Multi-omics pan-cancer analysis reveals an immunological role and prognostic potential of WDR76

Multi-omics pan-cancer analysis reveals an immunological role and prognostic potential of WDR76 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics pan-cancer analysis reveals an immunological role and prognostic potential of WDR76 Md Mohtasim Billah, Khadiza Mabsurah, Kaushik Ahammad, Israt Jahan Yeana, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7837284/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives WD repeat domain 76 (WDR76) significantly influences various metabolic and genomic processes, including RAS protein degradation, which plays a role in tumor cells. However, a systematic pan-cancer analysis of WDR76 has not been conducted. Therefore, this study aimed to identify the role of WDR76 in human tumors. Methods This study used publicly available databases and tools, including TCGA, UALCAN, GEPIA2, TIMER2.0, KMplotter, cBioPortal, STRING, Cytoscape, and TCGAplot, to investigate the potential roles of WDR76 in different types of tumors. Results WDR76 expression was higher in several tumor types; however, the prognostic associations varied by cancer and attenuated after covariate adjustment and FDR correction. Notably, promoter methylation of WDR76 was higher in tumors than in normal cells in multiple cancers. Deep deletions and point mutations were the most frequent alterations, with an overall frequency of approximately 1% in TCGA. Immune infiltration analysis using different algorithms revealed a correlation between CAF infiltration and different tumors, especially KIRC, KIRP, and LGG, with significant clinical outcomes. In the tumor immune microenvironment, WDR76 was positively correlated with different immune cells, stromal cells, immune checkpoint inhibitors, and stimulator-associated genes, suggesting a broad interaction with cancer immunity. The correlation between WDR76 and TMB and MSI was significant in UCEC, STAD, KIRC, and COAD. Functional and pathway enrichment analyses revealed an association between WDR76 and various cellular processes and functions. Conclusion Our analysis offers insights into WDR76’s context-dependent role, consistent with prior evidence of RAS degradation (tumor-suppressive), along with tumor-type-specific associations, prognostic significance, and immunological role across all tumors. WDR76 Pan-cancer analysis cancer biology Human Tumors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Cancer is a complex and heterogeneous disease characterized by the uncontrolled proliferation of abnormal cells that can invade and destroy healthy tissues ( 1 ). It arises due to genetic mutations and alterations in the cellular processes that regulate growth and division ( 2 ). According to the International Agency for Research on Cancer (IARC), 9.7 million individuals lost their lives to cancer globally in 2022, an estimate based on data from 115 countries. The IARC also estimates that 20 million new cancer cases were diagnosed in 2022, and one in every nine men and one in every twelve women will die from the disease ( 3 ). Despite remarkable advancements in our understanding of the molecular and cellular processes underlying cancer, it remains one of the most complicated and formidable challenges confronting the healthcare community ( 4 ). The WDR76 gene, located on chromosome 15q15.3, is a putative member of the WD40-repeat-containing domain superfamily ( 5 ). Although its significance is acknowledged, the precise functions of WDR76 in various diseases and cellular processes remain elusive. Recent investigations have begun to elucidate its roles, particularly in cancer biology, DNA damage response, and metabolic regulation ( 5 , 6 ). WDR76 interacts with proteins involved in DNA repair and heterochromatin, suggesting its potential role in maintaining genomic stability and regulating protein quality under stress ( 7 ). WDR76 has been identified as a tumor suppressor, notably in liver and colorectal cancers, and mediates the degradation of RAS proteins. These RAS proteins (H, K, and NRAS) are small GTPases that are crucial for regulating pathophysiological processes, such as cell proliferation, transformation, and development. WDR76 functions as an E3 linker protein that promotes polyubiquitination-dependent degradation of RAS. This degradation process inhibits cancer cell growth, transformation, and invasion, indicating that WDR76 plays a vital role in controlling tumor formation through RAS destabilization ( 8 , 9 ). RAS mutations that lock RAS proteins in GTP-binding forms are prevalent in most human cancers ( 8 , 10 ). RAS overexpression can also contribute to malignancy in colorectal cancer (CRC), lung adenocarcinoma, and breast cancer ( 6 , 8 , 9 , 11 , 12 ). Given the evidence that WDR76 degrades RAS and suppresses transformation in preclinical models, a pan-cancer survey is needed to clarify whether its expression and alteration patterns consistently align with tumor-suppressive activity or reveal cancer type-specific divergence. However, a comprehensive pan-cancer analysis of WDR76 is currently unavailable, and its role in human cancer development remains unclear. The study of pan-cancer tumorigenesis and progression has recently garnered increasing interest. The field of pan-cancer research is transitioning from fundamental research to clinical application ( 13 ). A pan-cancer analysis entails investigating clinical and genomic characteristics across a range of cancer types to generate hypotheses about gene expression patterns, immune interactions, and possible relevance to immunotherapy ( 14 ). This study aimed to examine the genetic changes and expression disruptions of WDR76 in various cancer types and assess its potential clinical significance. Utilizing advanced bioinformatics techniques and data from The Cancer Genome Atlas (TCGA) and Genotype -Tissue Expression (GTEx) databases, we conducted a comprehensive pan-cancer analysis of WDR76 ( 15 , 16 ). Our investigation encompassed gene expression, genetic alterations, DNA methylation patterns, survival outcomes, immune features, and functional enrichment analyses to elucidate WDR76's role in human malignancies. By identifying the pan-cancer role of WDR76, we aimed to unravel its potential as a biomarker and therapeutic target. 2 Method 2.1 Sample information: Most of the original data for the systematic pan-cancer analysis of WDR76 were obtained from public databases by The Cancer Genome Atlas (TCGA). The 33 cancers of interest in this study and their full names, along with their abbreviations, are listed in Supplementary Material Table 1. 2.2 Gene expression analysis: Expression data (log2 TPM+1) of WDR76 in 33 types of tumors and normal tissues were acquired from The Cancer Genome Atlas (TCGA) database. The differential expression of WDR76 was analyzed using TIMER2.0, GEPIA2, and the TCGAplot R package. 2.3 Proteomic expression analysis: The proteomic expression of WDR76 was examined to evaluate mRNA expression at the protein level using the UALCAN portal, which provides protein expression analysis options using data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the International Cancer Proteogenome Consortium (ICPC) datasets (17). 2.4 Survival analysis: For the survival analysis of the WDR76 gene in multiple cancers, we used the Kaplan-Meier plotter tool based on Affymetrix microarray information from TCGA databases (18). The prognostic value of WDR76 expression in 21 cancers was evaluated using overall survival (OS) and relapse-free survival (RFS). According to the p-value of WDR76 expression, the patients were divided into high- and low-expression groups (accessed March 01, 2025). 2.5 Methylation analysis: UALCAN is a web-based tool, designed to facilitate the analysis of publicly available cancer omics data. It can evaluate the epigenetic regulation of gene expression by promoter methylation based on the TCGA database (19). The UALCAN “TCGA analysis” module was used to analyze the promoter methylation level of WDR76 in normal tissues and cancers. Methylation levels are reported as β-values, ranging from 0 (unmethylated) to 1 (fully methylated), representing the fraction of methylation at each CpG site. 2.6 Genetic alteration analysis: The cBioPortal database is an open platform for analyzing cancer genomics data. Genetic alteration data of WDR76, including alteration frequency, mutation type, and mutated site, were available from the cBioPortal database. The “TCGA Pan Cancer Atlas Studies” dataset was selected to obtain the alteration frequency of WDR76. The “Cancer Types Summary” module was selected to observe the results of mutation, amplification, deep deletion, and multiple alterations across all TCGA tumors. Additionally, the “Mutation” module was used to identify WDR76 alterations. The “Survival” module under the “Comparison/Survival” section was used to identify the impact of gene mutations on the survival of cancer patients (18,20,21). 2.7 Correlation between WDR76 expression and CAF infiltration : This study evaluated the potential correlation between WDR76 expression and cancer-associated fibroblast (CAF) infiltration in all TCGA-documented tumors by selecting the ‘gene’ module under the ‘immune’ section of TIMER2.0, utilizing EPIC, MCPCOUNTER, and TIDE algorithms (22). We generated a correlation heatmap to visualize the association between WDR76 gene expression and CAF infiltration in various cancer types. Subsequently, the scatter diagram of the correlation heatmap was added with purity adjustment in Fig. 5(B-I). We computed CAF estimates using EPIC and performed the Cox regression model based on these estimates. The clinical relevance of CAF infiltration was explored by generating Kaplan–Meier plots. 2.8 Correlation between tumor immune microenvironment and WDR76 in pan-cancer: We utilized the TCGAplot (v8.0.0) R package to investigate the association between WDR76 mRNA expression levels and various immune-related factors, including immune checkpoints, immune cells, immune scores, and immune regulatory genes (immunoinhibitory, immunostimulatory) (23). 2.9 Correlation of WDR76 with TMB and MSI: Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI) are significant predictive biomarkers utilized in immunotherapy (24,25). The TMB and MSI matrix was extracted from The Cancer Genome Atlas (TCGA), and the association between WDR76 expression and TMB and MSI was analyzed using Pearson correlation analysis. A radar plot was generated to visualize the data using the TCGAplot R package (v8.0.0). This plot enabled the comparative analysis of WDR76 expression with TMB and MSI across various cancer types, facilitating a clearer understanding of their potential interplay. 2.10 Drug sensitivity analysis: The GSCA (Gene Set Cancer Analysis) platform was used to evaluate cell line pharmacogenomic correlations by integrating gene expression profiles with drug response data. The analysis incorporated data from 33 cancer types in TCGA and over 750 small-molecule compounds from the GDSC (Genomics of Drug Sensitivity in Cancer) and the CTRP (Cancer Therapeutic Response Portal) databases. Correlation between gene sets and drug sensitivity was assessed using Spearman correlation, and significance was adjusted by false discovery rate (FDR) correction. For cases where multiple drugs targeted the same molecule, results were aggregated per target to improve robustness (26). 2.11 PPI Network Analysis: The PPI (protein-protein interaction) network analysis was performed using the STRING database (27). To improve the reliability of the analysis, we set custom parameters, including, minimum interaction score: “low confidence (0.150)”; max number of interactions: “no more than 50 interactors”, to obtain relatively all possible protein-protein interactions of WDR76. We exported the results as a tabular text file in TSV format and subsequently imported it into Cytoscape (version 3.10.3) to construct the interaction map (28). 2.12 Intersection Analysis: We used the GEPIA2 database to obtain positively co-expressed genes using the “Similar Gene Detection” module. An intersection analysis was performed to identify the common genes between co-expressed and interacted genes obtained from GEPIA2 and STRING database using the “VennDiagram” R (v 4.4.2) package (29). The “Correlation Analysis” module of GEPIA2 and the “Gene Corr” module of TIMER2.0 were utilized to generate the scatter diagram and correlation heatmap of the intersected genes. 