Abstract Kidney renal clear cell carcinoma (KIRC) is the most common subtype of kidney cancer, characterized by complex molecular alterations. The FAM3 gene family, comprising FAM3A, FAM3B, FAM3C, and FAM3D, has been implicated in various cancers, but their roles in KIRC are not well understood. This study investigated the expression, diagnostic potential, and functional significance of FAM3 family genes in KIRC. This study explores the expression and functional roles of FAM3 family genes in KIRC using in silico and in vitro experiments. We performed RT-qPCR analysis to assess the expression of FAM3A, FAM3B, FAM3C, and FAM3D in KIRC and normal cell lines, revealing significant upregulation of FAM3A and FAM3D and downregulation of FAM3B and FAM3C in cancerous cells. ROC analysis demonstrated that FAM3 genes possess high diagnostic potential. Further validation using TCGA, OncoDB, and Human Protein Atlas (HPA) databases confirmed these expression patterns and their association with cancer progression. Methylation analysis indicated hypomethylation of FAM3A and FAM3D and hypermethylation of FAM3B and FAM3C, correlating with differential gene expression. Survival analysis revealed that high FAM3A expression was linked to poor prognosis, while low FAM3C expression correlated with reduced survival. Functional assays demonstrated that knockdown of FAM3A in 786-O cells reduced proliferation, clonogenicity, and migration, underscoring its potential role in KIRC pathogenesis. Additionally, FAM3 genes exhibited significant correlations with immune cell infiltration, immune inhibitor genes, and drug resistance, suggesting their involvement in modulating the tumor microenvironment. The miRNA-mRNA network analysis identified hsa-mir-19b-3p as a key regulator of FAM3 genes, further implicating these genes in KIRC progression. This comprehensive analysis highlights the potential of FAM3 genes as biomarkers and therapeutic targets in KIRC. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-05658-x. Keywords: KIRC, FAM3 family genes, Prognosis, Diagnosis, Treatment Subject terms: Biotechnology, Computational biology and bioinformatics Introduction Kidney Renal Clear Cell Carcinoma (KIRC) is one of the most common types of kidney cancer, accounting for approximately 70–80% of all renal cell carcinoma cases^[36]1,[37]2. It is characterized by its origin in the proximal convoluted tubule of the nephron and its clear cell histology^[38]3,[39]4. KIRC is known for its aggressive nature, with a high potential for metastasis and resistance to conventional therapies^[40]5. In 2024, KIRC remains one of the highly reported kidney cancer around the world, accounting for approximately 70–80% of all kidney cancer cases^[41]6,[42]7. Its incidence has been rising globally, with an estimated 75,000 new cases reported annually. Key risk factors contributing to the development of KIRC include smoking, obesity, hypertension, and genetic predispositions, such as mutations in the VHL gene, which plays a crucial role in tumor suppression^[43]8,[44]9. Additionally, chronic kidney disease and long-term use of certain medications, like diuretics, have been linked to an increased risk of KIRC^[45]10. The disease often presents asymptomatically in early stages, leading to late-stage diagnoses and complex treatment challenges^[46]10,[47]11. Despite advancements in targeted therapies and immunotherapies, the prognosis for patients with advanced KIRC remains poor, with a 5-year survival rate of around 12% for metastatic cases^[48]12,[49]13. Therefore, there is a pressing need for a deeper understanding of the molecular mechanisms driving KIRC progression and for the identification of novel therapeutic targets. The FAM3 (Family with Sequence Similarity 3) gene family consists of four members: FAM3A, FAM3B, FAM3C, and FAM3D^[50]14. These genes are involved in a variety of cellular processes, including metabolism, apoptosis, and inflammation^[51]15. Recent studies have highlighted the potential roles of FAM3 family genes in cancer biology. For instance, FAM3A has been implicated in the regulation of glucose metabolism and cancer cell proliferation in pancreatic cancer^[52]16. FAM3B has been shown to induce apoptosis in pancreatic beta cells and is associated with tumor suppression in colorectal cancer^[53]17. FAM3C promotes epithelial-to-mesenchymal transition (EMT) and metastasis in breast cancer^[54]18. FAM3D has been reported to act as a chemoattractant for neutrophils and macrophages and may play a role in the tumor microenvironment^[55]19. The overexpression of FAM3A is associated with poor prognosis and increased tumor aggressiveness^[56]16. FAM3B, in colorectal cancer, has been shown to act as a tumor suppressor by inducing apoptosis and inhibiting cell proliferation^[57]20. Reduced expression of FAM3B correlates with advanced tumor stage and poor patient outcomes^[58]21. Elevated levels of FAM3C are linked to increased metastatic potential and reduced overall survival. FAM3D, on the other hand, is involved in modulating the immune response within the tumor microenvironment^[59]18,[60]22. Its role as a chemoattractant for immune cells suggests that it may influence tumor growth and progression through interactions with the immune system^[61]23–[62]25. Despite the growing body of evidence suggesting that genes from the FAM3 family are involved in various types of cancer, their clinical relevance in KIRC has not been thoroughly investigated. This study is designed to fill this gap by exploring the roles of FAM3 family genes in KIRC through a combination of computational and experimental approaches^[63]26,[64]27. Specifically, we will integrate in silico analyses with in vitro experiments to gain a comprehensive understanding of these genes. Our objectives include examining the expression patterns of FAM3A, FAM3B, FAM3C, and FAM3D in KIRC, assessing their prognostic significance, and evaluating their functional impacts on cancer progression. By doing so, we aim to uncover novel insights into the molecular mechanisms underlying KIRC and identify potential new targets for therapeutic intervention. Methodology Cell lines and culture conditions Twenty KIRC cell lines, including Caki-1 and Caki-2 were sourced from the American Type Culture Collection (ATCC) under catalog numbers HTB-46™ and HTB-47™, respectively. The 786-O cell line was also sourced from ATCC with the catalog number CRL-1932™, while A498 and ACHN were sourced under HTB-44™ and CRL-1611™, respectively. The KIJ265T cell line was available from the Japanese Collection of Research Bioresources (JCRB) with catalog number JCRB1355, and KTCTL-30, KTCL-26, and KTCL-140 were also obtained