RNA-sequencing (RNA-seq) data for 33 tumor types were downloaded from the Cancer Genome Atlas (TCGA) database (12), which provides a comprehensive functional genomics data set encompassing various tumors, facilitating robust pan-cancer analyses. The STAR alignment pipeline was used to process the data, and transcripts per million (TPM) expression values were extracted. Data analysis and visualization were conducted using R software (version 4.2.1). Statistical tests based on data characteristics were performed using the stats (version 4.2.1) and car (version 3.1-0) packages. Analyses not meeting statistical requirements were excluded (13). The Wilcoxon rank sum test was used for group comparisons application. The expression profile of LRFN4 in cancer cell lines was analyzed using the Cancer Cell Line Encyclopedia (CCLE) database.

For tumors lacking normal tissue data (e.g., TCGA-GBM, TCGA-LAML), GEPIA2 was employed to compare TCGA tumor samples with GTEx normal tissues, using a P-value cutoff of 0.01 and a log₂FC threshold of 1 (14, 15). The correlation between LRFN4 expression-pathological staging was assessed using GEPIA2’s “Pathological Staging Chart,” generating normalized (log₂ [TPM + 1]) violin plots for stages I-IV. GEPIA2 was utilized for TCGA-GTEx data integration, with figures based on log-transformed values [log₂(TPM + 1)].

ROC analysis with the pROC package was used to evaluate LRFN4’s ability to distinguish tumors from normal tissues. Data from UCSC XENA, where TCGA and GTEx datasets were processed into TPM format via the Toil pipeline (16, 17). Prognostic Analysis, RNA-seq and clinical data from TCGA exploring overall survival (OS) and disease-free survival (DFS). GEPIA2 provided OS and DFS maps, stratifying cohorts into high- and low-expression groups (14). To further validate our findings, supplementary survival analysis was conducted using the Kaplan-Meier plotting tool, with data sourced from the GEO database. This analysis aimed to assess the prognostic significance of LRFN4 expression across multiple cancer types.

The TISIDB portal (http://cis.hku.hk/TISIDB/, accessed on September 3, 2024) was utilized to investigate the relationship between LRFN4 expression and the immune or molecular subtypes of various cancers.

The TCGA Pan-Cancer dataset (PANCAN, N = 10,535, G = 60,499) was used to investigate the relationship between LRFN4 expression and key therapeutic biomarkers, including tumor mutational burden (TMB), microsatellite instability (MSI), and the ESTIMATE scores. Expression profiles for the gene ENSG00000173621 (LRFN4) were extracted, with samples were filtered to include only “Primary Blood Derived Cancer - Peripheral Blood” and “Primary Tumor” types. Cancer types with fewer than three samples were excluded, resulting in data for 37 cancer types. TMB was calculated using MuTect2-processed simple nucleotide variation data, and the “maftools” R package (18–20). MSI scores were retrieved from TCGA’s public data. Tumor microenvironment characteristics (ESTIMATE scores) were obtained through the ESTIMATE algorithm (21, 22). All expression data were normalized using a log₂(x + 0.001) transformation before integration.

The cBioPortal platform (https://www.cbioportal.org/, accessed on September 3, 2024) was used to investigate LRFN4’s genetic alterations in a pan-cancer cohort. We selected the “TCGA Pan Cancer Atlas Studies” dataset to characterize mutation frequencies, mutation types, and copy number alterations using the “Cancer Types Summary” module. The Simple Nucleotide Variation Level 4 dataset from the GDC portal, which was processed by MuTect2, was used. Mutation data were annotated with protein domain information using the “maftools” R package (version 2.2.10) to map mutations to specific protein domains (19, 20). Mutation hotspots within critical functional domains were identified, highlighting potential targets for further experimental validation.