2.13 Enrichment Analysis: The BP (biological process), CC (cellular component), and MF (molecular function) of Gene Ontology functional enrichment analysis was performed by the “clusterProfiler” R (v 4.4.2) package using the combined gene list of STRING and GEPIA2 (30) (31). “Wikipathways”, “Reactome”, and “Kyoto Encyclopedia of Genes and Genomes (KEGG)” were assessed to perform pathway enrichment analysis using “clusterProfiler” and “ReactomePA” R (v 4.4.2) package (30,32–35). In addition, we obtained permission from KEGG to use KEGG and KEGG-related figures. 2.14 Statistical Analysis: The ANOVA methods in GEPIA2 and the Wilcoxon test in TIMER2 were used to assess the statistical significance of differential expression. Gene expression differences between the pathogenic stages in GEPIA2 were determined using a one-way ANOVA. KM plotter employed Cox proportional hazards regression for survival analysis. The TCGAplot R package's Pearson correlation analysis was used to examine the relationship between WDR76 expression and TMB, and MSI. The Spearman correlation test in TIMER2 was used to determine the p values and partial correlations for the immune cell infiltration analysis. A statistically significant difference was defined as p < 0.05. Almost all analyses were further checked as of October 6, 2025. 3 Results 3.1 Expression analysis : We initially used TIMER2.0 to analyze the expression of WDR76. The expression of WDR76 was significantly higher in tumor cells than in the corresponding normal tissues, including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PCPG, STAD, and UCEC (Fig. 1 A). However, lower expression levels than the corresponding normal tissue were observed in KICH, KIRP, and PRAD (Fig. 1 A). The TCGAplot R package was utilized to validate the differential expression results, which generates the pan-cancer box plot in Fig. 1 B showed that significant higher expression in sixteen tumors including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PCPG, SARC, STAD, and UCEC, whereas decreased mRNA expression in tumors compared with corresponding normal tissue found in KICH and PRAD. Expression analysis using the GEPIA2 boxplot demonstrated significantly higher expression of WDR76 in BRCA, CESC, CHOL, DLBC, ESCA, GBM, LGG, LIHC, LUSC, PAAD, PCPG, SKCM, STAD, and THYM (Supplementary Fig. 1). Utilizing the "stage plot" module of GEPIA2, WDR76 expression across different cancer stages was found to be associated with ACC, COAD, KICH, KIRP, LUAD, SKCM, LIHC, OV, and TGCT (Fig. 1 C). Furthermore, UALCAN demonstrated that the proteomic expression of WDR76 in BRCA, Clear cell RCC, GBM, LUSC, and UCEC was significantly elevated compared with the corresponding normal tissue (Supplementary Fig. 2). 3.2 Survival analysis : To analyze the potential prognostic significance of WDR76 based on TCGA datasets, we investigated the correlation between WDR76 expression and the prognosis of patients with different tumors using KM Plotter. In this study, we found that higher WDR76 expression was associated with poor OS in cases of BLCA, ESCA, KIRC, KIRP, LIHC, LUAD, PAAD, and SARC (Fig. 2 ). Meanwhile, low WDR76 expression was associated with a negative impact on OS in THYM, THCA, CESC, and READ (Fig. 2 ). Furthermore, increased WDR76 expression was correlated with unfavorable RFS outcomes in patients with KIRP, LIHC, PAAD, SARC, and THCA (Fig. 2 ). Alternatively, low WDR76 expression was correlated with a poorer RFS prognosis in HNSC, READ, and TGCT (Fig. 2 ). 3.3 Methylation analysis : The methylation levels of the WDR76 gene may differ in association with certain cancers. We found significant methylation levels in several tumors, including BLCA, BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, and TGCT (Fig. 3 ). Among them, the promoter β-value of methylation of WDR76 was higher in tumor tissues than in normal tissues in BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, and PRAD. In the cases of BLCA and TGCT, the promoter methylation of WDR76 was observed to be lower in tumor tissues compared to normal tissues. 3.4 Genetic alteration analysis : Genetic alteration of WDR76 across pan-cancer was evaluated using the cBioPortal database. The results revealed that 148 out of 10,953 patients (1%) displayed genetic alterations in the WDR76 gene (Fig. 4 A). The highest frequency of WDR76 alteration presented in DLBC (6.25%), MESO (5.75%), and UCEC (4.91%) (Fig. 4 A). Deep deletion and point mutation were the top two types of genetic alteration of WDR76 in cancers. Additionally, we identified a total of 84 mutation sites within the amino acid range of 1 to 626. This total includes 67 missense mutations, 10 truncating mutations, 4 splice site mutations, and 3 fusion mutations. A174T was the most frequent missense mutation site (Fig. 4 F). Furthermore, we explored the association between WDR76 genetic alterations and clinical outcomes in cancer patients. Surprisingly, we found WDR76 gene alteration was associated with PFS (p = 0.0397), OS (p = 0.0263), and DFS (p = 1.159e-3) in DLBC and PFS (p = 9.625e-5) in PRAD, respectively (Fig. 4 B-E). 3.5 Correlation between WDR76 expression and CAF infiltration : The correlation heatmap in Fig. 5 A demonstrated that the estimated level of CAF infiltration is positively correlated with WDR76 expression in ESCA, HNSC-HPV-, KIRC, KIRP, LGG, and PAAD. However, it was negatively correlated with HNSC-HPV + and THYM (Fig. 5 A). The survival analysis demonstrated a significant association between elevated levels of CAF infiltration and poor prognosis in KIRC (HR = 1.17, p = 0.025), KIRP (HR = 1.45, p = 0.0149), and LGG (HR = 1.18, p = 0.0585) (Fig. 5 J-L). 3.6 Correlation between tumor immune microenvironment and WDR76 in pan-cancer : Immune cells and stromal cells are important components of the tumor immune microenvironment (TME); they play crucial roles in the regulation of cancer development and progression. In this study, WDR76 was positively correlated with KIRC, PRAD, PAAD and negatively correlated with BRCA, CESC, ESCA, GBM, LUAD, PCPG, SARC, SKCM, and UCEC (Fig. 6 A). In immune cells, WDR76 was positively correlated with Macrophages M1, T cells CD4 memory resting, T cells CD4 memory activated, and was negatively associated with Plasma cells, B cells memory, T cells regulatory Tregs, Monocytes, and NK cells activated in most tumors (Fig. 6 B). WDR76 was also positively associated with almost all immune checkpoint-associated genes in ACC, BRCA, BLCA, COAD, HNSC, SKCM, KICH, KIRP, KIRC, LUAD, LIHC, LGG, MESO, OV, PRAD, PAAD, READ, STAD, THCA, UCEC, and negatively associated in THYM (Fig. 6 E). WDR76 was positively correlated with almost all immune inhibitory or stimulatory genes in ACC, BLCA, COAD, HNSC, KIRP, KIRC, KICH, LGG, LIHC, LUAD, OV, PRAD, PAAD, READ, SKCM, THCA, STAD, whereas it was negatively correlated with GBM, THYM, and, SARC (Fig. 6 C-D). These collective data suggest that WDR76 expression exhibits a widespread correlation with immunity in cancers and may influence survival through interactions with immune infiltration. 3.7 Correlation of WDR76 with TMB and MSI : TMB quantifies the number of mutations in a tumor specimen, and MSI exhibits genomic instability caused by a defective DNA mismatch repair system (MMR) ( 24 , 25 ). The radar plot displayed a significant positive correlation between WDR76 expression and TMB in 13 cancer types, including BLCA, COAD, DLBC, KICH, KIRC, LGG, LUAD, PCPG, PRAD, READ, SKCM, UCEC, and STAD (*p < 0.05, **p < 0.01) in Fig. 7 C. No significant negative correlation between WDR76 and TMB was found in any cancer. WDR76 expression exhibited a significant positive correlation with MSI in COAD, KIRC, SARC, STAD, and UCEC, and a significant negative correlation with MSI in DLBC and THCA (*p < 0.05, **p < 0.01) in Fig. 7 D. WDR76 demonstrated a significant positive correlation with both TMB and MSI in UCEC, COAD, KIRC, and STAD. 3.8 Drug sensitivity analysis : We investigated the drug sensitivity of WDR76 expression in tumors using the GSCA portal. The expression of WDR76 was negatively correlated with 26 drugs including BX-912 (PDK1inhibitors), FK866 (NAMPT inhibitors), Methotrexate (DHFR inhibitors), Navitoclax (BCL-2 family inhibitors), NPK76-11-72-1 (PLK3 inhibitors), PIK-93 (Pl3K inhibitors), Vorinostat (HDAC inhibitors), GSK1070916, WZ3105, and XMD13-2 which are kinase inhibitors through the GDSC database (Fig. 7 A). Conversely, drugs such as PD-0325901, RDEA119, Selumetinib, Trametinib, which are MEK inhibitors, showed a strong positive correlation with WDR76 expression (Fig. 7 A). Additionally, we integrated WDR76 expression data from the CTRP database to analyze drug sensitivity. Through Spearman’s correlation analysis, WDR76 expression was negatively correlated with all 30 drugs, including BI-2536, GSK461364 are PLK inhibitors; Clofarabine, Cytarabine hydrochloride, gemcitabine are DNA synthesis inhibitors; etoposide, topotecan, teniposide, isoevodiamine are topoisomerase inhibitors; KW-2449, KX2-391, tivantinib are kinase inhibitors; parbendazole, SB-743921, nakiterpiosin are tubulin polymerization inhibitors; vincristine, docetaxel are microtubule inhibitors (Fig. 7 B). 3.9 PPI Network and Intersection Analysis : PPI (protein-protein interaction) network analysis revealed 50 interacting genes (Fig. 7 E). We also retrieved 100 co-expressed genes from the “similar genes detection” module of GEPIA2 (Supplementary data). The Venn diagram in Fig. 8 A shows five common genes among the co-expressed and interacted genes: FEN1, KIF11, CHAF1B, ATAD2, and MCM4. The scatter diagram and correlation heatmap indicated a positive correlation between the expression of WDR76 and five common co-expressed genes (Fig. 8 B, C). 3.10 Enrichment Analysis : The Gene Ontology Biological Process (BP) revealed that the WDR76 correlated genes were involved in the chromosome segregation, DNA replication, nuclear division, organelle fission, nuclear chromosome segregation, regulation of cell cycle phase transition (Fig. 9 A). Gene Ontology Cellular Component (CC) functional enrichment analysis showed that the related genes of WDR76 were significantly associated with chromosomal region, spindle, nuclear chromosome, condensed chromosome, microtubule (Fig. 9 B). The Molecular Function (MF) Gene Ontology provided the most significant involvement of WDR76-related genes in ATP hydrolysis activity, catalytic activity acting on DNA, tubulin binding, microtubule binding, damaged DNA binding, and single-stranded DNA binding (Fig. 9 C). Reactome, Wikipathways, and KEGG analyses were performed to determine the enriched pathways of WDR76 and its co-expressed genes. Reactome enrichment analysis revealed that WDR76 participated in the following cell cycle checkpoints, DNA repair, M phase, DNA replication, G1/S Transition (Fig. 9 D). Wikipathways revealed that WDR76 was associated with several pathways, including retinoblastoma gene in cancer, DNA repair pathways full network, DNA replication, nucleotide excision repair in xeroderma pigmentosum, DNA IR damage and cellular response via ATR, and G1 to S cell cycle control (Fig. 9 E). Finally, the cell cycle pathway, DNA replication, mismatch repair, and p53 signaling pathway were significantly enriched in WDR76 according to KEGG pathway enrichment analysis (Fig. 9 F). 