As shown in Table S1 , a total of 21 cancer tissue and adjacent normal tissues were surgically collected from KIRC patients at The First Affiliated Hospital of Soochow University from January 2023 to April 2023. The samples were postoperatively pathologically confirmed. None of the patients had anti-tumor therapy prior to operation. This study was approved by the Ethics Committee of The First Affiliated Hospital of Soochow University with the number of 2023-181. All patients signed written informed consent.

The expression of OCLN between tumor and adjacent normal tissues was analyzed by TIMER2 (18) (http://timer.cistrome.org/). The mRNA sequencing data including 542 KIRC specimens and 72 normal renal tissue specimens and the corresponding clinical information were downloaded from TCGA database on Mar, 23^rd, 2023 (https://portal.gdc.cancer.gov/repository). As illustrated in Table 1 , a total of 533 KIRC patients with full clinical information were included in this study. The R program (v4.2.2) was used to analyze the expression of OCLN between KIRC and normal kidney tissues and paired samples in TCGA-KIRC datasets, and the “ggpubr” and “ggplot2” R package were used for visualization. The “survival” R package was used for statistical analysis of survival data. The protein level and subcellular localization of OCLN was assessed by the Human Protein Atlas (HPA) database (19) (https://www.proteinatlas.org/).

The clinical data of TCGA-KIRC were used to analyze the correlation between the OCLN expression and the clinical characteristics of KIRC patients, including age, gender, tumor grade, clinical stage and TNM stage.

The OCLN promoter methylation level between KIRC and normal renal tissues was analyzed in the UALCAN database (20) (http://ualcan.path.uab.edu/index.html). Moreover, a stratified analysis based on patients’ age, gender, tumor grade, clinical stage, and N stage was performed.

The functional enrichment analysis of differentially expressed genes (DEGs) was performed at the gene ontology (21) (GO) and Kyoto Encyclopedia of Genes and Genomes (22) (KEGG) level using “ClusterProfiler” package (23) and “enrichplot” package in R. Gene set enrichment analysis (GSEA) (24) was used to explore the underlying molecular mechanisms. The terms with p<0.05 were considered to be statistically significant. The STRING platform (25) (v12.0) was used to screen genes potentially interacted with OCLN (https://cn.string-db.org/).

Univariate and multivariate Cox regression analysis were conducted using “survival” R package to evaluate the association between OCLN expression and KIRC prognosis. In addition, a nomogram was constructed by the “rms” R package (26) and “regplot” R package was applied to predict 1, 3 and 5 years overall survival (OS) in KIRC patients.

“CIBERSORT” package (27) was used to calculate the abundance across 22 immune cell subgroups in all samples. Samples with p<0.05 were chosen for further analysis to compare the differences in infiltration of various immune cells between the high and low OCLN expression groups. Kaplan-Meier plotter (http://kmplot.com/) databases were used to analyze the relationship between the expression of OCLN and OS in KIRC patients in different immune cell subsets. “ESTIMATE” was used to calculate the stromal score and immune score for KIRC. The relationship between stromal/immune score and OCLN expression was analyzed, the median value was set as the dividing line of high and low score. ssGSEA analysis was performed using the “GSEABase” and “GSVA” R packages to obtain scores for immune-related functions, and the scores were calibrated to compare differences in immune-related functions between high and low OCLN expression groups. The correlation between the OCLN expression and the expression level of immune checkpoint‐related genes was further analyzed.

Proteins were extracted from KIRC and adjacent normal renal tissues by RIPA lysate (Beyotime, China) supplemented with proteinase inhibitor. Proteins were loaded into an SDS-PAGE gel, and then transferred into a PVDF membrane. The membrane was blocked with 5% skim milk for 2h at room temperature and then incubated overnight with the corresponding primary antibody (abs143408, absin) at 4°C. Finally, the membrane was incubated using horseradish peroxidase-conjugated secondary antibodies and exposed by an enhanced chemiluminescence (ECL) kit (Beyotime).