A correlation analysis was performed to investigate the relationship between LRFN4 expression and immune checkpoint genes using the standardized pan-cancer dataset from the UCSC database (https://xenabrowser.net, accessed on September 3, 2024). Specifically, we utilized the TCGA TARGET GTEx dataset (PANCAN, N=19,131, G=60,499) (17). The gene ENSG00000173621 (LRFN4) along with 60 immune checkpoint-related genes, including 24 inhibitory and 36 stimulatory genes, as defined in The Immune Landscape of Cancer (23), were extracted. We focused on samples derived from primary solid tumors and primary hematologic malignancies (bone marrow and peripheral blood), excluding normal tissue to ensure a tumor-specific context. Expression values were log₂(x+0.001)-transformed to stabilize variance and improve data normalization. Pearson correlation coefficients were calculated to quantify the relationship between LRFN4 and immune checkpoint genes. We further applied this methodology to analyze the correlation between LRFN4 and five immune pathway marker gene sets. The following selects eight widely - studied immune checkpoint - related genes to specifically analyze their correlations with LRFN4.

The xCell method (23), as implemented through the IOBR R package (24) (version 0.99.9), is employed to deconvolute gene expression data into immune infiltration scores for 67 immune and stromal cell types, thereby assessing immune cell infiltration in tumor samples.In addition, we utilized the “immune-gene” module of the TIMER2 web server (http://timer.cistrome.org/, accessed on September 4, 2024) to investigate the association between LRFN4 expression and immune cell infiltration across TCGA tumor types, with a focus on key cell subsets such as CD8+ T cells and cancer-associated fibroblasts (CAFs) (25). We focused our analysis on seven gastrointestinal cancers (esophageal, gastric, colorectal, liver, biliary tract, pancreatic, and small intestine cancers) due to their high incidence and mortality rates, as well as the availability of comprehensive data from public databases such as GEO and TCGA. These cancers share common features in the tumor microenvironment and immune response, making them ideal for investigating the role of LRFN4 in immune cell infiltration and tumor progression.Key immune cell subsets such as CD8+ T cells and cancer-associated fibroblasts (CAFs) (25) play a critical role in shaping the immunosuppressive TME. Immune infiltration was quantified using multiple computational algorithms, including TIMERCIBERSORT (25), CIBERSORT-abs, QUANTISEQ (26), xCell (23), MCPCOUNTER (27), and EPIC (28). This multi-algorithm approach ensured a comprehensive assessment of immune infiltration. Spearman rank correlation tests, adjusted for tumor purity, were applied to obtain P-values and partial correlation coefficients (cor).

PPI analysis of LRFN4 (Homo sapiens) was conducted using the STRING database (https://string-db.org, accessed on September 4, 2024) (28), with parameters set to an interaction score ≥ 0.150 (low confidence), and incorporating evidence-based edges, 50 first-shell interactors, and experimental validation as sources. The top 100 genes most strongly correlated with LRFN4 across TCGA datasets were identified using the GEPIA2 web tool (http://gepia2.cancer-pku.cn, accessed on September 4, 2024) (14) via the “similar gene detection” module. Pearson correlation (log₂ TPM values) between LRFN4 and these genes was computed. Heatmaps showing partial Spearman correlation coefficients and adjusted p-values for tumor purity were generated by the “Gene_Corr” module of TIMER2 (http://timer.cistrome.org, accessed on September 4, 2024) (29). Overlapping genes between LRFN4-binding proteins and LRFN4-correlated genes were determined using Jvenn (https://bioinfo.genotoul.fr/jvenn, accessed on September 4, 2024), and subjected to KEGG pathway enrichment analysis (30–32), and enriched terms visualized using a cnetplot (circular = F, colorEdge = T, node_label = T).

All analyses were executed in R 3.6.3 (64-bit), considering P < 0.05 as statistically significant.

Human gastric cancer cell lines, HGC-27 and MKN-45, were procured from QINGQI (Shanghai) Biotechnology Development Co., Ltd. The cells were cultured in 1640 medium (SH30027.01, HyClone, China) supplemented with 10% fetal bovine serum (SH30396.02, HyClone, China) and 1% penicillin-streptomycin mixture (SV30010, HyClone, China). The cultivation occurred at 37°C in a 5% CO₂ incubator (Jiemei Electronics, CI-191C). The cell culture medium was refreshed every two days, and the cells were employed for subsequent experiments upon reaching the exponential growth phase.