4 Discussion WDR76 is a member of the WD repeat (WDR) domain, which is involved in DNA damage repair, apoptosis, cell-cycle progression, and the regulation of gene expression ( 5 ). This study investigated the expression profiles of WDR76 and revealed elevated expression across the majority of tumor types. However, significantly lower expression was also found in some cases, especially in KICH and PRAD. The proteomic analysis showed higher protein expression in a subset of tumors exhibiting elevated mRNA expression. Both transcriptomic and proteomic analyses corroborated these findings. These findings suggest that WDR76 is an upregulated gene. The association between overexpression and clinical outcomes was evaluated using overall survival (OS) and relapse-free survival (RFS) using KMplotter. Our analysis demonstrated that increased WDR76 expression was associated with poor clinical outcomes in BLCA, ESCA, KIRC, LIHC, LUAD, and SARC. In patients with LIHC and SARC, both OS and RFS showed worse prognoses, along with higher expression. Despite the poor survival outcomes, higher WDR76 expression cannot be attributed to causality, as expression levels are specific to tumor types and may reflect compensatory responses. Adjustments of WDR76 expression levels based on the cancer type may lead to increased survival rates( 36 ). DNA methylation serves as a fundamental epigenetic mechanism for interpreting disease-associated alterations, particularly in cancer. It provides a stable, yet dynamic means of regulating gene function across both normal and malignant cellular states ( 37 ). Together with histone modification, DNA methylation orchestrates the regulation of gene expression and maintenance of high order chromatin structure (38). In our analysis of DNA methylation profiling, we observed that the WDR76 gene was predominantly hypermethylated across the analyzed samples. Hypermethylation of gene promoters is a well-established mechanism of transcriptional silencing, particularly for suppressor genes. This silencing may contribute to cancer development by inactivating protective genes involved in DNA repair and apoptosis regulation ( 39 ). The hypermethylation of WDR76 could thus play a role in tumorigenesis by impairing these crucial cellular processes, providing a growth advantage to cancer cells ( 40 ). Subsequent to these findings, it was also suggested that the activity of WDR76 may influence the occurrence and progression of cancer through DNA methylation, despite its tumor suppressor characteristics. Genetic alteration is an important influence on tumorigenesis and also plays an important role in WDR76. Tumor-infiltrating CAFs are associated with poor prognosis, resistance to treatment, and recurrence of cancer ( 41 ), and may serve as a potential biomarker of immunotherapy responsiveness. Cancer-associated fibroblasts (CAFs) comprise the main stromal element of cancers, and have both tumor-promoting and tumor-suppressive roles ( 42 ). The elevated CAF infiltration scores are found to be associated with poor prognosis in patients with KIRC, KIRP, and LGG. Therefore, WDR76 may serve as an indicator of shortened survival and invasive development of these tumor types. We speculated that WDR76 may play a role in influencing the CAF population in the tumor microenvironment and thus affecting the prognosis of KIRC, KIRP, and LGG. Supporting our observations, Cheng et al. (2024) also identified WDR76 as an independent prognostic risk factor of LGG ( 43 ). However, it must be considered that CAF estimations are computational surrogates and vary by algorithm. Tumor microenvironment (TME) ecosystems are characterized by the interactions between cancer and nonmalignant cells. It is composed of cancer associated fibroblasts (CAFs), tumor associated macrophages (TAMs), T cells, NK cells, B cells, endothelial cells, and other cell types that play critical roles in tumor proliferation, invasion, and drug resistance ( 44 , 45 ). In this study, WDR76 was positively associated with immune scores in 3 types of cancers, and negatively linked to immune scores in 9 types of cancer. Furthermore, WDR76 was positively correlated with immune cells in most tumors, including Macrophages M1, T cells CD4 memory resting, T cells CD4 memory activated, and negatively associated with Plasma cells, B cells memory, T cells regulatory Tregs, Monocytes, and NK cells. Cytotoxic T cells (CD8 + T cells), which express the cell-surface marker CD8, are the most potent effectors in the anticancer immune response and form the foundation of today’s effective cancer immunotherapies ( 46 ). CD4 + T cells increase CD8 + T cells' antitumor efficacy. In cancer, monocytes, macrophages, and neutrophils exhibit both pro- and antitumor functions ( 44 ). Under normal conditions, TReg cells prevent autoimmunity; however, during tumor development and progression, TReg cells suppress immunity, inhibit antitumor immunity, promote tumor growth, facilitate immune escape, and limit the beneficial responses of immunotherapy ( 45 ). Together, these results indicate that WDR76 expression is associated with immune cell composition within the tumor microenvironment. Importantly, these findings are observational and do not establish predictive value for immunotherapy response. Future validation in immune checkpoint inhibitor (ICI)-treated patient cohorts will be essential to determine whether WDR76 has predictive significance in the context of immunotherapy. Normally, immune checkpoints prevent the body from reacting to healthy cells. Some cancers acquire these checkpoints to allow tumor cells to escape immune system surveillance. The suppressive effects of tumor cells on T-cells are inhibited by immune checkpoint inhibitors. Immune checkpoint inhibition restores immune-mediated antitumor activity ( 47 ). In this study, we found that WDR76 was positively associated with immune checkpoint genes in 20 types of cancer. The high expression of immune checkpoint genes in different tumors may indicate that the tumor is using these molecules to suppress immune system activity and thus allowing it to escape immune surveillance. However, immune checkpoint inhibitors can effectively treat various cancers by blocking their evasion pathways. Moreover, WDR76 was correlated with almost all immune inhibitory or stimulating genes in different cancers. TMB and MSI are emerging genomic biomarkers for predicting the response and efficacy of immunotherapy and identifying suitable candidates for immunotherapy ( 48 ). Tumor mutation burden (TMB) is the total number of mutations per million bases in the coding region of genes that encode specific tumor cell proteins. Microsatellite Instability (MSI) refers to the alterations in simple sequence repeats (SSRs) due to the defective DNA mismatch repair (MMR) system ( 25 ). Most tumors with high TMB and MSI status respond better to immunotherapy involving immune checkpoint inhibitors (ICIs), as speculated by Palmeri et al. (2022) ( 48 ). We found WDR76 expression to be positively associated with TMB and MSI in UCEC, COAD, KIRC, and STAD, indicating that these cancer types with high WDR76 expression might respond better to and benefit from immunotherapy. On the other hand, WDR76 is found to be negatively correlated with MSI in DLBC and THCA, which implies that WDR76 may have a role in maintaining their genomic integrity and SSR stability. MSI serves a dual role in immunotherapy by acting as a prognostic biomarker and a response predictive factor, but it varies based on cancer stage and treatment regimen ( 25 , 49 ). A recent study by Li et al. (2023) revealed that TMB is a reliable predictor of immunotherapy efficacy in LUAD ( 50 ). We observed a significant positive correlation between TMB and WDR76 expression in LUAD. Therefore, we speculate that WDR76 could be a potential biomarker for the efficiency of immunotherapy in LUAD. The correlations between WDR76, mRNA, and anticancer drug sensitivity were explored. Figure 7 A-B summarizes the top 30 drugs whose effects showed the most significant association with WDR76 expression in pan-cancer. The over-expression of WDR76 was positively correlated with the 4 drugs. High gene expression of WDR76 could reduce the drug sensitivity of these 4 drugs, indicating its potential role in drug resistance. However, the over-expression of WDR76 was negatively correlated with the 26 drugs including BX-912, FK866, GSK1070916, Methotrexate, Navitoclax, NPK76-11-72-1, PlK-93, Vorinostat. Interestingly, high expression of WDR76 enhanced the sensitivity to these 26 drugs. The sensitivity to all 30 drugs presented in Fig. 7 B was inversely associated with WDR76 mRNA expression. These drugs had the potential to prevent the growth of malignancy ( 51 ). Many findings indicated that acute administration of Navitoclax is sufficient to significantly kill cancer cells ( 52 ). PlK-93 could assist in overcoming Programmed Death-Ligand 1 (PD-L1) induced immunosuppression, a vital mechanism tumors utilize to evade immune responses ( 53 ). Vorinostat (suberoylanilide hydroxamic acid) inhibits tumor growth and hematological malignancies, including Prostate cancer, leukemia, breast cancer, glioma, and lung cancer ( 54 ). These findings suggested that WDR76 may be a viable target for cancer treatment. GEPIA2 and STRING provided positively co-expressed genes enriched with several functions and pathways. The analysis revealed that WDR76 related genes had a significant association with DNA replication and cell cycle mechanisms. Studies have suggested that the activation of an oncogene causes aberrant CDK activity, which eventually causes double-strand breakage and genomic instability. These damages cause DNA damage response (DDR) that leads to apoptosis of cells, whereas oncogenes can bypass apoptosis due to mutations; consequently, tumorigenesis occurs ( 55 , 56 ). Because the expression pattern was higher in different cancers compared with normal tissues and the alteration status provided several mutational patterns in different cancers, the oncogenic potential of WDR76 could normally highlight. Cell cycle pathways are strictly regulated by various regulatory mechanisms in normal cells, such as cell cycle checkpoints, the p53 signaling pathway ( 57 , 58 ). The involvement in these functions and pathways indicated that WDR76 may be responsible for some oncogenic role in cancer proliferation. Based on our analysis, it can be said that WDR76 is a biologically complex gene that shows elevated expression in most tumors with the worst clinical outcomes. However, both the alteration and methylation patterns of the gene in tumor cells show deletion and hypermethylation. Furthermore, it is associated with the cell cycle, cell cycle checkpoints, DNA repair, excision repair, and the mismatch repair pathway. The following results reflect both oncogenic and suppressive gene characteristics, which might be possible when the gene plays a context-dependent oncogenic role as well as a suppressive effect. In their previous work, Lou et al. showed that some genes, such as CCND and CDK4/6, can exhibit both oncogenic and tumor suppressive roles; however, these were classified as oncogenes ( 59 ). Duality can arise in several ways, including gene alternative splicing, isoforms, mutations, PPI, different cell cycle phases, cell-cell interaction, evolutionary perspective etc ( 59 ). Therefore, WDR76 can act as both a tumor suppressor and, in some contexts, several factors can induce tumor proliferation. 5 Limitation This study has several significant limitations. First, the analysis is fundamentally reliant on publicly accessible datasets, primarily from The Cancer Genome Atlas (TCGA) consortium, accessed via platforms such as UALCAN and GEPIA2. While invaluable for pan-cancer analysis, these databases aggregate data that may vary in sample collection protocols, normalization strategies, and processing pipelines. This inherent heterogeneity can introduce systematic inconsistencies or batch effects, potentially confounding the interpretation of multi-omic results ( 60 ). Furthermore, the availability of corresponding normal tissue samples, particularly for rarer tumor types, was limited within some datasets (e.g., UALCAN). This scarcity can affect the statistical power and accuracy of differential gene expression, proteomic, and methylation analyses, which are critical for distinguishing cancer-specific alterations from normal physiological variation. Similarly, correlations derived from cell-line-based drug sensitivity analyses are hypothesis-generating ( 61 ). Such in vitro models, while useful, lack the complex interplay of the tumor microenvironment and host immune system, and therefore these findings do not directly imply or guarantee clinical efficacy in patients. Second, this investigation was conducted entirely using bioinformatics and in silico approaches. Consequently, the findings provide preliminary insights into WDR76's potential role in tumorigenesis rather than definitive functional proof. While our in silico analyses suggest WDR76 may function as a significant prognostic biomarker and a potential therapeutic target across various cancers, the lack of direct experimental validation is a primary limitation. Rigorous functional studies using in vitro (e.g., cancer cell lines) and in vivo (e.g., animal) models are crucial. Specific molecular assays—such as real-time quantitative PCR (RT-qPCR) to validate expression, protein-level analyses and mechanistic studies are required to confirm these computational predictions. These experimental steps are essential to fully elucidate WDR76's precise mechanistic role in cancer initiation, progression, and therapeutic response. Further dedicated research is necessary to explore the specific cancer-type-specific signaling pathways involved and to understand the context-dependent functional relevance of WDR76 in different tumor microenvironments. 