The paraffin-embedded specimens were cut into 4 µm thick section for immunohistochemical staining. All sections were dewaxed with xylene and then rehydrated in gradient alcohol. The sections were incubated with 3% H₂O₂ at room temperature for 15 minutes and then washed in PBS. Next, 5% BSA was incubated for 1 h at 37°C to block nonspecific binding sites. The sections were incubated with primary antibodies (abs143408, absin) overnight at 4°C. IHC was performed by SP Rabbit & Mouse HRP Kit (DAB) (CWBIO, China).

The human KIRC cell line (769P cells) were maintained at 37°C in a humidified atmosphere containing 5% CO₂ in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (Gibco, USA) and 1% penicillin/streptomycin solution (Beyotime, China). According to the manufacturer’s protocol, the plasmids were transfected into 769P cells using lipofectamine_2000 transfection reagent (Invitrogen, USA). After 48 h of transfection, RIPA buffer (Beyotime, China) was used to extract total proteins.

The EdU assay kit was purchased from Beyotime Company (C0078S, China). According to the manufacturer’s protocol, transfected cells were treated with EdU reagent for 3h. Then, cells were fixed with 4% paraformaldehyde for 15 min at room temperature, followed by incubation with 0.3% Triton X-100 for 15 min at room temperature. Finally, cells were stained with fluorescent dye and Hoechst. The Nikon TI2-D-PD inverted microscope (Japan) was used to capture the images.

For the wound healing assay, after 48 hours of transfection, 20 μL of pipette tip was used to scratch the cell plate and the cells were washed by PBS. Then, cells were continued to be cultured with 1% fetal bovine serum (Gibco, USA) at 37°C for 24h. The photographs were taken at 0h and 24h. For the transwell assay, after 48 hours of transfection, 5×10⁴ cells were seeded in a 24-well Transwell chamber (Corning, USA) with a layer of Matrigel matrix glue (Corning, USA) (matrix glue: Serum-free medium=1:6). After 36h, the cells in the upper chamber were fixed with 4% paraformaldehyde for 20 min at room temperature and stain with crystal violet (Sangon Biotech, China) for 20 min. The Nikon TI2-D-PD inverted microscope (Japan) was used to capture the images.

5-Azacytidine was purchased from MedChemExpress Company (HY-10586, USA). 769P cell were seeded into six-well plates at appropriate density the day before dosing. After 48h of addition of different concentrations of 5-Azacytidine, the cells were used for subsequent processing. TRIzol (Ambion, USA) was used to extract the total RNA from 769P cells. Then, the HiScript III RT SuperMix for qPCR (+gDNA wiper) (Vazyme, China) was used to perform the reverse transcription reactions. 2X Rapid Taq Master Mix (Vazyme, China) was used to perform gene amplification followed by PCR products added to a 1% agarose gel for electrophoresis. The procedure for protein extraction and western blot were the same as above. The detailed primer sequences are as follows:

After 48h treatment with 5-Azacytidine, genomic DNA was extracted from 769P cells with the FastPure Cell/Tissue DNA Isolation Mini kit (Vazyme, China), according to the manufacturer’s instructions. Next, the DNA(1μg) was treated with EpiArt Magnetic DNA Methylation Bisulfite kit following manufacturer’s instructions (Vazyme, China). The methylated and unmethylated Twist were amplified using 2xEpiArt HS Taq Master Mix (Vazyme, China). The detailed primer sequences were as follows: Methylated: 5′-TTTAAGGTTTTATTCGAAGTAGGC-3′(forward), 5′-GTTACGACCCGAAAAACGAA-3′(reverse); the unmethylated: 5′-TTAAGGTTTTATTTGAAGTAGGTGG-3′(forward), 5′-ACATTACAACCCAAAAAACAAA-3′(reverse). The PCR products were separated in a 1.5% agarose gel.