MKN-45 cells in the logarithmic growth phase were seeded into culture vessels until 50% - 70% confluence was achieved. Subsequently, an overexpressed lentivirus at an MOI of 20 was introduced for cell infection. After 48 hours, the cells were cultured in puromycin-containing medium for an additional 48 hours to select for successfully transfected cells, with the survival of virus-infected cells after puromycin treatment indicating successful transfection. The same procedure was performed for LRFN4 overexpression in the HGC-27 cells. For gene knockdown, an shRNA sequence targeting LRFN4 (shRNA1212: CCATAACCTTATTGACGCACT) was designed and integrated into a lentiviral vector. Subsequent experiments involved three groups for MKN-45 cells: Control, OE-NC, and OE-LRFN4, and for HGC-27 cells, the groups were Control, shNC, and shLRFN4.

The efficiency of LRFN4 knockdown and overexpression models was assessed through quantitative polymerase chain reaction (qPCR) and Western blot (WB) analyses. RNA was extracted post-treatment, reverse transcribed to cDNA and subjected to qPCR using LRFN4-specific primers with GAPDH as a reference. The ΔΔCt method determined relative mRNA levels. Protein lysates from treated cells were separated by SDS-PAGE, transferred to membranes, and probed with LRFN4 and GAPDH antibodies. Densitometry quantified LRFN4 protein expression relative to GAPDH.

Cell lines with stable LRFN4 overexpression, LRNF4 knockdown, and corresponding controls were cultured separately at 37°C under 5% CO₂ until suitable density was achieved. For apoptosis analysis, the culture medium was removed, and cells were washed twice with PBS to eliminate residues. Trypsin was added for cell detachment, and the resulting suspension was centrifuged at 1000g for 5 minutes. The supernatant was discarded, and the cell pellet was resuspended in PBS to a concentration of 1×10⁶ cells/ml. A 100 μl aliquot of the suspension was transferred to a flow tube, mixed with 5 μl of Annexin V-FITC and 5 μl of PI staining solution, and incubated at RT in the dark for 20 minutes. After adding 400μl of PBS, flow cytometry was used to detect and record the proportion of Annexin V-FITC and PI positive cells. Flow cytometer software distinguished early apoptotic (Annexin V⁺/PI^-), late apoptotic (Annexin V⁺/PI⁺), and normal cells (Annexin V^-/PI^-). The apoptosis proportions of different groups were compared to assess the impact of LRFN4 manipulation.

For cell cycle analysis, exponentially growing cell lines (LRFN4 overexpression, knockdown, and control) were processed similarly. After cell pellet collection, pre-cooled 70% ethanol was added, and cells were gently mixed by pipetting and fixed at 4°C overnight. Cells were centrifuged at 1200g for 3 minutes to remove ethanol, washed twice with PBS, incubated with PI dye solution (containing RNase A) at RT in the dark for 30 minutes. The stained cell suspension was filtered into a loading tube for flow cytometry detection and fluorescence signal recording. The software was used to analyze cell cycle phase distribution (G0/G1, S, G2/M) ( Figure 1 ).

Gastric cancer patient tissue arrays were sourced from Beijing Mescape Biotechnology Co., Ltd. The submitted samples were preserved in 4% paraformaldehyde. Following fixation, these specimens underwent meticulously trimmed, dehydrated, embedded, sectioned, stained, and sealed, strictly adhering to the standard operating procedures (SOP) of the pathology laboratory of the unit for detailed examination. The expression and localization of the target gene LRFN4 were analyzed using Visiopharm Intelligent Full-line AI Digital Pathology Quantitative Analysis Software. Additionally, SPSS was employed to analyze the relationship between LRFN4 gene expression and patient demographic variables such as gender, age, and clinical stage.