6 Conclusion Our comprehensive pan-cancer analysis reveals that WDR76 is consistently overexpressed across various tumor types, with its expression levels and genetic alterations significantly correlated with clinical outcomes in several cancers. Survival analyses indicate that WDR76 may serve as a prognostic biomarker. Methylation profiling further demonstrates that WDR76 is hypermethylated in certain cancers, consistent with a context-dependent role; prior functional studies support tumor-suppressive activity via RAS degradation, while our pan-cancer associations vary by tumor type. Our findings indicate that WDR76 expression is associated with immune cell infiltration and may influence tumor-infiltrating immune cells. Additionally, there appears to be a connection between WDR76 expression and the immune microenvironment, potentially affecting immune cell infiltration into tumors. Notably, WDR76 shows correlations with TMB and MSI in cancers such as UCEC, COAD, KIRC, and STAD; however, these associations are exploratory and should be interpreted as hypothesis-generating rather than predictive of immunotherapy response. Furthermore, drug sensitivity analysis suggests that WDR76 expression may modulate responses to targeted therapies, highlighting its potential utility in the development of precision immunotherapies. This study employs comprehensive bioinformatics approaches, offering preliminary yet compelling evidence for the role of WDR76 in tumorigenesis. Nonetheless, several limitations must be acknowledged. However, further in vitro and in vivo experimental validation is necessary to confirm these findings and elucidate the precise molecular functions and therapeutic potential of WDR76 in cancer. Declarations Ethical approval In this study, no experiments were conducted; all analyses were performed using data that were readily available to the public. Thus, ethical evaluation and approval were not required for this study. Consent to participate Not applicable. The study did not involve human participants or animals. Consent to publish Not applicable. This manuscript does not contain data or images from individual persons. Consent for publication The researchers affirm that no financial considerations or personal associations exist that could potentially compromise the objectivity or integrity of this investigation's publication. Funding The authors did not receive any funding for this study. Declaration of competing interests The authors declare that they have no conflicts of interest. Data availability This investigation examined publicly accessible datasets. The data can be located as specified below, The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/), GEPIA2 (http://gepia2.cancer-pku.cn/#index), TIMER2.0 (http://timer.cistrome.org/), UALCAN (https://ualcan.path.uab.edu/), cBioPortal (https://www.cbioportal.org/), Kaplan Meier plotter (https://kmplot.com/analysis/), GSCA (Gene Set Cancer Analysis) database (https://guolab.wchscu.cn/GSCA/#/), STRING database (https://string-db.org/), Cytoscape (https://cytoscape.org/), clusterProfiler R (v4.4.2) package (https://github.com/YuLab-SMU/clusterProfiler), ReactomePA R (v4.4.2) package (https://github.com/YuLab-SMU/ReactomePA) and TCGAplot R package (v8.0.0) (https://github.com/tjhwangxiong/TCGAplot). Author contributions Md Mohtasim Billah : Conceptualization, data curation, methodology, software, formal analysis and result interpretation, writing – original draft, writing-review and editing. Khadiza Mabsurah : Methodology, formal analysis and result interpretation, software, writing – original draft, writing - review and editing. Kaushik Ahammad, Israt Jahan Yeana, and Mosammad Sumaiya contributed equally in this article: Methodology, formal analysis and result interpretation, writing – original draft, writing - review and editing. Tarekul Islam : Formal analysis, writing – original draft, writing - review. Anushka Bhattacharjee : Formal analysis, writing – original draft, writing-review. Jannatul Ferdous : Formal analysis, writing – original draft. Md Jubayer Hossain : Conceptualization, writing - review and editing. Acknowledgement We would like to express our sincere gratitude to Dr. Syeda Tasneem Towhid for her invaluable guidance, support, and expertise in CHIRAL Bangladesh. We also extend our appreciation to CHIRAL Bangladesh for their assistance specially Mr. Md. Zabir Ahmed in facilitating various aspects of this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7837284","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556720736,"identity":"9871ab01-54a8-4c4a-9c5c-029962d44011","order_by":0,"name":"Md Mohtasim Billah","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Mohtasim","lastName":"Billah","suffix":""},{"id":556720737,"identity":"cc6d170a-1ef1-4163-b285-d306b6790ed8","order_by":1,"name":"Khadiza Mabsurah","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Khadiza","middleName":"","lastName":"Mabsurah","suffix":""},{"id":556720743,"identity":"83c85471-5e72-4c16-933a-9d8c71372583","order_by":2,"name":"Kaushik Ahammad","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Kaushik","middleName":"","lastName":"Ahammad","suffix":""},{"id":556720749,"identity":"d98975bc-2f01-46bc-8d74-d7cbfd328d12","order_by":3,"name":"Israt Jahan Yeana","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Israt","middleName":"Jahan","lastName":"Yeana","suffix":""},{"id":556720751,"identity":"b6de3769-f4d0-4108-a0ee-30499c26d329","order_by":4,"name":"Mosammad Sumaiya","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Mosammad","middleName":"","lastName":"Sumaiya","suffix":""},{"id":556720753,"identity":"28f6f935-be44-422d-b5b2-82022ba221c7","order_by":5,"name":"Tarekul Islam","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Tarekul","middleName":"","lastName":"Islam","suffix":""},{"id":556720755,"identity":"a8893831-8cd2-45a2-aae1-67e320a7726f","order_by":6,"name":"Anushka Bhattacharjee","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Anushka","middleName":"","lastName":"Bhattacharjee","suffix":""},{"id":556720756,"identity":"c1051ff0-ad18-4139-ae3f-a2a3676041f9","order_by":7,"name":"Jannatul Ferdous","email":"","orcid":"","institution":"Center for Health Innovation, Action, and Learning–Bangladesh (CHIRAL Bangladesh)","correspondingAuthor":false,"prefix":"","firstName":"Jannatul","middleName":"","lastName":"Ferdous","suffix":""},{"id":556720757,"identity":"708417f1-2c18-4c0f-8a08-982f8f43957f","order_by":8,"name":"Md. 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13:28:26","extension":"html","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161412,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/37f8a0b8366a25bf8e89d95a.html"},{"id":97709416,"identity":"27509377-0535-4b25-8b40-50a7b485d1c9","added_by":"auto","created_at":"2025-12-08 13:28:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4510472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGene expression analysis of WDR76 in different tumors and pathological stages. (A) Pan-cancer view of WDR76 expression via TIMER2.0. (B) Pan-cancer view of WDR76 expression analyzed by the TCGAplot R package. (C) WDR76 expression level compared with pathological stages in ACC, COAD, KICH, KIRP, LUAD, SKCM, LIHC, OV, and TGCT.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure01ExpressionAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/3966806f5d874e230cf46355.jpg"},{"id":97895297,"identity":"914a0c09-0d65-4939-a946-0d02cabf0943","added_by":"auto","created_at":"2025-12-10 15:33:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1641404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSurvival analysis of WDR76 with high and low expression levels in different tumors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure02SurvivalAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/9e2f35e8461b3c3d86243792.jpg"},{"id":97895217,"identity":"4a55a0dc-0f89-402f-985f-57ea12de6514","added_by":"auto","created_at":"2025-12-10 15:33:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1360214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDNA methylation analysis of WDR76 in different tumors, including BLCA, BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUSC, PAAD, PRAD, LUAD, and TGCT.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure03MethylationAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/1d9d84e4384f65c6938e0749.jpg"},{"id":97895355,"identity":"599a0c55-58d4-4d37-abeb-16abae31a970","added_by":"auto","created_at":"2025-12-10 15:34:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1317729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGenetic alteration analysis of WDR76 via cBioPortal. (A) Cancer type summary of the WDR76 gene. (B-E) Disease specific, overall, and progression free survival curve in diffuse large B-cell lymphoma and prostate adenocarcinoma. (F) WDR76 mutation plot.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure04GeneticAlterationAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/76a6ddcdac3cfd9755efeba9.jpg"},{"id":97895735,"identity":"59bff282-60d8-4267-8ecd-c88b052d7231","added_by":"auto","created_at":"2025-12-10 15:34:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2566084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation analysis between WDR76 expression and cancer associated fibroblast (CAF) infiltration. (A) Heatmap investigates the correlation of CAF infiltration utilizing different algorithms. (B-I) Positive and negative correlation between cancer associated fibroblast and WDR76 expression in different cancers, including ESCA, HNSC-HPV-, HNSC-HPV+, KIRC, KIRP, LGG, PRAD, and THYM. (J-L) Immune cell infiltration associated survival curve in KIRC, KIRP, and LGG.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure05ImmuneInfiltrationAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/7b819479b761f395dda57bc6.jpg"},{"id":97709431,"identity":"a9ce995c-ba30-4f46-9a1f-af7b4a207185","added_by":"auto","created_at":"2025-12-08 13:28:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3545741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePan-cancer correlation with WDR76 and immune microenvironment. (A)Gene immune score heatmap. (B) Gene immune cell heatmap. (C)Gene immune inhibitor heatmap. (D) Gene immune stimulator heatmap. (E) Immune checkpoint heatmap. The heatmap (*p \u0026lt; 0.05, **p \u0026lt; 0.01) was generated utilizing the previously published TCGAplot R package.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure06TMEAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/ab6a123f10358cfcc57ddca6.jpg"},{"id":97895521,"identity":"36f4f3e6-88e9-4f24-80db-bfa00d07469b","added_by":"auto","created_at":"2025-12-10 15:34:23","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3946267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePan-cancer correlation analysis of WDR76 expression with (A) GDSC drug sensitivity, (B) CTRP drug sensitivity, (C) TMB, and (D) MSI. (E) WDR76 associated protein-protein interaction (PPI) network analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure07CorrelationAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/8cb7627add34d564012c1013.jpg"},{"id":97895115,"identity":"4f7e8d56-2d9d-4559-98f9-3e3775c61abd","added_by":"auto","created_at":"2025-12-10 15:33:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2819768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWDR76 related co-expressed gene analysis. (A) Venn diagram illustrating intersection analysis. (B) Scatter plot of five intersected genes (FEN1, KIF11, CHAF1B, ATAD2, and MCM4) generated using the ‘Correlation Analysis’ module of GEPIA2. (C) Correlation heatmap of five interacted genes (FEN1, KIF11, CHAF1B, ATAD2, and MCM4) depicting positive correlation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure08CoexpressionAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/1db42ae8e33344278397b2e7.jpg"},{"id":97709438,"identity":"dacf912c-9020-4183-ae30-ca39d8e4b44b","added_by":"auto","created_at":"2025-12-08 13:28:25","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1537568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWDR76 related gene enrichment analysis using clusterProfiler R packages. (A) Gene ontology biological process. (B) Gene ontology cellular component. (C) Gene ontology molecular function. (D) Reactome pathway enrichment analysis. (E) Wikipathways enrichment analysis. (F) KEGG pathway enrichment analysis.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure9EnrichmentAnalysis.