All data were analyzed using R software (v4.2.2). Univariate and multivariate Cox regression analysis were used to identify independent prognostic factors. All experiments were independently performed at least three times. All statistical analyses were conducted using GraphPad Prism 9.0 (GraphPad, San Diego, CA, USA). The Student’s t-test was used to analyze the differences between two groups, and p<0.05 was considered to be statistically significant.

By TIMER database mining, the expression level of OCLN was obtained from a pan-cancer perspective. As shown in Figure 1A , the expression of OCLN was down-regulated in eight cancer types including KIRC. In Figures 1B, C , OCLN mRNA expression was significantly down-regulated in KIRC compared to normal tissues from the TCGA dataset. Kaplan-Meier survival curves showed that low OCLN expression was correlated with poor prognosis for KIRC patients ( Figure 1D ). Moreover, the protein-level expression of OCLN shown in Figure 1E was also down-regulated in KIRC according to the records in HPA database (28), and OCLN subcellular localization was obtained by immunofluorescence localization of the plasma membrane and cell junctions in A-431 and U2OS cells ( Figure 1F ). Overall, the analysis by public data sources summarized that OCLN was significantly downregulated in KIRC both at the mRNA and protein level.

The correlation between OCLN expression and corresponding clinicopathological features was analyzed to explore the clinical role of OCLN in KIRC. As shown in Figures 2A–G , the expression of OCLN was significantly related to tumor grade, clinical stage, T stage, and M stage in KIRC patients. The lower expression of OCLN was correlated with the worse pathological grade, later clinical stage, higher T stage and M stage in KIRC. The results indicated that low expression of OCLN may promote proliferation and metastasis of KIRC, resulting in a poor prognosis for KIRC patients.

To further explore the underlying mechanism of OCLN low-expression in KIRC, DNA methylation analysis was performed. As known, DNA methylation is an important event in the epigenetic modification of the genome and is closely related to disease process. The DNA methylation levels of OCLN for KIRC were tested using the UALCAN database. As shown in Figures 3A, B , the methylation level of OCLN in KIRC was higher than that in normal tissues, but there was no difference between the gender groups. Moreover, the methylation level of OCLN was positively correlated with age, clinical stage, and tumor grade level ( Figures 3C–E ). As N stage increased, the methylation level of OCLN tended to be highly upregulated ( Figure 3F ). The results suggested that DNA hypermethylation may be one of the potential reasons for low-expression of OCLN in KIRC.

To further evaluate the prognostic value of OCLN in KIRC, univariate and multivariate Cox regression were performed. The univariate Cox analysis indicated that OCLN expression (p<0.001), age (p<0.001), grade (p<0.001), and stage (p<0.001) were significant factors for overall survival of KIRC patients ( Figure 4A ). Multivariate Cox regression analysis further identified that the expression of OCLN (p=0.013) was an independent prognostic factor ( Figure 4B ). Based on these findings, a novel nomogram with OCLN expression and clinical parameters was constructed to predict 1-,3- and 5-year OS for KIRC patients ( Figure 4C ), and a higher score on the nomogram indicated a worse prognosis for a KIRC patient. As shown in Figure 4D , the nomogram calibration curve had good reliability in the observation of 1-, 3-, and 5- year survival rate.

Patients with KIRC in TCGA were divided into high and low risk groups according to the median value of OCLN expression to explore the underlying molecular function of OCLN. A total of 834 DEGs were identified based on differential expression analysis between high and low risk groups, among which 335 genes were upregulated and 499 genes were downregulated (|log₂FC |> 1 and FDR < 0.01, Table S2 ). As shown in Figures 5A–C , GO enrichment analysis at biological process (BP), cellular component (CC), and molecular function (MF) levels showed that DEGs were involved in immune-related functions. For example, in GO-BP domain, the genes were enriched in humoral immune response, B cell receptor signaling pathway and regulation of B cell activation. In GO-CC domain, the genes were associated with immunoglobulin complex circulating and immunoglobulin complex. In GO-MF domain, immunoglobulin receptor binding was observed to be functionally enriched. As shown in Figures 5D, E , GSEA enrichment analysis indicated that the drug metabolism cytochrome P450, epithelial cell signaling in helicobacter pylori infection and metabolism of xenobiotics by cytochrome P450 were upregulated in the low OCLN expression group, and the DEGs were significantly enriched in KEGG pathways such as IL-17 signaling pathway, Tight junction and primary immunodeficiency.