from JCRB under catalog numbers JCRB1118, JCRB1117, and JCRB1357, respectively. RCC10RGB and RCC7RGB were available through the German Collection of Microorganisms and Cell Cultures (DSMZ) under catalog numbers ACC 635 and ACC 640. Meanwhile, RCM-1 was obtained from JCRB under catalog number JCRB1227. The SK-RC series of cell lines, including SK-RC-29, SK-RC-52, SK-RC-54, SK-RC-59, and SK-RC-66, were available through the European Collection of Authenticated Cell Cultures (ECACC) with catalog numbers 86091724, 86111810, 86111912, 86112011, and 87021912, respectively. Finally, the UOK series, including UOK111, UOK117, and UOK268, were all available from ATCC under catalog numbers CRL-3399™, CRL-3410™, and CRL-3438™. For ten normal kidney cell lines, HK-2 was available from ATCC with the catalog number CRL-2190™, and the RPTEC/TERT1 and RPTEC/TERT2 lines were also sourced from ATCC with catalog numbers CRL-4031™ and CRL-4032™. The HREC cell line was available from ScienCell Research Laboratories under catalog number 3000, while HKT-1 was sourced from Lonza with catalog number CC-2550. The HK-2TERT cell line shared the same ATCC catalog number as HK-2 (CRL-2190™). Additionally, NRK-52E and NRK-49F were available from ATCC under catalog numbers CRL-1571™ and CRL-1570™, respectively. HPTC was obtained from Cell Biologics under catalog number C363. The KIRC cell lines were cultured in RPMI-1640 medium (Thermo Fisher Scientific, Catalog No. 11875093) supplemented with 10% fetal bovine serum (FBS, Thermo Fisher Scientific, Catalog No. 26140079) and 1% penicillin–streptomycin (Thermo Fisher Scientific, Catalog No. 15070063). The normal kidney cell lines were cultured in DMEM/F-12 medium (Thermo Fisher Scientific, Catalog No. 11320033) supplemented with 10% FBS and 1% penicillin–streptomycin. All cell cultures were maintained at 37 °C in a humidified atmosphere containing 5% CO[2]. RNA extraction Total RNA was extracted from the cultured cells using the PureLink™ RNA Mini Kit (Thermo Fisher Scientific, Catalog No. 12183018A) according to the manufacturer’s instructions. Briefly, cells were lysed using lysis buffer provided in the kit, and the lysates were homogenized through a spin column. After washing with the provided wash buffers, RNA was eluted in RNase-free water. The concentration and purity of the extracted RNA were determined using a NanoDrop™ 2000 spectrophotometer (Thermo Fisher Scientific, Catalog No. ND2000). RT-qPCR Reverse transcription of the extracted RNA into complementary DNA (cDNA) was performed using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Catalog No. 4368814). Quantitative PCR was carried out using TaqMan™ Gene Expression Assays (Thermo Fisher Scientific) specific for the FAM3 family genes (FAM3A: Assay ID Hs01088769_g1, FAM3B: Assay ID Hs00365209_m1, FAM3C: Assay ID Hs01036988_m1, FAM3D: Assay ID Hs00371132_m1). The PCR reactions were performed in a 96-well plate using the TaqMan™ Universal PCR Master Mix (Thermo Fisher Scientific, Catalog No. 4304437) on an Applied Biosystems™ QuantStudio™ 5 Real-Time PCR System (Thermo Fisher Scientific, Catalog No. A28139). Each 20 μL reaction contained 1 μL of cDNA, 1X TaqMan™ Universal PCR Master Mix, 1X TaqMan™ Gene Expression Assay, and RNase-free water. The relative expression levels of the target genes were calculated using the ΔΔCt method, which compares the expression levels of the target genes in experimental conditions to a control group, while accounting for variations in cDNA input using GAPDH as an internal reference. All experiments were performed in triplicate. Expression validation using TCGA cohorts The expression validation of FAM3 family genes in additional TCGA (The Cancer Genome Atlas) KIRC cohorts was performed using three online databases: UALCAN, GEPIA2, and the Human Protein Atlas (HPA). UALCAN analysis UALCAN ([65]http://ualcan.path.uab.edu) is a versatile and accessible online platform that provides comprehensive and interactive tools for analyzing cancer OMICS data^[66]28,[67]29. We used UALCAN to compare the expression levels of FAM3 family genes between KIRC tissues and normal kidney tissue samples. GEPIA2 analysis GEPIA2 ([68]http://gepia2.cancer-pku.cn) is a web server designed for the analysis of RNA sequencing expression data from TCGA projects^[69]30. It provides customizable functions such as tumor/normal differential expression analysis, profiling plotting, correlation analysis, and patient survival analysis. We used GEPIA2 to validate the differential expression of FAM3 family genes and assess their prognostic significance in KIRC. Human protein atlas (HPA) analysis The HPA database ([70]https://www.proteinatlas.org) provides information on the tissue and cell distribution of all human proteins^[71]31. We used HPA to validate the protein expression levels of FAM3 family genes in KIRC tissues. Promoter methylation analysis of FAM3 family genes The promoter methylation status of the FAM3 family genes was analyzed using the OncoDB and GSCA (Gene Set Cancer Analysis) databases. OncoDB analysis OncoDB ([72]https://oncodb.org) was used to retrieve methylation beta values for FAM3 family genes in KIRC^[73]32. The methylation levels were compared between KIRC tissues and normal kidney tissues. GSCA analysis GSCA ([74]http://bioinfo.life.hust.edu.cn/GSCA/#/) provided additional methylation data and analysis^[75]33. We evaluated the correlation between promoter methylation and gene expression levels of FAM3 genes in KIRC. Methylation levels in KIRC tissues versus normal tissues were compared, and visualized using the GSCA platform’s box plots and heatmaps. Genetic alteration analysis The genetic alteration landscape of FAM3 family genes in KIRC was analyzed using the cBioPortal database ([76]https://www.cbioportal.org)^[77]34,[78]35. Data on mutations, including frequency and types, were retrieved for FAM3 genes. Survival analysis of FAM3 family genes KM Plotter ([79]https://kmplot.com/analysis/) is an online tool that correlates gene expression with patient survival across various cancers^[80]36,[81]37. On the other hand, GENT2 ([82]http://gent2.appex.kr/gent2/) is a comprehensive database providing gene expression and survival data from multiple cancer studies, enabling meta-analyses^[83]38. In this study, both KM plotter and GENT2 were used to perform survival analysis of FAM3 genes in KIRC. For this purpose, survival graphs were generated for the high and low expression groups, and log-rank tests were performed to compare the survival differences between these groups. The log-rank test assesses whether there are significant differences in survival curves between two groups. The hazard ratio (HR) was calculated