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/706a0671b8cf82ad37612cf9.jpg"},{"id":102298711,"identity":"b763e1d4-08d0-4dd8-b44b-699ffcf1882f","added_by":"auto","created_at":"2026-02-10 10:58:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24255908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/4a84eddf-0d88-4775-930f-216cc3980ca0.pdf"},{"id":97895373,"identity":"cc45d8f3-21e4-4d48-b1ab-42ba04a0e977","added_by":"auto","created_at":"2025-12-10 15:34:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":504340,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/fa81160199c36ca7ad77ad96.docx"},{"id":97709419,"identity":"3ce2c45c-c0c8-499a-b930-20c6f2fd8226","added_by":"auto","created_at":"2025-12-08 13:28:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18294,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/bc7fd964c68f589ab32c908e.xlsx"},{"id":97893424,"identity":"38529e77-4e7f-4d70-ab32-30b7e9a6d1b6","added_by":"auto","created_at":"2025-12-10 15:30:24","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1268497,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/0e3365443e5911c2efc31bcb.jpg"},{"id":97895289,"identity":"2fb050e6-19eb-494c-aef9-285bd4d2f1a7","added_by":"auto","created_at":"2025-12-10 15:33:56","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1058439,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7837284/v1/dcd531d123e9ab304cf026bc.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics pan-cancer analysis reveals an immunological role and prognostic potential of WDR76","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCancer is a complex and heterogeneous disease characterized by the uncontrolled proliferation of abnormal cells that can invade and destroy healthy tissues (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It arises due to genetic mutations and alterations in the cellular processes that regulate growth and division (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). According to the International Agency for Research on Cancer (IARC), 9.7\u0026nbsp;million individuals lost their lives to cancer globally in 2022, an estimate based on data from 115 countries. The IARC also estimates that 20\u0026nbsp;million new cancer cases were diagnosed in 2022, and one in every nine men and one in every twelve women will die from the disease (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite remarkable advancements in our understanding of the molecular and cellular processes underlying cancer, it remains one of the most complicated and formidable challenges confronting the healthcare community (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe WDR76 gene, located on chromosome 15q15.3, is a putative member of the WD40-repeat-containing domain superfamily (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Although its significance is acknowledged, the precise functions of WDR76 in various diseases and cellular processes remain elusive. Recent investigations have begun to elucidate its roles, particularly in cancer biology, DNA damage response, and metabolic regulation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). WDR76 interacts with proteins involved in DNA repair and heterochromatin, suggesting its potential role in maintaining genomic stability and regulating protein quality under stress (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). WDR76 has been identified as a tumor suppressor, notably in liver and colorectal cancers, and mediates the degradation of RAS proteins. These RAS proteins (H, K, and NRAS) are small GTPases that are crucial for regulating pathophysiological processes, such as cell proliferation, transformation, and development. WDR76 functions as an E3 linker protein that promotes polyubiquitination-dependent degradation of RAS. This degradation process inhibits cancer cell growth, transformation, and invasion, indicating that WDR76 plays a vital role in controlling tumor formation through RAS destabilization (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). RAS mutations that lock RAS proteins in GTP-binding forms are prevalent in most human cancers (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). RAS overexpression can also contribute to malignancy in colorectal cancer (CRC), lung adenocarcinoma, and breast cancer (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Given the evidence that WDR76 degrades RAS and suppresses transformation in preclinical models, a pan-cancer survey is needed to clarify whether its expression and alteration patterns consistently align with tumor-suppressive activity or reveal cancer type-specific divergence. However, a comprehensive pan-cancer analysis of WDR76 is currently unavailable, and its role in human cancer development remains unclear.\u003c/p\u003e\u003cp\u003eThe study of pan-cancer tumorigenesis and progression has recently garnered increasing interest. The field of pan-cancer research is transitioning from fundamental research to clinical application (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). A pan-cancer analysis entails investigating clinical and genomic characteristics across a range of cancer types to generate hypotheses about gene expression patterns, immune interactions, and possible relevance to immunotherapy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This study aimed to examine the genetic changes and expression disruptions of WDR76 in various cancer types and assess its potential clinical significance. Utilizing advanced bioinformatics techniques and data from The Cancer Genome Atlas (TCGA) and Genotype -Tissue Expression (GTEx) databases, we conducted a comprehensive pan-cancer analysis of WDR76 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our investigation encompassed gene expression, genetic alterations, DNA methylation patterns, survival outcomes, immune features, and functional enrichment analyses to elucidate WDR76's role in human malignancies. By identifying the pan-cancer role of WDR76, we aimed to unravel its potential as a biomarker and therapeutic target.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cp\u003e\u003cstrong\u003e2.1 Sample information:\u0026nbsp;\u003c/strong\u003eMost of the original data for the systematic pan-cancer analysis of WDR76 were obtained from public databases by The Cancer Genome Atlas (TCGA). The 33 cancers of interest in this study and their full names, along with their abbreviations, are listed in Supplementary Material Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Gene expression analysis:\u0026nbsp;\u003c/strong\u003eExpression data (log2 TPM+1) of \u003cstrong\u003eWDR76\u003c/strong\u003e in 33 types of tumors and normal tissues were acquired from The Cancer Genome Atlas (TCGA) database. The differential expression of \u003cstrong\u003eWDR76\u003c/strong\u003e was analyzed using TIMER2.0, GEPIA2, and the TCGAplot R package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Proteomic expression analysis:\u003c/strong\u003e The proteomic expression of WDR76 was examined to evaluate mRNA expression at the protein level using the UALCAN portal, which provides protein expression analysis options using data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the International Cancer Proteogenome Consortium (ICPC) datasets\u0026nbsp;(17).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Survival analysis:\u0026nbsp;\u003c/strong\u003eFor the survival analysis of the WDR76 gene in multiple cancers, we used the Kaplan-Meier plotter tool based on Affymetrix microarray information from TCGA databases\u0026nbsp;(18). The prognostic value of WDR76 expression in 21 cancers was evaluated using overall survival (OS) and relapse-free survival (RFS). According to the p-value of WDR76 expression, the patients were divided into high- and low-expression groups (accessed March 01, 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Methylation analysis:\u0026nbsp;\u003c/strong\u003eUALCAN\u0026nbsp;is a web-based tool, designed to facilitate the analysis of publicly available cancer omics data. It can evaluate the epigenetic regulation of gene expression by promoter methylation based on the TCGA database\u0026nbsp;(19). The UALCAN \u0026ldquo;TCGA analysis\u0026rdquo; module was used to analyze the promoter methylation level of WDR76 in normal tissues and cancers. Methylation levels are reported as \u0026beta;-values, ranging from 0 (unmethylated) to 1 (fully methylated), representing the fraction of methylation at each CpG site.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Genetic alteration analysis:\u0026nbsp;\u003c/strong\u003eThe cBioPortal database is an open platform for analyzing cancer genomics data. Genetic alteration data of WDR76, including alteration frequency, mutation type, and mutated site, were available from the cBioPortal database. The \u0026ldquo;TCGA Pan Cancer Atlas Studies\u0026rdquo; dataset was selected to obtain the alteration frequency of WDR76. The \u0026ldquo;Cancer Types Summary\u0026rdquo; module was selected to observe the results of mutation, amplification, deep deletion, and multiple alterations across all TCGA tumors. Additionally, the \u0026ldquo;Mutation\u0026rdquo; module was used to identify WDR76 alterations. The \u0026ldquo;Survival\u0026rdquo; module under the \u0026ldquo;Comparison/Survival\u0026rdquo; section was used to identify the impact of gene mutations on the survival of cancer patients\u0026nbsp;(18,20,21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Correlation between WDR76 expression and CAF infiltration\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThis study evaluated the potential correlation between WDR76 expression and cancer-associated fibroblast (CAF) infiltration in all TCGA-documented tumors by selecting the \u0026lsquo;gene\u0026rsquo; module under the \u0026lsquo;immune\u0026rsquo; section of TIMER2.0, utilizing EPIC, MCPCOUNTER, and TIDE algorithms\u0026nbsp;(22). We generated a correlation heatmap to visualize the association between WDR76 gene expression and CAF infiltration in various cancer types. Subsequently, the scatter diagram of the correlation heatmap was added with purity adjustment in Fig. 5(B-I). We computed CAF estimates using EPIC and performed the Cox regression model based on these estimates. The clinical relevance of CAF infiltration was explored by generating Kaplan\u0026ndash;Meier plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Correlation between tumor immune microenvironment and WDR76 in pan-cancer:\u0026nbsp;\u003c/strong\u003eWe utilized the TCGAplot (v8.0.0) R package to investigate the association between WDR76 mRNA expression levels and various immune-related factors, including immune checkpoints, immune cells, immune scores, and immune regulatory genes (immunoinhibitory, immunostimulatory)\u0026nbsp;(23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Correlation of WDR76 with TMB and MSI:\u0026nbsp;\u003c/strong\u003eTumor Mutational Burden (TMB) and Microsatellite Instability (MSI) are significant predictive biomarkers utilized in immunotherapy\u0026nbsp;(24,25).\u0026nbsp;The TMB and MSI matrix was extracted from The Cancer Genome Atlas (TCGA), and the association between WDR76 expression and TMB and MSI was analyzed using Pearson correlation analysis.\u0026nbsp;A radar plot was generated to visualize the data using the TCGAplot R package (v8.0.0).\u0026nbsp;This plot enabled the comparative analysis of WDR76 expression with TMB and MSI across various cancer types, facilitating a clearer understanding of their potential interplay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Drug sensitivity analysis:\u0026nbsp;\u003c/strong\u003eThe GSCA (Gene Set Cancer Analysis) platform was used to evaluate cell line pharmacogenomic correlations by integrating gene expression profiles with drug response data. \u0026nbsp;The analysis incorporated data from 33 cancer types in TCGA and over 750 small-molecule compounds from the GDSC (Genomics of Drug Sensitivity in Cancer) and the CTRP (Cancer Therapeutic Response Portal) databases. Correlation between gene sets and drug sensitivity was assessed using Spearman correlation, and significance was adjusted by false discovery rate (FDR) correction. For cases where multiple drugs targeted the same molecule, results were aggregated per target to improve robustness (26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 PPI Network Analysis:\u003c/strong\u003e The PPI (protein-protein interaction) network analysis was performed using the STRING database\u0026nbsp;(27). To improve the reliability of the analysis, we set custom parameters, including, minimum interaction score: \u0026ldquo;low confidence (0.150)\u0026rdquo;; max number of interactions: \u0026ldquo;no more than 50 interactors\u0026rdquo;, to obtain relatively all possible protein-protein interactions of WDR76. We exported the results as a tabular text file in TSV format and subsequently imported it into Cytoscape (version 3.10.3) to construct the interaction map\u0026nbsp;(28).