To further decipher the potential interplay among OCLN and DEGs, an OCLN-related protein-protein interaction network was constructed and analyzed. As shown in Figures 5F, G , the top 50 up- and down-regulated genes between OCLN high- and low-expression group were clustered, and a total of 21 genes with direct links with OCLN were visualized by STRING online. Through correlation analysis in the GEPIA (29) database, a total of 14 genes, i.e., CDH1, CGN, CLDN7, CLDN8, CLDN16, CLDN19, DSP, EGF, ILDR1, MAL, MAL2, MARVELD2, MPP7 and TJP3, were determined to be highly correlated with OCLN (R>0.5; P<0.05) ( Figure S1 ).

The role of OCLN in the immune microenvironment of KIRC was explored using the ESTIMATE method to analyze the correlation between OCLN expression and immune cell infiltration. As shown in Figure 6A , the results showed that the low-OCLN-expression group had higher levels of infiltration of Tregs and Macrophages M0 cells than the low-OCLN-expression group. The effects of OCLN expression on the OS of KIRC patients with high or low immune cell infiltration were also explored using Kaplan-Meier Plotter database. The high expression of OCLN of KIRC in enriched basophils, enriched macrophages, decreased mesenchymal stem cells and decreased Type 1 T-helper cells had better prognosis ( Figure S2 ). It suggested that high expression of OCLN in KIRC may affect prognoses partly due to immune cells infiltration. As observed in Figure 6B , the low OCLN-expression group revealed higher scores in multiple immune functions, such as aDCs, APC_co_stimulation, CCR, CD8⁺T cells, Inflammation-promoting, Parainflammation, T_cell_co_inhibition, TIL, Tfh,checkpoint, Cytolytic activity and Type_I_IFN_Reponse. The ESTIMATE algorithm was also applied to calculate stromal score, immune score, and ESTIMATE score. As shown in Figure 6C , the lower OCLN level was associated with higher stromal score, immune score and ESTIMATE score. In addition, the interactions between OCLN and immune checkpoint genes were evaluated. As shown in Figure 6D , the majority of immune checkpoint genes increased significantly in the OCLN low group, such as CTLA4 and PDCD1, and OCLN was negatively associated with overwhelming majority immune checkpoint molecules ( Figure 6E ). In summary, OCLN played potential roles in immune escape of KIRC and could serve as a candidate target for KIRC immunotherapy.

Western blot and immunohistochemistry (IHC) experiments were performed respectively to validate the expression of OCLN in clinical samples. As shown in Figures 7A, B , the protein expression of OCLN was decreased in KIRC tissue samples, and the immunohistochemistry staining further verified that the expression of OCLN was lower in tumor tissues. 5-azacytidine(5-Azac) is a DNA methylation inhibitor. To investigate the effect of promoter methylation on OCLN gene expression, 769P cells was treated with 5-Azac at concentrations of 0 μM, 2.5 μM, and 5 μM, respectively. As shown in Figures 7C, D , treated with 48 hours of 5-azacytidine induced demethylation of the OCLN promoter and increased OCLN mRNA and protein expression in 769P cells.

Moreover, 769P cell line was selected for follow-up functional experiments to confirm the results of bioinformatics analysis. As shown in Figures 8A–C . OCLN was successfully overexpressed in 769P cells, and the overexpression of OCLN significantly inhibited cell proliferation as indicated by EdU assays. Meanwhile, as shown in Figures 8D–G , OCLN overexpression significantly reduced the migration and invasion abilities of 769P cells.