to evaluate the relative risk of death between the high and low expression groups. Cancer hallmark analysis CancerSEA ([84]http://biocc.hrbmu.edu.cn/CancerSEA/) is an interactive web database that focuses on the functional states of cancer cells^[85]39. It provides a comprehensive analysis of single-cell RNA sequencing data, identifying specific genes associated with various cancer hallmarks and functional states, such as proliferation, metastasis, and drug resistance. In the present work, this database was utilized to analyze correlation of FAM3 genes with cancer hallmarks and distinct other functional states in KIRC. MuTarget MuTarget ([86]http://mutarget.cansearch.ca/) is a database designed to identify mutations in genes that significantly alter gene expression^[87]40. It focuses on the impact of these genetic alterations on gene expression levels, providing insights into how specific mutations can influence cancer biology. In this work, MuTarget was utilized to analyze the effect of mutant genes on the expression of FAM3 family genes expression. TISIDB analysis The correlation between FAM3 family genes and immune inhibitors as well as immune subtypes of KIRC was assessed using the TISIDB database ([88]http://cis.hku.hk/TISIDB/)^[89]41. CancerSEA analysis The correlation of FAM3 family genes with different functional states of KIRC was performed using the CancerSEA database ([90]http://biocc.hrbmu.edu.cn/CancerSEA/)^[91]39. This platform provides insights into the functional states of cancer cells at the single-cell level. We analyzed the association of FAM3 gene expression with various functional states, including proliferation, apoptosis, and metastasis, among others. GSCA analysis The immunolytic activity and drug sensitivity of FAM3 family genes in KIRC were assessed using the GSCA database ([92]http://bioinfo.life.hust.edu.cn/GSCA/#/)^[93]33. The immunolytic activity analysis involved evaluating the correlation between FAM3 gene expression and immune cells. Drug sensitivity analysis was performed to determine the association between FAM3 gene expression and the sensitivity to various chemotherapeutic agents, providing insights into potential therapeutic targets. Gene enrichment analysis DAVID ([94]https://david.ncifcrf.gov/) is a comprehensive bioinformatics tool that facilitates the interpretation of large gene lists^[95]42. It provides functional annotation, enrichment analysis, and clustering, helping researchers understand the biological significance of their data. In the current work, DAVID was utilized to perform gene enrichment analysis of FAM3 family genes. miRNA-mRNA network analysis ENCORI ([96]http://starbase.sysu.edu.cn/) is a comprehensive database designed to explore RNA interactions and regulatory networks^[97]43. It integrates data on RNA-binding proteins, miRNA-target interactions, and various RNA-RNA interactions. This tool aids in understanding the complex roles of RNAs in gene regulation. In this work, DAVID was utilized to predict miRNA-mRNA network of the FAM3A family genes. Expression analysis of hsa-mir-19b-3p Expression analysis of hsa-mir-19b-3p across TCGA KIRC samples was conducted via UALCAN^[98]28. While in KIRC and Normal control cell lines, RT-qPCR was employed to analyze expression of hsa-mir-19b-5p following aftermentioned conditions. The TaqMan™ assays for hsa-mir-19b-5p (Assay ID 477962_mir) and the endogenous control, U6 snRNA, were used. The relative expression levels of hsa-mir-19b-5p were calculated using the comparative Ct method (ΔΔCt), with U6 snRNA serving as the internal control for normalization. All experiments were conducted in triplicate to ensure the reproducibility and accuracy of the results. FAM3A gene knockdown The FAM3A gene was knockdown in 786-O cells using siRNA. The Silencer™ Select Pre-Designed siRNA (Thermo Fisher Scientific, Catalog No. 4390824) targeting FAM3A (siFAM3A) was transfected into cells using Lipofectamine™ RNAiMAX Transfection Reagent (Thermo Fisher Scientific, Catalog No. 13778150) according to the manufacturer’s instructions. For protein extraction, RIPA buffer (Thermo Fisher Scientific, Catalog No. 89900) with a protease inhibitor cocktail (Thermo Fisher Scientific, Catalog No. 78430) was used. Protein concentration was measured with the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, Catalog No. 23225). Equal protein amounts were separated by SDS-PAGE and transferred to a PVDF membrane (Thermo Fisher Scientific, Catalog No. 88518). The membranes were blocked with 5% non-fat milk in TBST and incubated overnight at 4 °C with primary antibody against FAM3A (Thermo Fisher Scientific, Catalog No. PA6-50248) and GAPDH (loading control, Thermo Fisher Scientific, Catalog No. MA5-15738). Following washes, the membranes were probed with HRP-conjugated secondary antibodies (Thermo Fisher Scientific, Catalog No. 31460) and developed using SuperSignal™ West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific, Catalog No. 34580). Bands were visualized with the iBright™ FL1000 Imaging System (Thermo Fisher Scientific, Catalog No. A44115). Colony formation assay Transfected 786-O cells were plated in 6-well plates at a density of 500 cells per well and cultured for 10–14 days. Colonies were then fixed using 4% paraformaldehyde (Thermo Fisher Scientific, Catalog No. 28908) and stained with 0.5% crystal violet (Thermo Fisher Scientific, Catalog No. C0775) for 30 min. All experiments were performed in triplicate. Cell proliferation assay Cell proliferation was assessed using the CellTiter-Glo^® Luminescent Cell Viability Assay (Promega, Catalog No. G7572). Transfected 786-O cells were seeded in 96-well plates at a density of 2000 cells per well. Cell viability was measured at 0, 24, 48, and 72 h after transfection. The CellTiter-Glo^® reagent was added to each well, and the plates were incubated for 10 min. Luminescence was then quantified using a microplate reader (Thermo Fisher Scientific, Catalog No. 464203). All experiments were performed in triplicate. Wound healing assay Transfected 786-O cells were seeded in 6-well plates and grown to confluence. A uniform scratch was made across the cell monolayer using a 200 μL pipette tip. The cells were washed with PBS and incubated in serum-free RPMI-1640 medium. Images of the wound area were captured at 0, 24, and 48 h using an inverted microscope (Thermo Fisher Scientific, Catalog No. AMF1000). The wound width was measured using ImageJ software, and the percentage of wound closure was calculated. All experiments were performed in triplicate. Statistical analyses Statistical analyses were performed with GraphPad Prism version 9.0 (GraphPad Software, San Diego, CA, USA). An