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Intersection Analysis:\u003c/strong\u003e We used the GEPIA2 database to obtain positively co-expressed genes using the \u0026ldquo;Similar Gene Detection\u0026rdquo; module. An intersection analysis was performed to identify the common genes between co-expressed and interacted genes obtained from GEPIA2 and STRING database using the \u0026ldquo;VennDiagram\u0026rdquo; R (v 4.4.2) package\u0026nbsp;(29).\u0026nbsp;The \u0026ldquo;Correlation Analysis\u0026rdquo;\u0026nbsp;module of GEPIA2 and the \u0026ldquo;Gene Corr\u0026rdquo; module\u0026nbsp;of TIMER2.0 were utilized to generate the scatter diagram and correlation heatmap of the intersected genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 Enrichment Analysis:\u003c/strong\u003e The BP (biological process), CC (cellular component), and MF (molecular function) of Gene Ontology functional enrichment analysis was performed by the \u0026ldquo;clusterProfiler\u0026rdquo; R (v 4.4.2) package using the combined gene list of STRING and GEPIA2 \u0026nbsp;(30)\u0026nbsp;(31). \u0026ldquo;Wikipathways\u0026rdquo;, \u0026ldquo;Reactome\u0026rdquo;, and \u0026ldquo;Kyoto Encyclopedia of Genes and Genomes (KEGG)\u0026rdquo; were assessed to perform pathway enrichment analysis using \u0026ldquo;clusterProfiler\u0026rdquo; and \u0026ldquo;ReactomePA\u0026rdquo; R (v 4.4.2) package (30,32\u0026ndash;35). In addition, we obtained permission from KEGG to use KEGG and KEGG-related figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.14 Statistical Analysis:\u0026nbsp;\u003c/strong\u003eThe ANOVA methods in GEPIA2 and the Wilcoxon test in TIMER2 were used to assess the statistical significance of differential expression. Gene expression differences between the pathogenic stages in GEPIA2 were determined using a one-way ANOVA. KM plotter employed Cox proportional hazards regression for survival analysis. The TCGAplot R package\u0026apos;s Pearson correlation analysis was used to examine the relationship between WDR76 expression and TMB, and MSI. The Spearman correlation test in TIMER2 was used to determine the p values and partial correlations for the immune cell infiltration analysis. A statistically significant difference was defined as \u003cem\u003ep \u0026lt; 0.05.\u003c/em\u003e Almost all analyses were further checked as of October 6, 2025.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cb\u003e3.1 Expression analysis\u003c/b\u003e: We initially used TIMER2.0 to analyze the expression of WDR76. The expression of WDR76 was significantly higher in tumor cells than in the corresponding normal tissues, including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PCPG, STAD, and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, lower expression levels than the corresponding normal tissue were observed in KICH, KIRP, and PRAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The TCGAplot R package was utilized to validate the differential expression results, which generates the pan-cancer box plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB showed that significant higher expression in sixteen tumors including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, LIHC, LUAD, LUSC, PCPG, SARC, STAD, and UCEC, whereas decreased mRNA expression in tumors compared with corresponding normal tissue found in KICH and PRAD. Expression analysis using the GEPIA2 boxplot demonstrated significantly higher expression of WDR76 in BRCA, CESC, CHOL, DLBC, ESCA, GBM, LGG, LIHC, LUSC, PAAD, PCPG, SKCM, STAD, and THYM (Supplementary Fig.\u0026nbsp;1). Utilizing the \"stage plot\" module of GEPIA2, WDR76 expression across different cancer stages was found to be associated with ACC, COAD, KICH, KIRP, LUAD, SKCM, LIHC, OV, and TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, UALCAN demonstrated that the proteomic expression of WDR76 in BRCA, Clear cell RCC, GBM, LUSC, and UCEC was significantly elevated compared with the corresponding normal tissue (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 Survival analysis\u003c/b\u003e: To analyze the potential prognostic significance of WDR76 based on TCGA datasets, we investigated the correlation between WDR76 expression and the prognosis of patients with different tumors using KM Plotter. In this study, we found that higher WDR76 expression was associated with poor OS in cases of BLCA, ESCA, KIRC, KIRP, LIHC, LUAD, PAAD, and SARC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Meanwhile, low WDR76 expression was associated with a negative impact on OS in THYM, THCA, CESC, and READ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, increased WDR76 expression was correlated with unfavorable RFS outcomes in patients with KIRP, LIHC, PAAD, SARC, and THCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Alternatively, low WDR76 expression was correlated with a poorer RFS prognosis in HNSC, READ, and TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Methylation analysis\u003c/b\u003e: The methylation levels of the WDR76 gene may differ in association with certain cancers. We found significant methylation levels in several tumors, including BLCA, BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, and TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among them, the promoter β-value of methylation of WDR76 was higher in tumor tissues than in normal tissues in BRCA, COAD, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, and PRAD. In the cases of BLCA and TGCT, the promoter methylation of WDR76 was observed to be lower in tumor tissues compared to normal tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.4 Genetic alteration analysis\u003c/b\u003e: Genetic alteration of WDR76 across pan-cancer was evaluated using the cBioPortal database. The results revealed that 148 out of 10,953 patients (1%) displayed genetic alterations in the WDR76 gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The highest frequency of WDR76 alteration presented in DLBC (6.25%), MESO (5.75%), and UCEC (4.91%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Deep deletion and point mutation were the top two types of genetic alteration of WDR76 in cancers. Additionally, we identified a total of 84 mutation sites within the amino acid range of 1 to 626. This total includes 67 missense mutations, 10 truncating mutations, 4 splice site mutations, and 3 fusion mutations. A174T was the most frequent missense mutation site (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Furthermore, we explored the association between WDR76 genetic alterations and clinical outcomes in cancer patients. Surprisingly, we found WDR76 gene alteration was associated with PFS (p\u0026thinsp;=\u0026thinsp;0.0397), OS (p\u0026thinsp;=\u0026thinsp;0.0263), and DFS (p\u0026thinsp;=\u0026thinsp;1.159e-3) in DLBC and PFS (p\u0026thinsp;=\u0026thinsp;9.625e-5) in PRAD, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.5 Correlation between WDR76 expression and CAF infiltration\u003c/b\u003e: The correlation heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA demonstrated that the estimated level of CAF infiltration is positively correlated with WDR76 expression in ESCA, HNSC-HPV-, KIRC, KIRP, LGG, and PAAD. However, it was negatively correlated with HNSC-HPV\u0026thinsp;+\u0026thinsp;and THYM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The survival analysis demonstrated a significant association between elevated levels of CAF infiltration and poor prognosis in KIRC (HR\u0026thinsp;=\u0026thinsp;1.17, p\u0026thinsp;=\u0026thinsp;0.025), KIRP (HR\u0026thinsp;=\u0026thinsp;1.45, p\u0026thinsp;=\u0026thinsp;0.0149), and LGG (HR\u0026thinsp;=\u0026thinsp;1.18, p\u0026thinsp;=\u0026thinsp;0.0585) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ-L).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.6 Correlation between tumor immune microenvironment and WDR76 in pan-cancer\u003c/b\u003e: Immune cells and stromal cells are important components of the tumor immune microenvironment (TME); they play crucial roles in the regulation of cancer development and progression. In this study, WDR76 was positively correlated with KIRC, PRAD, PAAD and negatively correlated with BRCA, CESC, ESCA, GBM, LUAD, PCPG, SARC, SKCM, and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In immune cells, WDR76 was positively correlated with Macrophages M1, T cells CD4 memory resting, T cells CD4 memory activated, and was negatively associated with Plasma cells, B cells memory, T cells regulatory Tregs, Monocytes, and NK cells activated in most tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). WDR76 was also positively associated with almost all immune checkpoint-associated genes in ACC, BRCA, BLCA, COAD, HNSC, SKCM, KICH, KIRP, KIRC, LUAD, LIHC, LGG, MESO, OV, PRAD, PAAD, READ, STAD, THCA, UCEC, and negatively associated in THYM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). WDR76 was positively correlated with almost all immune inhibitory or stimulatory genes in ACC, BLCA, COAD, HNSC, KIRP, KIRC, KICH, LGG, LIHC, LUAD, OV, PRAD, PAAD, READ, SKCM, THCA, STAD, whereas it was negatively correlated with GBM, THYM, and, SARC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). These collective data suggest that WDR76 expression exhibits a widespread correlation with immunity in cancers and may influence survival through interactions with immune infiltration.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.7 Correlation of WDR76 with TMB and MSI\u003c/b\u003e: TMB quantifies the number of mutations in a tumor specimen, and MSI exhibits genomic instability caused by a defective DNA mismatch repair system (MMR) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The radar plot displayed a significant positive correlation between WDR76 expression and TMB in 13 cancer types, including BLCA, COAD, DLBC, KICH, KIRC, LGG, LUAD, PCPG, PRAD, READ, SKCM, UCEC, and STAD (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC. No significant negative correlation between WDR76 and TMB was found in any cancer. WDR76 expression exhibited a significant positive correlation with MSI in COAD, KIRC, SARC, STAD, and UCEC, and a significant negative correlation with MSI in DLBC and THCA (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD. WDR76 demonstrated a significant positive correlation with both TMB and MSI in UCEC, COAD, KIRC, and STAD.\u003c/p\u003e\u003cb\u003e3.8 Drug sensitivity analysis\u003c/b\u003e: We investigated the drug sensitivity of WDR76 expression in tumors using the GSCA portal. The expression of WDR76 was negatively correlated with 26 drugs including BX-912 (PDK1inhibitors), FK866 (NAMPT inhibitors), Methotrexate (DHFR inhibitors), Navitoclax (BCL-2 family inhibitors), NPK76-11-72-1 (PLK3 inhibitors), PIK-93 (Pl3K inhibitors), Vorinostat (HDAC inhibitors), GSK1070916, WZ3105, and XMD13-2 which are kinase inhibitors through the GDSC database (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Conversely, drugs such as PD-0325901, RDEA119, Selumetinib, Trametinib, which are MEK inhibitors, showed a strong positive correlation with WDR76 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Additionally, we integrated WDR76 expression data from the CTRP database to analyze drug sensitivity. Through Spearman\u0026rsquo;s correlation analysis, WDR76 expression was negatively correlated with all 30 drugs, including BI-2536, GSK461364 are PLK inhibitors; Clofarabine, Cytarabine hydrochloride, gemcitabine are DNA synthesis inhibitors; etoposide, topotecan, teniposide, isoevodiamine are topoisomerase inhibitors; KW-2449, KX2-391, tivantinib are kinase inhibitors; parbendazole, SB-743921, nakiterpiosin are tubulin polymerization inhibitors; vincristine, docetaxel are microtubule inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.9 PPI Network and Intersection Analysis\u003c/b\u003e: PPI (protein-protein interaction) network analysis revealed 50 interacting genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). We also retrieved 100 co-expressed genes from the \u0026ldquo;similar genes detection\u0026rdquo; module of GEPIA2 (Supplementary data). The Venn diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA shows five common genes among the co-expressed and interacted genes: FEN1, KIF11, CHAF1B, ATAD2, and MCM4. The scatter diagram and correlation heatmap indicated a positive correlation between the expression of WDR76 and five common co-expressed genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.10 Enrichment Analysis\u003c/b\u003e: The Gene Ontology Biological Process (BP) revealed that the WDR76 correlated genes were involved in the chromosome segregation, DNA replication, nuclear division, organelle fission, nuclear chromosome segregation, regulation of