Cells were harvested at the logarithmic growth phase. The procedure involved washing with PBS, lysing with RIPA buffer on ice (30 min), scraping, centrifugation (12,000 rpm, 4°C, 5 min). Subsequent steps included an additional centrifugation (5000 rpm, 4°C, 5 min), lysis with RIPA (ice, 20–30 min), sonication, and further centrifugation (12,000 rpm, 4°C, 10 min). Protein concentrations were determined using a BCA kit with BSA standards, samples were mixed with reagent and incubate at 37°C (30 min), followed by absorbance measurement at 562nm. Samples were then mixed with 5× loading buffer, boiled, and centrifuged. Prepare gels, load samples, electrophorese (80 V to 120 V), transfer proteins to a 0.2 μm PVDF membrane (100 V, 1 h). Blocking was performed with 5% non-fat milk in PBS-T (room temperature, 1 h/4°C overnight), followed by incubation with appropriately diluted primary and secondary antibodies, and washing with PBS-T. Mix ECL solutions A and B, applied to the membrane (1 min), wrap, expose film (1-5min), develop, and fix. In the Western blot analysis, we used Abcam antibodies for target protein detection. The Cleaved-Caspase-3 (ab2303, 1:500), Caspase 3 (ab90437, 1:1000), Cyclin D1 (ab226977, 1:500), Cdk4 (ab108357, 1:1000), anti - LRFN4 (ab106369, 1:1000), and anti - GAPDH (ab8245, 1:3000) antibodies were applied.

The data are expressed as mean values ± standard deviation (SD), with all experiments conducted in triplicate. All statistical evaluations were carried out using GraphPad Prism 7.0, SPSS (version 22.0), or R software (version 4.1.2). A significance threshold was set at P < 0.05. The following notation indicates statistical significance: ns for not significant; *P < 0.05; **P < 0.01; ***P < 0.001.

This study investigated the potential role of human LRFN4 in tumor development and progression by referencing its mRNA (NM_001363524.2) and protein (NP_001350453.1) sequences. The expression profiles of LRFN4 across multiple cancer types were analyzed using data from The Cancer Genome Atlas (TCGA) (33). As depicted in Figure 2 , LRFN4 expression was significantly higher in specific cancers including bladder cancer (BLCA), breast cancer (BRCA), cholangiocarcinoma (CHOL), colorectal adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), rectum adenocarcinoma (READ), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC) compared to matched normal tissues. LRFN4 is also widely expressed in various tumor cell lines. ( Figure 2 ).

To address the lack of matched normal tissues in certain cancers, normal tissue data from the GTEx database was integrated using the GEPIA platform (http://gepia.cancer-pku.cn). This analysis revealed significant differences in LRFN4 expression for diffuse large B-cell lymphoma (DLBC), glioblastoma (GBM), lower-grade glioma (LGG), skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), and thymoma (THYM) ( Figure 3 ). Notably, LRFN4 expression was markedly reduced in TGCT, thyroid carcinoma (THCA), and adrenocortical carcinoma (ACC) tumor tissues.

Given the observed pan-cancer overexpression of LRFN4, its association with cancer progression was further elucidated by assessing its correlation with pathological staging. Using the “pathological staging chart” module of GEPIA2, significant correlations between LRFN4 expression and pathological stages were identified in adrenocortical carcinoma (ACC), lung adenocarcinoma (LUAD), kidney renal clear cell carcinoma (KIRC), lung squamous cell carcinoma (LUSC), and ovarian cancer (OV) ( Figure 4 ). No significant correlations were observed in other cancer types. These findings highlight the potential value of LRFN4 as a biomarker for cancer progression and its implications for therapeutic strategy formulation.