unpaired Student’s t-test was used to compare differences between two groups. Pearson’s correlation coefficient was applied to examine the relationship between gene expression and various parameters. Statistical significance was defined as a p*-value < 0.05. Results Expression analysis of FAM3 family genes in KIRC cell lines We analyzed the expression patterns of the FAM3 gene family in 20 KIRC and 10 normal control cell lines using RT-qPCR. The results demonstrated that FAM3A and FAM3D were significantly (p-value < 0.05) upregulated in cancer cell lines compared to normal controls, indicating their potential role in tumor progression (Fig. [99]1A). Conversely, FAM3B and FAM3C exhibited significant (p-value < 0.05) downregulation in KIRC cells relative to control cells (Fig. [100]1A). To evaluate the diagnostic potential of these genes, ROC analysis was performed, revealing that FAM3A, FAM3B, FAM3C, and FAM3D displayed strong diagnostic capabilities, with AUC values exceeding 0.7 (Fig. [101]1B). Fig. 1. [102]Fig. 1 [103]Open in a new tab Expression analysis and diagnostic performance of FAM3 genes in kidney renal clear cell carcinoma (KIRC) and normal control cell lines. (A) The box plots display the relative expression levels of FAM3A, FAM3B, FAM3C, and FAM3D genes in kidney renal clear cell carcinoma (KIRC) and normal control cell lines, as determined by RT-qPCR. (B) Receiver operating characteristic (ROC) curves assess the diagnostic accuracy of each FAM3 gene in distinguishing KIRC from normal cells. p*-value < 0.05. Expression validation of FAM3 genes using additional KIRC cohorts The expression patterns of the FAM3 family genes in KIRC were validated using additional databases. Analysis of TCGA data via UALCAN confirmed that FAM3A and FAM3D were significantly (p-value < 0.05) upregulated in KIRC samples, whereas FAM3B and FAM3C exhibited markedly lower expression in cancer tissues (Fig. [104]2A). These findings were further supported by OncoDB, where FAM3A and FAM3D showed significantly (p-value < 0.05) elevated expression in KIRC samples, while FAM3B and FAM3C demonstrated reduced expression levels (Fig. [105]2B). Protein expression analysis using the Human Protein Atlas (HPA) reinforced these trends, revealing high staining intensity for FAM3A and FAM3D in KIRC tissues samples, indicative of increased protein levels, while FAM3B and FAM3C displayed weak staining, suggesting reduced protein expression (Fig. [106]2C). Fig. 2. [107]Fig. 2 [108]Open in a new tab Validation of FAM3 Family Gene Expression in KIRC Using UALCAN, OncoDB, and HPA Databases. (A) Expression levels validation of FAM3A, FAM3B, FAM3C, and FAM3D in KIRC tissues compared to normal tissues based on The Cancer Genome Atlas (TCGA) samples using UALCAN. (B) Validation of FAM3 gene expression differences based on The Cancer Genome Atlas (TCGA) samples using OncoDB. (C) Protein expression analysis of FAM3 protein from the Human Protein Atlas (HPA) database reveals immunohistochemical staining patterns in KIRC tissues. p-value < 0.05. Expression of FAM3 genes across cancer stages and analysis of promoter methylation level Next, we conducted analysis to investigate the expression patterns of FAM3 family genes across different cancer stages of KIRC, performed promoter methylation analysis, and analyzed potential impact of methylation level on patient survival. Violin plots generated using the GEPIA2 database illustrated the expression levels of FAM3A, FAM3B, FAM3C, and FAM3D across cancer stages I to IV (Fig. [109]3A). The expression levels of FAM3A remained consistent across stages, with an F value of 1.15 and a p-value of 0.33, indicating no significant variation (Fig. [110]3A). Similarly, FAM3B expression did not show significant stage-specific differences (F value = 1.3, p = 0.272) (Fig. [111]3A). In contrast, FAM3C (F value = 3.13, p = 0.255) and FAM3D (F value = 6.74, p = 0.000182) displayed significant stage-specific expression differences, suggesting a potential role in cancer progression (Fig. [112]3A). The methylation status of FAM3 genes in KIRC compared to normal tissues was assessed using the OncoDB database. Beta value plots revealed significant (p-value < 0.05) promoter hypomethylation of FAM3A and FAM3D, while FAM3B and FAM3C exhibited promoter hypermethylation in KRC samples (Fig. [113]3B). Correlation analysis using the GSCA database further demonstrated a negative correlation between promoter methylation levels and gene expression, reinforcing the regulatory impact of promoter methylation on FAM3 genes (Fig. [114]3C). Survival analysis conducted via the GSCA database evaluated the association between promoter methylation levels and survival outcomes, including Disease-Free Interval (DFI), Disease-Specific Survival (DSS), Overall Survival (OS), and Progression-Free Survival (PFS) (Fig. [115]3D). The results indicated that altered promoter methylation levels (hypomethylation and hypermethylation) of FAM3A, FAM3B, FAM3C, and FAM3D were not significantly (p-value < 0.05) linked to reduced survival rates (except a few cases) in KIRC patients (Fig. [116]3D). Fig. 3. [117]Fig. 3 [118]Open in a new tab Expression and Methylation Analysis of FAM3 Genes in Kidney Renal Clear Cell Carcinoma (KIRC). (A) Violin plots showing the expression patterns of FAM3A, FAM3B, FAM3C, and FAM3D across different stages (I to IV) of KIRC. (B) Promoter methylation analysis of FAM3A, FAM3B, FAM3C, and FAM3D genes using the OncoDB database. (C) Correlation analysis between methylation and mRNA expression of FAM3 genes, obtained from the GSCA database. (D) Survival analysis illustrating the differences between high and low methylation groups for each FAM3 gene across various cancer types, including KIRC. p-value < 0.05. Genetic alteration analysis of FAM3 genes The genetic alteration analysis of FAM3 genes was conducted using the cBioPortal database to evaluate mutation frequency, mutation types, and their potential impact on OS and Disease-Free Survival (DFS) in KIRC patients. The analysis revealed that only FAM3A harbored mutations in KIRC samples, with a mutation frequency of 0.3%, while no mutations were detected in FAM3B, FAM3C, or FAM3D (Fig. [119]4A). The classification of FAM3A mutations indicated that all identified variants were missense mutations, involving single nucleotide changes that resulted in amino acid substitutions (Fig. [120]4B). Variant type analysis demonstrated that these mutations were single nucleotide polymorphisms (SNPs), with C > T transitions being the most prevalent mutation type (Fig. [121]4B). Kaplan–Meier survival analysis was performed to assess the impact of FAM3A mutations on patient survival. The results