fourth coordinate of Zn&#x2b;, H72 came up with recent structure analyzed in comparison with mouse MUTYH (<xref ref-type="bibr" rid="B67">Nakamura et al., 2021</xref>) (<xref ref-type="fig" rid="F8">Figure 8</xref>). Q310H substitution (rs3219489) has been identified among 40% OSCC patients under study. His310 is found in close vicinity of the Zn-binding motif in IDC and creates an opportunity for quenching the fourth coordinate of the metal ion (<xref ref-type="fig" rid="F9">Figure 9</xref>). This possibility creates a significant effect on the overall conformation and fold of the MUTYH by distancing the N-terminal of the 6-helix barrel. No report about the correlation of Q310H with human carcinoma has been published yet.</p>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>
<bold>(A)</bold> Schematic representation of the domains and mutation of the human MUTYH. <bold>(B)</bold> Structural representation of the mutation.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g008.tif">
<alt-text content-type="machine-generated">(A) Schematic representation of the domains and mutation of the human MUTYH. (B) Structural representation of the mutation.</alt-text>
</graphic>
</fig>
<fig id="F9" position="float">
<label>FIGURE 9</label>
<caption>
<p>
<bold>(A)</bold> Structural representation of mouse MUTYH highlighting residues interacting with Zn ion. <bold>(B)</bold> Structural representation of human MUTYH highlighting residues interacting with Zn ion, along with the nearby mutation.</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g009.tif">
<alt-text content-type="machine-generated">(A) Structural representation of mouse MUTYH highlighting residues interacting with Zn ion. (B) Structural representation of human MUTYH highlighting residues interacting with Zn ion, along with the nearby mutation.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3-4">
<title>Association between SNPs and ineffectiveness of anticancer drugs/treatment</title>
<p>The atlas of cancer treatment revolves around the surgical procedure followed by adjuvant radiation therapy (aRT) and/or concurrent chemoradiation (CRT). The primary goal of radio-chemotherapy is to induce irreversible DNA damage in tumor/cancer cells that leads to cell death. This strategy relies on the inability of tumor cells to properly repair the DNA damage, which would otherwise allow them to survive and proliferate. While DNA repair mechanisms help prevent the onset of cancer in the normal condition, in established tumors, enhanced DNA repair capacity can contribute to tumor survival, allowing cancer cells to endure the damaging effects of radio-chemotherapy (<xref ref-type="bibr" rid="B38">Helleday et al., 2008</xref>; <xref ref-type="bibr" rid="B99">Wang et al., 2016</xref>). Radiation also triggers the formation of reactive oxygen species (ROS) and nitric oxide species (RNOS) that, in turn, induce inflammation and stress response, followed by the release of cytokines and chemokines (<xref ref-type="bibr" rid="B14">Bentzen, 2006</xref>; <xref ref-type="bibr" rid="B110">Zhao et al., 2007</xref>).Any patient exposed to radiotherapy certainly experiences the related toxicity, which can be minor to severe and extend for weeks to lifetime (<xref ref-type="bibr" rid="B9">Azria et al., 2008</xref>; <xref ref-type="bibr" rid="B79">Riballo et al., 1999</xref>; <xref ref-type="bibr" rid="B103">West and Barnett, 2011</xref>). Genetic variability plays a crucial role in how individuals from different ethnic background respond to various treatments. These variations can influence drug metabolism, efficacy, and potential toxicity, leading to different treatment outcomes (<xref ref-type="bibr" rid="B44">Kerns et al., 2015</xref>). Therefore, the future objective for cancer treatment should be oriented toward tailored chemo-radiation plans based on genetic determinants to minimize cancer/tumor progress.</p>
<p>Accordingly, we analyzed the genetic inheritance of OSCC patients under study in correlation with possible toxicity due to chemo-radiotherapy. Our study highlights the FLT3 substitution at 227;interestingly, the same alteration was found to trigger toxicity against sunitinib treatment (<xref ref-type="bibr" rid="B45">Kim et al., 2013</xref>). Sunitinib binds with the active conformation of the FLT3 kinase (DFGin) through a specific interaction with C694 at the hinge region and D829 at the DFG motif (<xref ref-type="bibr" rid="B3">Acharya et al., 2022</xref>). T227M alteration may influence the monomeric&#x2013;dimeric interconversion of FLT3 and affect the binding of sunitinib with the activated kinase domain with no effect on cancer cells, while, as a multitarget kinase inhibitor, it continues to work on receptor tyrosine kinases and hampers the normal physiological functions, resulting in toxicity. <xref ref-type="fig" rid="F10">Figure 10</xref> depicts the interaction details between FLT3 and sunitinib.</p>