Receiver Operating Characteristic (ROC) curves ( Figure 5 ) were constructed to assess LRFN4’s diagnostic potential in distinguishing tumors from normal tissues. LRFN4 demonstrated promising diagnostic utility across multiple cancers, with Area Under the Curve (AUC) values: ACC (0.819), BLCA (0.636), CHOL (1.000), COAD (0.776), LAML (0.695), DLBCL (0.816), ESCA (0.910), GBML (0.671), SARC (0.925), LUAD (0.711), OV (0.932), PRAD (0.797), STAD (0.954), TGCT (0.983), THCA (0.844), and OSCC (0.912), with AUC values closer to 1 indicating superior predictive performance.

Tumor samples were stratified into high- and low-expression groups for analyses of overall survival (OS) and disease-free survival (DFS) using TCGA datasets. Results demonstrated significant associations of high LRFN4 expression with poor OS in ACC (P < 0.001), CESC (P = 0.032), LUAD (P = 0.029), and LIHC (P = 0.001) ( Figure 6A ). DFS analysis ( Figure 6B ) showed correlation with worse outcomes for high LRFN4 expression in ACC (P = 1.5e-05), PRAD (P = 0.00024), SARC (P = 0.0032), and UVM (P = 0.00012), while low LRFN4 expression was associated with poor DFS in OV (P = 0.004) (34).

Using the Kaplan-Meier plotter tool, high LRFN4 expression was associated with poor OS (P = 0.017), distant metastasis-free survival (DMFS, P = 0.019), and recurrence-free survival (RFS, P = 0.049) in breast cancer, and with progression-free interval (PFI) in various other cancer types. Conversely, low LRFN4 expression was associated with poor OS in AML and OV (P < 0.05). High LRFN4 expression correlated with poor prognosis in gastric cancer (FP, P = 0.0011; PPS, P = 0.00018), ovarian cancer (RFS, P = 0.045), colon cancer (relapse-free survival, P = 0.0019), and lung cancer (P = 0.00026) ( Figure 7 ). Notably, low LRFN4 expression was related to poor prognosis in colon cancer (P = 0.0028), but showed no significant correlation with OS, PFS, RFS, or DSS in liver cancer. These findings suggest that LRFN4’s prognostic significance is tumor-type specific, indicating its potential as a prognostic biomarker across various malignancies.

Using the TISIDB portal, the association of LRFN4 with immune subtypes was examined across various cancers, including ACC, BLCA, BRCA, CESC, CHOL, KIRP, KIRC, LIHC, LUAD, LUSC, PAAD, PRAD, SARC, STAD, TGCT, THCA, and UCEC ( Figure 8 ). Significantly, LRFN4 expression was associated with immune subtypes, indicating its potential role in regulating immune responses in cancers.

LRFN4 expression was found to differentiate among different molecular types in cancer types such as ACC, BRCA, COAD, ESCA, GBM, LGG, LIHC, LUSC, OV, PCPG, SKCM, STAD, and UCEC ( Figure 9 ). These findings suggest a close relationship between LRFN4 expression and molecular subtypes, contributing to the molecular classification of tumors.Overall, these results imply the potential of LRFN4 as a biomarker for immune and molecular subtype classification of various cancers.

The expression of LRFN4 was examined for its relationship with TMB, which affects immunotherapy efficacy. LRFN4 expression correlated positively with TMB in ACC, LGG, LUAD, LUSC, PCPG, PRAD, SARC, STAD, and THYM, and negatively with KIRP ( Figure 10A ).A positive correlation was found between MSI and LRFN4 expression in BRCA, CESC, ESCA, HNSC, KICH, LUAD, LUSC, PRAD, STAD, TGCT, THCA, and THYM ( Figure 10B ). The relationship between LRFN4 and the three scores from the ESTIMATE algorithm was examined. A significant positive correlation was confirmed in UCEC, CHOL, KICH, DLBC, ACC, PCPG, LAML, UCS, OV, READ, MESO, BLCA, SKCM.P, KIRC, HNSC, and KIRP ( Figure 10C ), suggesting LRFN4’s potential role in anti-tumor immunity through influencing the TME composition.