showed no significant difference in OS and DFS between patients with FAM3A mutations and those without genetic alterations, suggesting that these mutations do not substantially influence KIRC patient prognosis (Fig. [122]4C-D). Fig. 4. [123]Fig. 4 [124]Open in a new tab Genetic Alteration Analysis of FAM3 Genes in Kidney Renal Clear Cell Carcinoma (KIRC) using cBioPortal. (A) Bar chart depicting the frequency of genetic alterations in FAM3A, FAM3B, FAM3C, and FAM3D across kidney renal clear cell carcinoma (KIRC) samples. The green bars represent missense mutations, and the grey areas indicate no alterations. The overall alteration rate is 0.3%. (B) Detailed analysis of variant classification and types for FAM3 genes. (C) Kaplan–Meier survival curve illustrating the overall survival probability for patients with altered (red line) versus unaltered (blue line) FAM3 genes. (D) Kaplan–Meier survival curve showing the disease-free survival probability for patients with altered (red line) versus unaltered (blue line) FAM3 genes. p-value < 0.05. Survival analysis of FAM3 family genes in KIRC Survival analyses were conducted to determine the prognostic significance of FAM3 family genes in KIRC patients. Kaplan–Meier survival analysis using the KM plotter demonstrated that high FAM3A expression was significantly (p-value < 0.05) associated with poorer prognosis (HR 1.43, 95% CI 1.05–1.95, log-rank p = 0.021), suggesting that increased FAM3A expression correlates with reduced OS (Fig. [125]5A). Similarly, the low expression of FAM3B exhibited a strong prognostic impact, as it was significantly (p-value < 0.05) linked to worse OS (HR 1.6, 95% CI 1.13–2.28, log-rank p = 0.0078) (Fig. [126]5A). The low expression FAM3 was found to be significantly (p-value < 0.05) associated with poorer OS (HR 0.61, 95% CI 0.44–0.83, log-rank p = 0.0017) (Fig. [127]5A). Although the survival analysis for FAM3D did not reach statistical significance (HR 0.73, 95% CI 0.52–1.01, log-rank p = 0.057), a trend was observed suggesting that higher expression of FAM3D might be linked to poorer OS outcomes (Fig. [128]5A). To strengthen KM plotter findings, a meta-analysis was performed using the GENT2 database, which consolidated hazard ratios (HR) from multiple independent studies. The pooled HR for FAM3A across various datasets indicated association between high expression and poor OS (HR 2.58, 95% CI 0.85–7.90) (Fig. [129]5B). FAM3B exhibited a pooled HR of 0.53 (95% CI 0.1–2.79), suggesting a correlation between its expression and OS (Fig. [130]5B). The combined HR for FAM3C was 1.06 (95% CI 0.9–1.81), indicating a prognostic impact (Fig. [131]5B). Similarly, FAM3D displayed a pooled HR of 0.92 (95% CI 0.63–1.33), with slight impact on survival (Fig. [132]5B). However, in the meta-analysis, the p-values for all analyses were insignificant, indicating that the observed associations require further validation in larger patient cohorts. Fig. 5. [133]Fig. 5 [134]Open in a new tab Survival and Expression Analysis of FAM3 Genes in Kidney Renal Clear Cell Carcinoma (KIRC). (A) Kaplan–Meier survival plots for FAM3A, FAM3B, FAM3C, and FAM3D using KM Plotter. (B) Forest plots from expression analysis using the GENT2 database, illustrating the hazard ratios for FAM3A, FAM3B, FAM3C, and FAM3D across various studies. Each plot includes individual study results and overall fixed and random effects models. The horizontal lines represent 95% confidence intervals (CI), and the sizes of the boxes reflect the weight of each study in the meta-analysis. Heterogeneity statistics are provided to assess variability among study results. p-value < 0.05. Correlation of FAM3 genes with immune inhibitor genes, immune subtypes, and other mutated genes The relationship between FAM3 family genes and immune-related factors (immune inhibitors and immune subtypes) in KIRC was investigated via TISIDB database. On the other hand, the correlation analysis of FAM3A genes with mutational statuses of other genes was conducted using MuTarget database. Correlation analysis with immune inhibitors using TISIDB database revealed that FAM3A exhibited a significant (p-value < 0.05) positive association with PVRL2, IDO1, CD160, and ADORA2A, while FAM3B correlated positively with VTCN1, TGFB1, PVRL2, and BTLA (p-value < 0.05). Similarly, FAM3C displayed significant (p-value < 0.05) positive correlations with IL10, PDCD1LG2, and TGFBR1, whereas FAM3D was significant (p-value < 0.05) associated with IDO1, IL10, and PVRL2 (Fig. [135]6A). Further analysis of FAM3 gene expression across different immune subtypes in KIRC, based on data from the TISIDB database, indicated significant (p-value < 0.05) variations across subtypes (Fig. [136]6B). FAM3A exhibited the highest expression in C5 and the lowest in C3, while FAM3B showed increased expression in C5 and the lowest in C6. Similarly, FAM3C displayed elevated expression in C5 and reduced expression in C3, whereas FAM3D was highly expressed in C6 and minimally expressed in C5 (Fig. [137]6B). Additionally, analysis of the correlation between FAM3 gene expression and mutational status of other genes in KIRC, utilizing the MuTarget database, demonstrated significant associations (Fig. [138]6C). FAM3A expression was significantly elevated in samples harboring MSR1 mutations (p = 7.58e−03), while FAM3B expression was downregulated in PBRM1-mutant samples (p = 3.11e−03) (Fig. [139]6C). FAM3C exhibited decreased expression in CHD4-mutant samples (p = 2.25e−03), whereas FAM3D showed elevated expression in samples with MLH3 mutations (p = 5.51e−03) (Fig. [140]6C). Fig. 6. [141]Fig. 6 [142]Open in a new tab Correlation of FAM3 Genes with Immune Inhibitor Genes, Immune Subtypes, and Mutant Gene Expression in Kidney Renal Clear Cell Carcinoma (KIRC). (A) Heatmap illustrating the correlation of FAM3A, FAM3B, FAM3C, and FAM3D expression with immune inhibitor genes in KIRC using the TISIDB database. (B) Violin plots showing the expression levels (log2CPM) of FAM3A, FAM3B, FAM3C, and FAM3D across different immune subtypes (C1-C6) in KIRC. (C) Box plots depicting the expression levels of FAM3A, FAM3B, FAM3C, and FAM3D in relation to the mutation status of other genes (MSR1, PBRM1, CHD4, MLH3) in renal clear cell carcinoma using the MuTarget database. p-value < 0.05. Correlations of FAM3 genes with cancer hallmarks, functional states, immune cells, and drug sensitivity in KIRC The correlations of FAM3 family genes with cancer hallmarks and functional states in KIRC were evaluated using the CancerSEA database. FAM3 gene expression demonstrated significant (p-value < 0.05) associations with key cancer hallmarks, including “Sustaining Proliferative Signaling” and “Evading Growth Suppressors”, suggesting their involvement in promoting cancer cell proliferation and bypassing growth-inhibitory mechanisms (Fig. [143]7A). Further investigation into the functional states of FAM3 genes within KIRC revealed their positive correlations with multiple oncogenic processes. FAM3 genes were associated with epithelial-mesenchymal transition (EMT), hypoxia, stemness, differentiation, angiogenesis, invasion, DNA damage, proliferation, and cell cycle regulation, indicating their potential roles in tumor progression (Fig. [144]7B). The immune-related functions of FAM3 genes in KIRC were explored using the GSCA database. Correlation analysis revealed that FAM3A and FAM3B were positively associated with multiple immune cell types, including MAIT cells and CD8_naïve cells, implying their role in enhancing immune cell infiltration within the tumor microenvironment. FAM3C exhibited negative correlations with several immune cell types, including macrophages and monocytes, suggesting an immunosuppressive role in KIRC (Fig. [145]7C). The relationship between FAM3 gene expression and drug sensitivity was analyzed using the GSCA database. FAM3A showed strong positive correlations with resistance to various chemotherapeutic and targeted drugs, including TW 37, AZD7762, BHG712, and BX-912, indicating its potential involvement in drug resistance mechanisms. FAM3B and FAM3C also exhibited correlations with drug resistance, though to a lesser extent than FAM3A (Fig. [146]7D). Fig. 7. [147]Fig. 7 [148]Open in a new tab Correlation Analysis of FAM3 Genes with Cancer Hallmarks, Functional States, Immune Cells, and Drug Sensitivity in Kidney Renal Clear Cell Carcinoma (KIRC). (A) Radial plot depicting the correlation of FAM3 genes with various cancer hallmarks in KIRC using the CancerSEA database. (B) Heatmap showing the correlation of FAM3 genes with 14 different functional states of KRC using the CancerSEA database. (C) Bubble plot illustrating the correlation between FAM3 gene expression and different immune cell infiltrates in KIRC using the GSCA database. (D) Bubble plot showing the correlation between FAM3 gene expression and drug sensitivity (IC50 values) in KIRC using the GSCA database. p-value < 0.05. miRNA-mRNA network of FAM3 family genes miRNA-mRNA analysis was conducted to explore the regulatory interactions between miRNAs and the FAM3 family genes and to evaluate the expression, prognostic significance, and diagnostic potential of the central miRNA in KIRC. The ENCORI database predicted a miRNA-mRNA network, identifying hsa-miR-19b-3p as a key regulator of all four FAM3 genes, indicating its central role in influencing the expression and function of FAM3 genes in KIRC (Fig. [149]8A). Expression analysis using the UALCAN database revealed that hsa-miR-19b was significantly (p-value < 0.05) upregulated in KIRC tumor samples compared to normal tissues (Fig. [150]8B). The RT-qPCR results further corroborated this observation, showing higher expression of hsa-miR-19b-3p in 20 KIRC cell lines relative to 10 normal kidney cell lines (Fig. [151]8D). Survival analysis demonstrated that high expression of hsa-miR-19b was associated with poorer survival outcomes in KIRC patients (Fig. [152]8C). Additionally, the ROC curve analysis revealed that hsa-miR-19b-3p exhibited a high diagnostic value for distinguishing KIRC cell lines from normal individuals (Fig. [153]8E). Fig. 8. [154]Fig. 8 [155]Open in a new tab miRNA-mRNA Network and Expression Analysis of hsa-mir-19b-3p and FAM3 Genes in Kidney Renal Clear Cell Carcinoma (KIRC). (A) miRNA-mRNA interaction network of FAM3A, FAM3B, FAM3C, and FAM3D with various miRNAs predicted using the ENCORI database. (B) Box plot showing the expression levels of hsa-mir-19b-3p in normal kidney tissues and primary KIRC samples from the TCGA database using UALCAN. (C) Kaplan–Meier survival curve depicting the impact of hsa-mir-19b-3p expression levels on overall survival in KIRC patients. (D) Box plot illustrating the expression levels of hsa-mir-19b-3p miRNA in KIRC and normal kidney cell lines measured by RT-qPCR. (E) ROC curve showing the diagnostic performance of hsa-mir-19b-3p miRNA in differentiating KIRC from normal kidney tissues. p-value < 0.05. Gene enrichment analysis Gene enrichment analysis of the FAM3 family genes was conducted using the DAVID database to identify the biological processes, molecular functions, cellular components, and pathways. Cellular component enrichment analysis identified the “Transcription repressor complex” as significantly enriched, suggesting that FAM3 family genes may be involved in forming complexes that repress transcription, potentially playing a role in gene regulation within the cell (Fig. [156]9A). Molecular function enrichment revealed several significant functions, including “NEDD8 ligase activity, histone deacetylase regulator activity, ATP-dependent DNA/DNA annealing activity,” and others related to binding activities such as “TFIID-class transcription factor complex binding, MDM2/MDM4 family protein binding, and p53 binding” (Fig. [157]9B). These findings imply that FAM3 genes might be involved in post-translational modifications, DNA repair, transcription regulation, and interactions with key regulatory proteins like p53, which are crucial for cell cycle control and apoptosis. Biological process enrichment analysis showed that FAM3 genes are involved in processes such as “cellular response to actinomycin D and antibiotics, DNA damage response signal transduction by p53 class mediator, and various metabolic processes including glucose and hexose metabolic processes” (Fig. [158]9C). These results suggest that FAM3 genes may play a role in cellular stress responses, DNA damage repair pathways, and metabolic regulation, highlighting their potential importance in maintaining cellular homeostasis and responding to external stressors. Pathway enrichment analysis revealed that FAM3 genes are significantly associated with pathways related to various cancers, including bladder cancer, endometrial cancer, basal cell carcinoma, and melanoma (Fig. [159]9D). Additionally, pathways related to drug resistance (e.g., platinum drug resistance), signaling (e.g., p53 and Wnt signaling), and cancer progression were enriched (Fig. [160]9D). Fig. 9. [161]Fig. 9 [162]Open in a new tab Gene Enrichment Analysis of FAM3 Genes. (A) Cellular Component (CC) enrichment analysis highlighting the significant enrichment of the transcription repressor complex. (B) Molecular Function (MF) enrichment analysis showing various significantly enriched activities, including NEDD8 ligase activity, histone deacetylase regulator activity, and ATP-dependent