cell cycle phase transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Gene Ontology Cellular Component (CC) functional enrichment analysis showed that the related genes of WDR76 were significantly associated with chromosomal region, spindle, nuclear chromosome, condensed chromosome, microtubule (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). The Molecular Function (MF) Gene Ontology provided the most significant involvement of WDR76-related genes in ATP hydrolysis activity, catalytic activity acting on DNA, tubulin binding, microtubule binding, damaged DNA binding, and single-stranded DNA binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Reactome, Wikipathways, and KEGG analyses were performed to determine the enriched pathways of WDR76 and its co-expressed genes. Reactome enrichment analysis revealed that WDR76 participated in the following cell cycle checkpoints, DNA repair, M phase, DNA replication, G1/S Transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Wikipathways revealed that WDR76 was associated with several pathways, including retinoblastoma gene in cancer, DNA repair pathways full network, DNA replication, nucleotide excision repair in xeroderma pigmentosum, DNA IR damage and cellular response via ATR, and G1 to S cell cycle control (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). Finally, the cell cycle pathway, DNA replication, mismatch repair, and p53 signaling pathway were significantly enriched in WDR76 according to KEGG pathway enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWDR76 is a member of the WD repeat (WDR) domain, which is involved in DNA damage repair, apoptosis, cell-cycle progression, and the regulation of gene expression (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This study investigated the expression profiles of WDR76 and revealed elevated expression across the majority of tumor types. However, significantly lower expression was also found in some cases, especially in KICH and PRAD. The proteomic analysis showed higher protein expression in a subset of tumors exhibiting elevated mRNA expression. Both transcriptomic and proteomic analyses corroborated these findings. These findings suggest that WDR76 is an upregulated gene. The association between overexpression and clinical outcomes was evaluated using overall survival (OS) and relapse-free survival (RFS) using KMplotter. Our analysis demonstrated that increased WDR76 expression was associated with poor clinical outcomes in BLCA, ESCA, KIRC, LIHC, LUAD, and SARC. In patients with LIHC and SARC, both OS and RFS showed worse prognoses, along with higher expression. Despite the poor survival outcomes, higher WDR76 expression cannot be attributed to causality, as expression levels are specific to tumor types and may reflect compensatory responses. Adjustments of WDR76 expression levels based on the cancer type may lead to increased survival rates(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDNA methylation serves as a fundamental epigenetic mechanism for interpreting disease-associated alterations, particularly in cancer. It provides a stable, yet dynamic means of regulating gene function across both normal and malignant cellular states (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Together with histone modification, DNA methylation orchestrates the regulation of gene expression and maintenance of high order chromatin structure (38). In our analysis of DNA methylation profiling, we observed that the WDR76 gene was predominantly hypermethylated across the analyzed samples. Hypermethylation of gene promoters is a well-established mechanism of transcriptional silencing, particularly for suppressor genes. This silencing may contribute to cancer development by inactivating protective genes involved in DNA repair and apoptosis regulation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The hypermethylation of WDR76 could thus play a role in tumorigenesis by impairing these crucial cellular processes, providing a growth advantage to cancer cells (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Subsequent to these findings, it was also suggested that the activity of WDR76 may influence the occurrence and progression of cancer through DNA methylation, despite its tumor suppressor characteristics.\u003c/p\u003e\u003cp\u003eGenetic alteration is an important influence on tumorigenesis and also plays an important role in WDR76. Tumor-infiltrating CAFs are associated with poor prognosis, resistance to treatment, and recurrence of cancer (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and may serve as a potential biomarker of immunotherapy responsiveness. Cancer-associated fibroblasts (CAFs) comprise the main stromal element of cancers, and have both tumor-promoting and tumor-suppressive roles (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The elevated CAF infiltration scores are found to be associated with poor prognosis in patients with KIRC, KIRP, and LGG. Therefore, WDR76 may serve as an indicator of shortened survival and invasive development of these tumor types. We speculated that WDR76 may play a role in influencing the CAF population in the tumor microenvironment and thus affecting the prognosis of KIRC, KIRP, and LGG. Supporting our observations, Cheng et al. (2024) also identified WDR76 as an independent prognostic risk factor of LGG (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). However, it must be considered that CAF estimations are computational surrogates and vary by algorithm.\u003c/p\u003e\u003cp\u003eTumor microenvironment (TME) ecosystems are characterized by the interactions between cancer and nonmalignant cells. It is composed of cancer associated fibroblasts (CAFs), tumor associated macrophages (TAMs), T cells, NK cells, B cells, endothelial cells, and other cell types that play critical roles in tumor proliferation, invasion, and drug resistance (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In this study, WDR76 was positively associated with immune scores in 3 types of cancers, and negatively linked to immune scores in 9 types of cancer. Furthermore, WDR76 was positively correlated with immune cells in most tumors, including Macrophages M1, T cells CD4 memory resting, T cells CD4 memory activated, and negatively associated with Plasma cells, B cells memory, T cells regulatory Tregs, Monocytes, and NK cells. Cytotoxic T cells (CD8\u0026thinsp;+\u0026thinsp;T cells), which express the cell-surface marker CD8, are the most potent effectors in the anticancer immune response and form the foundation of today\u0026rsquo;s effective cancer immunotherapies (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). CD4\u0026thinsp;+\u0026thinsp;T cells increase CD8\u0026thinsp;+\u0026thinsp;T cells' antitumor efficacy. In cancer, monocytes, macrophages, and neutrophils exhibit both pro- and antitumor functions (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Under normal conditions, TReg cells prevent autoimmunity; however, during tumor development and progression, TReg cells suppress immunity, inhibit antitumor immunity, promote tumor growth, facilitate immune escape, and limit the beneficial responses of immunotherapy (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Together, these results indicate that WDR76 expression is associated with immune cell composition within the tumor microenvironment. Importantly, these findings are observational and do not establish predictive value for immunotherapy response. Future validation in immune checkpoint inhibitor (ICI)-treated patient cohorts will be essential to determine whether WDR76 has predictive significance in the context of immunotherapy.\u003c/p\u003e\u003cp\u003eNormally, immune checkpoints prevent the body from reacting to healthy cells. Some cancers acquire these checkpoints to allow tumor cells to escape immune system surveillance. The suppressive effects of tumor cells on T-cells are inhibited by immune checkpoint inhibitors. Immune checkpoint inhibition restores immune-mediated antitumor activity (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In this study, we found that WDR76 was positively associated with immune checkpoint genes in 20 types of cancer. The high expression of immune checkpoint genes in different tumors may indicate that the tumor is using these molecules to suppress immune system activity and thus allowing it to escape immune surveillance. However, immune checkpoint inhibitors can effectively treat various cancers by blocking their evasion pathways. Moreover, WDR76 was correlated with almost all immune inhibitory or stimulating genes in different cancers.\u003c/p\u003e\u003cp\u003eTMB and MSI are emerging genomic biomarkers for predicting the response and efficacy of immunotherapy and identifying suitable candidates for immunotherapy (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Tumor mutation burden (TMB) is the total number of mutations per million bases in the coding region of genes that encode specific tumor cell proteins. Microsatellite Instability (MSI) refers to the alterations in simple sequence repeats (SSRs) due to the defective DNA mismatch repair (MMR) system (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Most tumors with high TMB and MSI status respond better to immunotherapy involving immune checkpoint inhibitors (ICIs), as speculated by Palmeri et al. (2022) (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). We found WDR76 expression to be positively associated with TMB and MSI in UCEC, COAD, KIRC, and STAD, indicating that these cancer types with high WDR76 expression might respond better to and benefit from immunotherapy. On the other hand, WDR76 is found to be negatively correlated with MSI in DLBC and THCA, which implies that WDR76 may have a role in maintaining their genomic integrity and SSR stability. MSI serves a dual role in immunotherapy by acting as a prognostic biomarker and a response predictive factor, but it varies based on cancer stage and treatment regimen (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). A recent study by Li et al. (2023) revealed that TMB is a reliable predictor of immunotherapy efficacy in LUAD (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). We observed a significant positive correlation between TMB and WDR76 expression in LUAD. Therefore, we speculate that WDR76 could be a potential biomarker for the efficiency of immunotherapy in LUAD.\u003c/p\u003e\u003cp\u003eThe correlations between WDR76, mRNA, and anticancer drug sensitivity were explored. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B summarizes the top 30 drugs whose effects showed the most significant association with WDR76 expression in pan-cancer. The over-expression of WDR76 was positively correlated with the 4 drugs. High gene expression of WDR76 could reduce the drug sensitivity of these 4 drugs, indicating its potential role in drug resistance. However, the over-expression of WDR76 was negatively correlated with the 26 drugs including BX-912, FK866, GSK1070916, Methotrexate, Navitoclax, NPK76-11-72-1, PlK-93, Vorinostat. Interestingly, high expression of WDR76 enhanced the sensitivity to these 26 drugs. The sensitivity to all 30 drugs presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB was inversely associated with WDR76 mRNA expression. These drugs had the potential to prevent the growth of malignancy (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Many findings indicated that acute administration of Navitoclax is sufficient to significantly kill cancer cells (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). PlK-93 could assist in overcoming Programmed Death-Ligand 1 (PD-L1) induced immunosuppression, a vital mechanism tumors utilize to evade immune responses (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Vorinostat (suberoylanilide hydroxamic acid) inhibits tumor growth and hematological malignancies, including Prostate cancer, leukemia, breast cancer, glioma, and lung cancer (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). These findings suggested that WDR76 may be a viable target for cancer treatment.