<fig id="F10" position="float">
<label>FIGURE 10</label>
<caption>
<p>Docked conformation of sunitinib bound to the FLT3 tyrosine kinase domain (DFGin-active conformation of the kinase, PDB ID: 6JQR).</p>
</caption>
<graphic xlink:href="fgene-17-1666142-g010.tif">
<alt-text content-type="machine-generated">Docked conformation of sunitinib bound to the FLT3 tyrosine kinase domain (DFGin-active conformation of the kinase, PDB ID: 6JQR).</alt-text>
</graphic>
</fig>
<p>The XPC SNPs [rs2228000 and rs2228001] and their role are well-realized in case of cancers, but their association with post radio-chemotherapy adversity is yet to be fully understood. The radio-chemotherapy sensitivity of XPC [rs2228000 and rs2228001] for lung squamous cell carcinoma and head and neck cancer has been recently reported (<xref ref-type="bibr" rid="B57">Ma&#x107;kowiak et al., 2024</xref>; <xref ref-type="bibr" rid="B99">Wang et al., 2016</xref>). Especially, in the case of rs2228001, polymorphism at the 939 position causes higher morbidity and adverse effects during and post-treatment (<xref ref-type="bibr" rid="B57">Ma&#x107;kowiak et al., 2024</xref>). Being an important member of excision repair (BER), genetic alteration in XRCC1 [rs25487 and rs1799782] has a substantial effect on carcinoma progress and treatment itself. The effect of these SNPs on chemo-radio sensitivity is controversial and needs further studies. A recent meta-analysis correlates high XRCC1 expression with increased risk of minor treatment response and lesser chance of overall survival. It is further confirmed that rs25487 is unlikely to be toxic post-therapy for esophageal cancer, while it can trigger acute RT-induced adverse effects in head and neck cancer (<xref ref-type="bibr" rid="B32">Gong et al., 2021</xref>; <xref ref-type="bibr" rid="B35">Gu et al., 2015</xref>). Another meta-analysis concludes the Arg194Trp polymorphism is sensitive to platin-based drugs, while Arg399Gln has reduced sensitivity toward it in non-small-cell lung cancer in the Chinese population (<xref ref-type="bibr" rid="B35">Gu et al., 2015</xref>). <italic>ERCC5</italic> plays a pivotal role in NER, and hence, SNPs associated [rs1047768 and rs17655] with this protein are well-studied for their effect in inducing cancer. The connection of these SNPs, rs1047768 [His 46 Gln] and rs17655 [Asp1558 His, Asp1104 His], with chemo-radiotherapy-induced toxicity is still disputed. While meta-analysis on the Egyptian population demonstrated that these SNPs have no effect on the clinical outcome of platinum-based chemotherapy in NSCLC, another similar study on the Chinese population concluded that the C allele carriers of the <italic>ERCC5</italic> rs1047768 polymorphism are more sensitive to platinum-based chemotherapy (<xref ref-type="bibr" rid="B2">Abdalkhalek et al., 2022</xref>; <xref ref-type="bibr" rid="B106">Xu et al., 2018</xref>). The association of rs1801516 in ATM with cancer onset is well-realized, but its correlation with adverse outcomes post-radiotherapy is still inconclusive. While some publications refute any connectivity of rs1801516 with radiotoxicity, the meta-analysis on breast and prostate cancer postulates the correspondence between this gene alteration with an increased risk of radiation-induced normal tissue toxicity (<xref ref-type="bibr" rid="B7">Andreassen et al., 2016</xref>; <xref ref-type="bibr" rid="B13">Bensenane et al., 2024</xref>). Doxorubicin has been used extensively to treat various types of cancer as an antineoplastic agent. Apart from assisting in DNA damage through topoisomerase II, doxorubicin can stimulate ATM activation by S1981 phosphorylation and, hence, assist in the process of DNA double-strand repair directly and arrest the cell-cycle phase (<xref ref-type="bibr" rid="B41">Hublarova et al., 2010</xref>; <xref ref-type="bibr" rid="B49">Kurz et al., 2004</xref>). In addition to the activation of the ATM/P53 pathway, doxorubicin triggers the MAPK/NF-&#x3ba;&#x3b2; survival pathway by interacting with PI3K family members, which may lead to drug resistance (<xref ref-type="bibr" rid="B74">Panta et al., 2004</xref>). Hence, the inhibition of ATM in combination with radiotherapy or chemotherapy would be considered a better treatment option, where DNA damage is the preferred pathway to trigger innate immunity (<xref ref-type="bibr" rid="B26">Dong et al., 2022</xref>; <xref ref-type="bibr" rid="B29">Garc&#xed;a et al., 2022</xref>).</p>