DNA/DNA annealing activity. (C) Biological Process (BP) enrichment analysis displaying significant processes such as cellular response to actinomycin D, cellular response to antibiotic, and negative regulation of gluconeogenesis. (D) KEGG pathway enrichment analysis indicating the involvement of FAM3 genes in multiple pathways, including bladder cancer, endometrial cancer, basal cell carcinoma, and melanoma^[163]65,[164]66. p-value < 0.05. Knockdown of FAM3A and functional analysis To investigate the functional role of FAM3A in KIRC, we performed gene knockdown experiments in 786-O cells. The knockdown of FAM3A was confirmed at both the mRNA and protein levels, with a significant reduction in mRNA expression and protein expression in the si-FAM3A-786-O group compared to the control group (Fig. [165]10A-C and supplementary data Fig. [166]1). This reduction resulted in significant alterations in cellular behaviors. Specifically, the proliferation assay revealed a marked decrease in proliferative capacity in cells with reduced FAM3A expression (Fig. [167]10D), suggesting that FAM3A might play a role in supporting cell cycle progression or other proliferation-related pathways. In addition to its role in proliferation, FAM3A knockdown impaired the cells’ ability to form colonies, indicating a decrease in clonogenic potential (Fig. [168]10E, F). This finding suggests that FAM3A is involved in cellular survival and growth under non-adherent conditions, which is an important characteristic for cancer metastasis and progression. Moreover, the wound healing assay demonstrated that cells with reduced FAM3A expression exhibited a faster wound closure rate compared to control cells, indicating that FAM3A may facilitate cell migration, a crucial process in cancer metastasis (Fig. [169]10G, H). Fig. 10. [170]Fig. 10 [171]Open in a new tab Effects of FAM3A Knockdown on 786-O Cell Line: Expression, Proliferation, Colony Formation, and Wound Healing Assays. (A) RT-qPCR analysis of FAM3A mRNA expression in Ctrl-786-O and si-FAM3A-786-O. (B) Western blot analysis of FAM3A protein levels in Ctrl-786-O and si-FAM3A-786-O cells, with GAPDH as the loading control. (C) Densitometric analysis of FAM3A protein expression normalized to GAPDH in Ctrl-786-O and si-FAM3A-786-O cells. (D) Cell proliferation assay showing the percentage of proliferating cells in si-FAM3A-786-O compared to Ctrl-786-O. (E) Representative images of colony formation assay with crystal violet staining for Ctrl-786-O and si-FAM3A-786-O cells. (F) Quantification of the number of colonies formed in the colony formation assay. (G) Wound healing assay quantification showing the percentage of wound closure in Ctrl-786-O and si-FAM3A-786-O cells at 24 h. (H) Representative images of the wound healing assay at 0 and 24 h for both Ctrl-786-O and si-FAM3A-786-O cells. p*-value < 0.05. Discussion KIRC is one of the most common and aggressive forms of kidney cancer, known for its resistance to conventional therapies and high metastatic potential^[172]12,[173]44. This malignancy often presents significant clinical challenges due to its propensity to spread to other organs and its poor response to traditional treatments such as chemotherapy and radiation. As a result, patients with advanced-stage KIRC frequently face a grim prognosis, with limited effective therapeutic options available^[174]45–[175]48. Despite recent advancements in treatment modalities, including targeted therapies and immunotherapies, the overall survival rates for patients with advanced KIRC remain disappointingly low. This emphasizes an urgent need for the identification of novel molecular markers and therapeutic targets that can improve diagnosis, prognostication, and treatment outcomes. The FAM3 gene family, consisting of FAM3A, FAM3B, FAM3C, and FAM3D, represents a group of genes implicated in a variety of critical biological processes. These include metabolic regulation, where they influence glucose and lipid metabolism, cell proliferation, and immune response^[176]14,[177]16. For example, FAM3A has been associated with insulin secretion and glucose homeostasis, while FAM3B, also known as PANDER (pancreatic derived factor), has been implicated in apoptosis and pancreatic beta-cell function^[178]14,[179]16. FAM3C, known for its role in inhibiting epithelial-mesenchymal transition (EMT), a process crucial for cancer metastasis, has garnered attention as a potential tumor suppressor. Similarly, FAM3D has been linked to immune modulation and inflammatory responses^[180]45. Despite these associations, the specific roles and mechanisms of the FAM3 genes in the context of KIRC have not been fully elucidated. Our study explored the expression patterns and functional roles of the FAM3 family genes in KIRC. We observed that FAM3A and FAM3D were significantly upregulated in KIRC cell lines and tumor tissues, while FAM3B and FAM3C were downregulated. These findings align with previous studies that have reported altered expression of these genes in various cancers, suggesting their involvement in tumorigenesis. For instance, Dong et al. reported that FAM3A promotes cell proliferation and migration in lung cancer^[181]16, while Meng et al. demonstrated that FAM3D suppresses tumor growth in colorectal cancer by modulating the immune microenvironment^[182]49,[183]50. Our study adds to this body of knowledge by highlighting the differential expression and potential oncogenic or tumor-suppressive roles of these genes in KIRC. The diagnostic potential of the FAM3 genes was evaluated using ROC analysis, which revealed high AUC values for FAM3 genes, suggesting their utility as biomarkers for KIRC. This is consistent with previous studies where high AUC values for FAM3A have been reported in distinguishing malignant from benign lung and breast tissues^[184]51,[185]52. Our data also revealed significant correlations between FAM3 gene expression and various immune inhibitors, immune subtypes, and gene mutations, indicating a complex interplay between these genes and the tumor microenvironment. For example, FAM3A’s positive correlation with immune inhibitory genes such as IDO1 suggests a role in immune evasion, a characteristic often observed in aggressive cancers. Moreover, the functional analysis of FAM3A knockdown in 786-O cells demonstrated a significant reduction in cell proliferation and colony formation and increase in cell migration. Interestingly, while FAM3A knockdown slowed proliferation and colony formation, it unexpectedly accelerated wound closure in migration assays. This duality suggests a complex role for FAM3A in regulating different aspects of cell behavior. Previous research has shown that