\u003c/p\u003e\u003cp\u003eGEPIA2 and STRING provided positively co-expressed genes enriched with several functions and pathways. The analysis revealed that WDR76 related genes had a significant association with DNA replication and cell cycle mechanisms. Studies have suggested that the activation of an oncogene causes aberrant CDK activity, which eventually causes double-strand breakage and genomic instability. These damages cause DNA damage response (DDR) that leads to apoptosis of cells, whereas oncogenes can bypass apoptosis due to mutations; consequently, tumorigenesis occurs (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Because the expression pattern was higher in different cancers compared with normal tissues and the alteration status provided several mutational patterns in different cancers, the oncogenic potential of WDR76 could normally highlight. Cell cycle pathways are strictly regulated by various regulatory mechanisms in normal cells, such as cell cycle checkpoints, the p53 signaling pathway (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). The involvement in these functions and pathways indicated that WDR76 may be responsible for some oncogenic role in cancer proliferation.\u003c/p\u003e\u003cp\u003eBased on our analysis, it can be said that WDR76 is a biologically complex gene that shows elevated expression in most tumors with the worst clinical outcomes. However, both the alteration and methylation patterns of the gene in tumor cells show deletion and hypermethylation. Furthermore, it is associated with the cell cycle, cell cycle checkpoints, DNA repair, excision repair, and the mismatch repair pathway. The following results reflect both oncogenic and suppressive gene characteristics, which might be possible when the gene plays a context-dependent oncogenic role as well as a suppressive effect. In their previous work, Lou et al. showed that some genes, such as CCND and CDK4/6, can exhibit both oncogenic and tumor suppressive roles; however, these were classified as oncogenes (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Duality can arise in several ways, including gene alternative splicing, isoforms, mutations, PPI, different cell cycle phases, cell-cell interaction, evolutionary perspective etc (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Therefore, WDR76 can act as both a tumor suppressor and, in some contexts, several factors can induce tumor proliferation.\u003c/p\u003e"},{"header":"5 Limitation","content":"\u003cp\u003eThis study has several significant limitations. First, the analysis is fundamentally reliant on publicly accessible datasets, primarily from The Cancer Genome Atlas (TCGA) consortium, accessed via platforms such as UALCAN and GEPIA2. While invaluable for pan-cancer analysis, these databases aggregate data that may vary in sample collection protocols, normalization strategies, and processing pipelines. This inherent heterogeneity can introduce systematic inconsistencies or batch effects, potentially confounding the interpretation of multi-omic results (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Furthermore, the availability of corresponding normal tissue samples, particularly for rarer tumor types, was limited within some datasets (e.g., UALCAN). This scarcity can affect the statistical power and accuracy of differential gene expression, proteomic, and methylation analyses, which are critical for distinguishing cancer-specific alterations from normal physiological variation. Similarly, correlations derived from cell-line-based drug sensitivity analyses are hypothesis-generating (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Such \u003cem\u003ein vitro\u003c/em\u003e models, while useful, lack the complex interplay of the tumor microenvironment and host immune system, and therefore these findings do not directly imply or guarantee clinical efficacy in patients.\u003c/p\u003e\u003cp\u003eSecond, this investigation was conducted entirely using bioinformatics and \u003cem\u003ein silico\u003c/em\u003e approaches. Consequently, the findings provide preliminary insights into WDR76's potential role in tumorigenesis rather than definitive functional proof. While our \u003cem\u003ein silico\u003c/em\u003e analyses suggest WDR76 may function as a significant prognostic biomarker and a potential therapeutic target across various cancers, the lack of direct experimental validation is a primary limitation. Rigorous functional studies using \u003cem\u003ein vitro\u003c/em\u003e (e.g., cancer cell lines) and \u003cem\u003ein vivo\u003c/em\u003e (e.g., animal) models are crucial. Specific molecular assays\u0026mdash;such as real-time quantitative PCR (RT-qPCR) to validate expression, protein-level analyses and mechanistic studies are required to confirm these computational predictions. These experimental steps are essential to fully elucidate WDR76's precise mechanistic role in cancer initiation, progression, and therapeutic response. Further dedicated research is necessary to explore the specific cancer-type-specific signaling pathways involved and to understand the context-dependent functional relevance of WDR76 in different tumor microenvironments.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eOur comprehensive pan-cancer analysis reveals that WDR76 is consistently overexpressed across various tumor types, with its expression levels and genetic alterations significantly correlated with clinical outcomes in several cancers. Survival analyses indicate that WDR76 may serve as a prognostic biomarker. Methylation profiling further demonstrates that WDR76 is hypermethylated in certain cancers, consistent with a context-dependent role; prior functional studies support tumor-suppressive activity via RAS degradation, while our pan-cancer associations vary by tumor type. Our findings indicate that WDR76 expression is associated with immune cell infiltration and may influence tumor-infiltrating immune cells. Additionally, there appears to be a connection between WDR76 expression and the immune microenvironment, potentially affecting immune cell infiltration into tumors. Notably, WDR76 shows correlations with TMB and MSI in cancers such as UCEC, COAD, KIRC, and STAD; however, these associations are exploratory and should be interpreted as hypothesis-generating rather than predictive of immunotherapy response. Furthermore, drug sensitivity analysis suggests that WDR76 expression may modulate responses to targeted therapies, highlighting its potential utility in the development of precision immunotherapies. This study employs comprehensive bioinformatics approaches, offering preliminary yet compelling evidence for the role of WDR76 in tumorigenesis. Nonetheless, several limitations must be acknowledged. However, further in vitro and in vivo experimental validation is necessary to confirm these findings and elucidate the precise molecular functions and therapeutic potential of WDR76 in cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, no experiments were conducted; all analyses were performed using data that were readily available to the public. Thus, ethical evaluation and approval were not required for this study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The study did not involve human participants or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain data or images from individual persons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe researchers affirm that no financial considerations or personal associations exist that could potentially compromise the objectivity or integrity of this investigation's publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive any funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation examined publicly accessible datasets. The data can be located as specified below, The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/), GEPIA2 (http://gepia2.cancer-pku.cn/#index), TIMER2.0 (http://timer.cistrome.org/), UALCAN (https://ualcan.path.uab.edu/), cBioPortal (https://www.cbioportal.org/), Kaplan Meier plotter (https://kmplot.com/analysis/), GSCA (Gene Set Cancer Analysis) database (https://guolab.wchscu.cn/GSCA/#/), STRING database (https://string-db.org/), Cytoscape (https://cytoscape.org/), clusterProfiler R (v4.4.2) package (https://github.com/YuLab-SMU/clusterProfiler), ReactomePA R (v4.4.2) package (https://github.com/YuLab-SMU/ReactomePA) and TCGAplot R package (v8.0.0) (https://github.com/tjhwangxiong/TCGAplot).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMd Mohtasim Billah\u003c/strong\u003e: Conceptualization, data curation, methodology, software, formal analysis and result interpretation, writing – original draft, writing-review and editing. \u003cstrong\u003eKhadiza Mabsurah\u003c/strong\u003e: Methodology, formal analysis and result interpretation, software, writing – original draft, writing - review and editing. \u003cstrong\u003eKaushik Ahammad, Israt Jahan Yeana, and Mosammad Sumaiya\u003c/strong\u003e contributed equally in this article: Methodology, formal analysis and result interpretation, writing – original draft, writing - review and editing. \u003cstrong\u003eTarekul Islam\u003c/strong\u003e: Formal analysis, writing – original draft, writing - review. \u003cstrong\u003eAnushka Bhattacharjee\u003c/strong\u003e: Formal analysis, writing – original draft, writing-review. \u003cstrong\u003eJannatul Ferdous\u003c/strong\u003e: Formal analysis, writing – original draft. \u003cstrong\u003eMd Jubayer Hossain\u003c/strong\u003e: Conceptualization, writing - review and editing. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to Dr. Syeda Tasneem Towhid for her invaluable guidance, support, and expertise in CHIRAL Bangladesh. We also extend our appreciation to CHIRAL Bangladesh for their assistance specially Mr. Md. Zabir Ahmed in facilitating various aspects of this study. Their contributions were instrumental in ensuring the success of the present study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFriedman A. Cancer as Multifaceted Disease. Math Model Nat Phenom. 2012;7(1):3\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRecillas-Targa F. Cancer Epigenetics: An Overview. Arch Med Res. 2022;53(8):732\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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PLOS ONE. 2017;12(4):e0176763.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"WDR76, Pan-cancer analysis, cancer biology, Human Tumors","lastPublishedDoi":"10.21203/rs.3.rs-7837284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7837284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eWD repeat domain 76 (WDR76) significantly influences various metabolic and genomic processes, including RAS protein degradation, which plays a role in tumor cells. However, a systematic pan-cancer analysis of WDR76 has not been conducted. Therefore, this study aimed to identify the role of WDR76 in human tumors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study used publicly available databases and tools, including TCGA, UALCAN, GEPIA2, TIMER2.0, KMplotter, cBioPortal, STRING, Cytoscape, and TCGAplot, to investigate the potential roles of WDR76 in different types of tumors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWDR76 expression was higher in several tumor types; however, the prognostic associations varied by cancer and attenuated after covariate adjustment and FDR correction. Notably, promoter methylation of WDR76 was higher in tumors than in normal cells in multiple cancers. Deep deletions and point mutations were the most frequent alterations, with an overall frequency of approximately 1% in TCGA. Immune infiltration analysis using different algorithms revealed a correlation between CAF infiltration and different tumors, especially KIRC, KIRP, and LGG, with significant clinical outcomes. In the tumor immune microenvironment, WDR76 was positively correlated with different immune cells, stromal cells, immune checkpoint inhibitors, and stimulator-associated genes, suggesting a broad interaction with cancer immunity. The correlation between WDR76 and TMB and MSI was significant in UCEC, STAD, KIRC, and COAD. Functional and pathway enrichment analyses revealed an association between WDR76 and various cellular processes and functions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur analysis offers insights into WDR76\u0026rsquo;s context-dependent role, consistent with prior evidence of RAS degradation (tumor-suppressive), along with tumor-type-specific associations, prognostic significance, and immunological role across all tumors.\u003c/p\u003e","manuscriptTitle":"Multi-omics pan-cancer analysis reveals an immunological role and prognostic potential of WDR76","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 13:28:19","doi":"10.21203/rs.3.rs-7837284/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16864653-8ee4-4278-8c88-d9e40610be7b","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T11:12:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 13:28:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7837284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7837284","identity":"rs-7837284","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}