</sec>
</sec>
<sec sec-type="conclusion" id="s4">
<title>Conclusion</title>
<p>Overall, our study highlights the critical role of pharmacogenomics in advancing personalized cancer care, particularly in OSCC, which is a malignancy marked by poor prognosis and high therapeutic resistance. By integrating mutational profiling with structural and functional analysis, we identified key SNPs that may modulate drug response, DNA repair efficiency, and treatment outcomes. Notable variants in genes such as <italic>FLT3, XRCC1, XPC, MUTYH</italic>, and <italic>ATM</italic> were found to induce conformational alterations that could influence carcinogenesis, chemoresistance, and radiotoxicity. The novel discovery of <italic>FLT3</italic> T227M and <italic>MUTYH</italic> Q310H in OSCC patients from Southwest India further underscores the value of region-specific genomic data in shaping precision oncology strategies and improving therapeutic decision-making.</p>
<p>Despite the strengths of this integrative approach, the interpretations are dependent on <italic>in silico</italic> analysis, which requires further experimental validation. The proposed mechanistic hypotheses such as altered kinase activation and drug-binding efficiency in <italic>FLT3</italic> T227M or disrupted zinc coordination affecting MUTYH repair necessitate verification through cellular models, biochemical assays, and high-throughput structural assessment. Hence, future studies must include expanded cohorts to verify the clinical relevance, functional assay, and further refined genotype-based treatment recommendations. Incorporating genetic screening into routine cancer care, alongside continued structural validations with the development of pharmacogenomic resources, will accelerate the translation of these discoveries into evidence-based personalized therapy for OSCC patients.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s5">
<title>Data availability statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found here: <ext-link ext-link-type="uri" xlink:href="https://bcga.iitm.ac.in/cbioportal/study/summary?id=HNSCC_Yenepoya_2025">https://bcga.iitm.ac.in/cbioportal/study/summary?id&#x3d;HNSCC_Yenepoya_2025</ext-link>.</p>
</sec>
<sec sec-type="ethics-statement" id="s6">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Yenepoya Ethics Committee-1, Yenepoya (Deemed to be University), Mangalore, Karnataka. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="s7">
<title>Author contributions</title>
<p>PS: Data curation, Formal Analysis, Methodology, Software, Visualization, Writing &#x2013; original draft. KK: Data curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing &#x2013; review and editing. AA: Data curation, Formal Analysis, Methodology, Software, Visualization, Writing &#x2013; original draft. SG: Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing &#x2013; original draft. RD: Conceptualization, Formal Analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing &#x2013; review and editing.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<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="s10">
<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="s11">
<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="s12">
<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.2026.1666142/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2026.1666142/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.xlsx" id="SM1" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/30771/overview">Jos&#xe9; A. G. Ag&#xfa;ndez</ext-link>, University of Extremadura, Spain</p>
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<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1554692/overview">Md. Siddiqul Islam</ext-link>, American International University-Bangladesh, Bangladesh</p>
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<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3228227/overview">Damodara Reddy Vaddi</ext-link>, REVA University, India</p>
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