FAM3A can modulate glucose metabolism and apoptosis^[186]53, which may contribute to its effects on cell proliferation and survival. Our findings extend these observations to KIRC, indicating that FAM3A may promote tumor progression by supporting metabolic activities and resisting apoptotic signals. Furthermore, we explored the impact of promoter methylation on FAM3 gene expression and patient survival. Our findings revealed distinct methylation patterns among the FAM3 family members, with hypomethylation observed in the promoters of FAM3A and FAM3D and hypermethylation in the promoters of FAM3B and FAM3C. These methylation changes were significantly correlated with the expression levels of these genes, indicating a regulatory mechanism at the epigenetic level. Promoter hypomethylation generally leads to gene activation, which was evident for FAM3A and FAM3D in our study. Increased expression of these genes due to hypomethylation might contribute to tumor progression in KIRC. This phenomenon has been observed in various other cancers as well, where promoter hypomethylation was linked to oncogene activation and enhanced tumor growth^[187]54,[188]55. For instance, hypomethylation of promoters in oncogenes like RASSF1A and CDKN2A has been implicated in the progression of breast and lung cancers, respectively^[189]56,[190]57. Our survival analysis revealed that high expression of FAM3A was associated with poorer OS, reinforcing its potential role as an oncogene in KIRC. This finding is consistent with data from other cancers, such as liver cancer, where FAM3A overexpression correlates with poor prognosis^[191]58. In contrast, FAM3C’s downregulation and its association with worse survival outcomes highlight its potential tumor-suppressive role in KIRC. This observation aligns with prior studies where FAM3C has been demonstrated to inhibit epithelial-mesenchymal transition (EMT) and metastasis in breast cancer^[192]59. The inhibition of EMT is a critical factor in preventing cancer cells from acquiring invasive properties, which are essential for metastasis. Therefore, the downregulation of FAM3C may facilitate the EMT process, thereby promoting metastatic progression in KIRC. Our study suggests that FAM3C may similarly act as a suppressor of metastatic processes in KIRC. This tumor-suppressive role of FAM3C could be multifaceted. Firstly, FAM3C might be involved in maintaining the epithelial phenotype of cells, thereby preventing them from undergoing EMT. By maintaining the epithelial characteristics, FAM3C helps in restricting the cells from becoming motile and invasive, which are prerequisites for metastasis. Secondly, FAM3C may play a role in regulating the tumor microenvironment by inhibiting the expression of various immune cells, including macrophage and monocyte cells etc. To further support the claim that FAM3C may regulate the tumor microenvironment, future experiments should include immune cell profiling in KIRC models, cytokine/chemokine analysis, and co-culture systems with immune cells. Additionally, in vivo models and gene expression analysis in immune cells will provide deeper insights into FAM3C’s role in immune modulation. The miRNA-mRNA analysis presented in this study revealed hsa-mir-19b-3p as a key regulator of all four FAM3 family genes, suggesting a significant role in the modulation of FAM3 gene expression in KIRC. The upregulation of hsa-mir-19b in KIRC tumor samples supports its involvement in the pathogenesis of KIRC by influencing the expression of critical genes. These findings are consistent with previous studies showing that hsa-mir-19b-3p is often upregulated in various cancers, including breast cancer, and can act as an oncogenic miRNA by regulating tumor-related genes^[193]60,[194]61. Specifically, hsa-mir-19b-3p has been implicated in promoting cell proliferation, invasion, and survival^[195]62, which aligns with the higher expression observed in KIRC cell lines and samples compared to normal controls. Furthermore, the association between high expression of hsa-mir-19b and poorer survival outcomes in KIRC patients corroborates findings from other studies, where elevated levels of this miRNA have been linked to poor prognosis in cancers such as breast and colorectal cancer^[196]63,[197]64. The merits of this study lie in its comprehensive analysis of the FAM3 gene family in KIRC, encompassing gene expression, promoter methylation, genetic alterations, and functional implications. This holistic approach provides valuable insights into the diagnostic, prognostic, and therapeutic potential of these genes in KIRC. Additionally, the integration of various databases and experimental validations strengthens the reliability of our findings. However, the study also has certain limitations. The primary limitation is the reliance on publicly available data and in vitro experiments, which may not fully capture the complexity of FAM3 gene functions in vivo. Furthermore, while we identified correlations between FAM3 genes and clinical outcomes, the study does not establish causation. Future research should include in vivo studies and explore the molecular mechanisms underlying the observed associations to provide a more definitive understanding of the roles of FAM3 genes in KIRC. Conclusion Our study on the FAM3 gene family in KIRC revealed significant insights into their roles in KIRC. Expression analysis shows that FAM3A and FAM3D are notably upregulated in cancer cell lines and tissue samples, suggesting their involvement in cancer development, while FAM3B and FAM3C are downregulated, potentially indicating their role as markers or in disease progression. Diagnostic evaluations using ROC curves demonstrate high AUC values for FAM3A and FAM3D, emphasizing their potential as robust biomarkers. Validation through multiple databases confirms these expression patterns and their diagnostic utility. Methylation analysis reveals that FAM3A and FAM3D were hypomethylated, whereas FAM3B and FAM3C were hypermethylated, correlating with reduced patient survival rates. Although FAM3A mutations are rare, they do not significantly impact survival. Functional assays of FAM3A show its crucial role in cell proliferation, clonogenic potential, and migration, highlighting its involvement in cancer metastasis. Overall, FAM3 genes are integral to KIRC, with implications for diagnosis, prognosis, and potential therapeutic targeting. These findings suggest that further research into FAM3 genes could enhance understanding and treatment of kidney cancer. Electronic supplementary material Below is the link to the electronic supplementary material. [198]Supplementary Material 1^ (99.9KB, pdf) Acknowledgements