Using TCGA cohort data, we analyzed the genetic alteration status of LRFN4 across multiple tumor types. As depicted in Figure 11C , the highest frequency of LRFN4 mutations, surpassing 5%, occurred in Head and Neck Squamous Cell Carcinoma (HNSCC), where alterations were predominantly classified as “mutation.” In gastric cancer, “amplification” represented the main type of copy number alteration (CNA), with mutation frequencies approximating 2% ( Figure 11C ) (35). These results indicate a widespread spectrum of genetic changes of LRFN4 across different cancers, suggesting its potential role in tumorigenesis. A comprehensive analysis of LRFN4 gene alteration types, locations, and cases is shown in Figure 11A . Amplification was observed as the most prevalent alteration type. Domain-specific changes, such as Q48*p of the fn3 domain ( Figure 11B ), were detected in isolated cases of lung adenocarcinoma (LUAD) and stomach adenocarcinoma (STAD), potentially leading to missense mutations in the LRFN4 gene, highlighting the diverse genetic landscapes associated with LRFN4 alterations.

To explore the clinical relevance, we examined the association between LRFN4 alterations and survival outcomes across various cancers. Despite the unavailability of certain data, such as rectum adenocarcinoma (READ) on the cBioPortal platform (36), the prevalence of LRFN4 alterations in digestive system malignancies was evaluated, showing an overall incidence of 3% ( Supplementary Figure S1A ).

The impact of LRFN4 gene alterations on expression levels was also evaluated. ( Supplementary Figure S1B ) For instance, copy number variations (CNVs) (37) were less frequent in colorectal adenocarcinoma (COAD) and pancreatic adenocarcinoma (PAAD). Notably, the correlation between LRFN4 expression levels and clinical features in COAD and PAAD was found to be weaker, as these tumor types exhibited fewer CNVs. These findings suggest that genomic alterations of LRFN4 are common across cancers and may affect its expression, contributing to the regulation of cancer progression.

Co-expression analyses of LRFN4 and ICP genes were performed across 33 tumor types. As shown in Figure 12A , almost all ICP-related genes exhibited significant co-expressed with LRFN4, except in LUSC and SARC, where a negative correlation was observed. In other tumor types, a positive correlation with LRFN4 was found (P < 0.05). These findings imply that high LRFN4 expression could predict immunotherapy responses targeting ICP genes, indicating its potential as a novel therapeutic target in immunotherapy (5). Interestingly, LRFN4 showed limited co-expression with ICP genes in READ and MESO, suggesting suboptimal immunotherapy responses for patients with high LRFN4 expression in these tumors. These results highlight LRFN4’s complex role in the tumor immune microenvironment and its potential as a prognostic biomarker or therapeutic target in human cancer immunotherapy.

To further explore LRFN4’s role in immune regulation, Pearson correlation coefficients between ENSG00000173621 (LRFN4) and marker genes of five major immune pathways were calculated (38). Except for CESC, KIPAN, and LUSC where negative correlations were observed, most immune-related genes positively correlated with LRFN4 expression across tumor types (P < 0.05) ( Figure 12B ). These findings suggest an intricate link between LRFN4 and immune microenvironment, highlighting its cancer-specific influence on immune pathway activation. A comprehensive and extensive correlation has been identified between LRFN4 and immune checkpoint genes. ( Figure 12C ).

Significant correlations were identified between LRFN4 expression and immune cell infiltration across all 44 cancer types (39, 40), showing notable positive associations with certain immune cell types (e.g., MEP cells, MSC cells, Th1 cells, Th2 cells) and significant negative correlations with others (P < 0.05) ( Figure 13A ). The deconvo_mcpcounter method (27) was employed to further analyze the correlation between LRFN4 expression and immune cell populations in seven digestive system cancers:

Hepatocellular carcinoma (LIHC): Significant positive correlations were observed between LRFN4 and various immune cell types (T cells, CD8+ T cells, cytotoxic lymphocytes, B lineage cells, NK cells, monocytic lineage cells, myeloid dendritic cells, endothelial cells, and fibroblasts).Esophageal cancer (ESCA): Correlations were found with T cells, CD8+ T cells, B lineage cells, and myeloid dendritic cells. Prostate adenocarcinoma (PRAD): LRFN4 was associated with T cells, CD8+ T cells, cytotoxic lymphocytes, B lineage cells, NK cells, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts. Stomach adenocarcinoma (STAD): Significant associations were observed with T cells, neutrophils, and fibroblasts. Pancreatic adenocarcinoma (PAAD): Positive correlations were noted with CD8+ T cells, cytotoxic lymphocytes, B lineage cells, NK cells, myeloid dendritic cells, neutrophils, endothelial cells, and fibroblasts ( Figure 13B ).

To elucidate the molecular mechanisms of LRFN4 in tumorigenesis, analyses for LRFN4-binding proteins and correlated genes were performed, followed by pathway enrichment studies.

Using STRING, 50 LRFN4-binding proteins (supported by experimental evidence) were identified, with their interaction network shown in Figure 14A (41). Venn diagram analysis revealed TPX2 and KIF18B as common genes between LRFN4-binding proteins and correlated gene sets. ( Figure 14B ) The heatmap ( Figure 14C ) indicated positive correlations of LRFN4 with these five genes across most analyzed cancer types. From TCGA tumor expression data via GEPIA2, the top 100 genes positively correlated with LRFN4 expression were determined, among which TPX2 (R = 0.36), KIF18B (R = 0.39), RCE1 (R = 0.53), PCNXL3 (R = 0.49), and SAMD1 (R = 0.48) showed significant correlations (P < 0.001) ( Figure 14D ). KEGG and GO enrichment analyses were further performed by integrating the LRFN4-binding protein dataset with the top 100 LRFN4-correlated genes. KEGG enrichment ( Figure 14E , (42) suggested LRFN4’s potential involvement in tumorigenesis through pathways like the cell cycle, cell senescence, and chemokine signaling. GO analysis ( Figure 14F ) showed that associated genes participate in cell proliferation processes, including mitosis and chemokine signaling, underscoring LRFN4’s essential role in cell mitosis and proliferation. Overall, these results highlight LRFN4’s potential as a key regulator in tumor pathogenesis and its value in cancer prognosis and therapy.

We detected the expression of LRFN4 protein in 80 cases of STAD tissues using fluorescence-based multiplex immunohistochemistry (mIHC) and confirmed these findings. LRFN4 protein was localized in both the cytoplasm and the interstitium ( Figure 15A ). ( Table 1 ) Furthermore, LRFN4 protein expression exhibited significant associations with vascular invasion, tumor size, and TNM stage ( Table 2 ).

Flow cytometry was employed to detect specific alterations in apoptosis and cell cycle progression in STAD cells following the knockdown and overexpression of LRFN4. The results indicate that the knockdown of LRFN4 significantly promotes apoptosis in STAD cells and markedly reduces S phase arrest. This suggests that LRFN4 may enhance the survival of gastric cancer cells by inhibiting apoptosis and may play a role in regulating the cell cycle, particularly during the S phase. Its knockdown impedes the normal progression through the S phase, thereby impacting the cells’ proliferative capacity. Future studies should further investigate the molecular mechanisms by which LRFN4 regulates apoptosis and cell cycle regulation. ( Figures 15B, C ).

The Western Blot analysis indicated a positive correlation between high LRFN4 expression and the levels of cyclin D1 and CDK4, suggesting that LRFN4 may contribute to the promotion of cell cycle progression. In contrast, a negative correlation was observed between elevated LRFN4 expression and cleaved-caspase-3 expression, indicating that LRFN4 might inhibit apoptosis by suppressing caspase-3 activation. These findings underscore the potential of LRFN4 as a therapeutic target in cancer treatment ( Figure 1 ).