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PubMed2026

Multi-omics and experimental validation identify RAPGEF2 as a protective prognostic biomarker in clear cell renal cell carcinoma.

Kidney Renal Clear Cell Carcinoma (KIRC) is characterized by marked molecular heterogeneity and metabolic reprogramming, underscoring the need for reliable biomarkers for prognostic assessment and individualized treatment. RAPGEF2, a guanine nucleotide exchange factor has been implicated in cell adhesion and differentiation, but its role in KIRC remains unclear. In this study, we systematically evaluated the expression pattern, prognostic significance, genomic associations, biological function, and therapeutic relevance of RAPGEF2 in KIRC through integrated multi-omics analyses and experimental validation. Pan-cancer single-cell and Spatial transcriptomic analysis revealed heterogeneous RAPGEF2 expression across tumor types, with a relatively prominent signal in KIRC, where RAPGEF2 was mainly enriched in endothelial cells. Survival analyses in the TCGA-KIRC showed that high RAPGEF2 expression was significantly associated with favorable overall survival, disease-specific survival, and progression-free interval, and these findings were validated in independent ICGC_RECA-EU and E-MTAB-1980 cohorts. Multivariate Cox regression further confirmed RAPGEF2 as an independent protective prognostic factor. Immunohistochemistry in a tissue microarray cohort demonstrated that higher RAPGEF2 protein expression was associated with improved overall survival. Genomic analyses showed that low RAPGEF2 expression was related to higher mutational burden. Functional assays demonstrated that RAPGEF2 knockdown promoted KIRC progression. Enrichment analyses indicated that RAPGEF2 may be associated with metabolic pathway remodeling, while immunotherapy cohort analyses suggested its potential association with therapeutic benefit. Collectively, RAPGEF2 is identified as a protective prognostic biomarker and potential functional regulator in KIRC.

Biomarker↗

PubMed2026

Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma exhibits striking intra-tumoral heterogeneity at morphological and genetic levels, complicating treatment and contributing to disease progression. CcRCCs with rhabdoid differentiation are highly aggressive tumors characterized by distinct histopathologies. However, the relationship between morphology, underlying molecular alterations, and tumor behavior remains largely unclear. Here, we present Deep Visual Multi-Omics, an approach integrating digital pathology, morphology-guided single-cell isolation, and ultra-sensitive multi-omics profiling to link cell morphologies to their molecular underpinnings. Across five tumors, we profiled ~40,000 AI-classified and expert-curated cells. We identified progressive molecular dysregulation across cells with increasing histopathological grade coexisting within heterogeneous tumors as well as distinct molecular alterations associated with aggressive rhabdoid ccRCC cells, including signatures consistent with enhanced FOXM1-driven proliferation, altered cell-matrix interactions, and a putative immunomodulatory phenotype. Notably, rhabdoid cells exhibited elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling, suggesting that they themselves may act as a source of signals influencing the local immune microenvironment. Besides providing new insights into the biology of ccRCC and highlighting avenues for future translational studies, this illustrates the potential of Deep Visual Multi-omics to dissect cancer heterogeneity and characterize high-risk cell populations.

Humans↗

PubMed2026

Urinary multi-omics reveal non-invasive diagnostic biomarkers in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy. Yet, no rapid, non-invasive biomarkers are available for diagnosis or screening. Urine represents an ideal analyte matrix due to its accessibility, low invasiveness, longitudinal sampling, and the kidney's central role in filtration. Here, we integrated proteomic, lipidomic, and metabolomic analyses of urine from ccRCC patients and controls to identify diagnostic biomarkers. Multi-omics profiling revealed urogenital metabolic dysregulation in ccRCC, including increased lipid metabolism, altered mitochondrial respiration signatures, and elevated urinary lipid content. We identified three urinary protein biomarkers: serum amyloid A1 (SAA1), haptoglobin (HP), and lipocalin 15 (LCN15). Using a parallel reaction monitoring mass spectrometry workflow, we developed a rapid and sensitive assay and combined these markers into a diagnostic UrineScore. The UrineScore achieved 0.96 in an area under the receiver operating characteristic curve analysis in the discovery cohort, and 0.95 in an independent validation cohort. Together, these results support the feasibility of multi-omics-guided urinary biomarker discovery and represent a step toward accessible diagnostic platforms for ccRCC.

Humans↗

PubMed2026

Proteomic Analysis Identifies Potential Biomarkers of ELOC -Mutated Renal Cell Carcinoma.

ELOC -mutated renal cell carcinoma (RCC) is a rare tumor with only ∼40 cases reported to date; it shares a molecular background with clear cell RCC (ccRCC) in terms of hypoxia-inducible factor-alpha (HIF-α) protein accumulation. ELOC -mutated RCC is characterized by prominent leiomyomatous stromal growth and a more indolent clinical course compared with ccRCC. In our previous study, whole-genome sequencing of 102 ccRCC cases identified 5 cases of ELOC -mutated RCC. In the present study, we conducted proteomic and immunohistochemical analyses on up to 13 Japanese ELOC -mutated RCCs, including 8 previously reported cases, to elucidate its distinct molecular mechanisms and identify biomarkers that may be useful in distinguishing ELOC -mutated RCC from ccRCC. Proteomic profiling revealed that molecules, including cytokeratin 7, scinderin (SCIN), and sortilin 1 (SORT1), were significantly overexpressed in ELOC -mutated RCC compared with ccRCC. Notably, SCIN and SORT1 emerged as novel potential diagnostic biomarkers for distinguishing ELOC -mutated RCC from ccRCC. The analysis further suggested that ELOC -mutated RCC relies more on oxidative phosphorylation and less on glycolysis than ccRCC. This metabolic shift may be linked to the relative depletion of NAD+ due to the low expression of NAPRT and QPRT. In addition, we observed geographic variation in the disease frequency among different cohorts, with a higher frequency in Japan. Our findings provide novel insights into the pathogenesis of ELOC -mutated RCC and highlight SCIN and SORT1 as potential supportive biomarkers.

Humans↗

PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗

PubMed2026

Identification of molecular subtypes in clear cell renal cell carcinoma based on chromatin regulators and tumor immune microenvironment profiling.

In the histological classification of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) accounts for the highest proportion and is the most common subtype. Despite advances in management, it continues to be associated with considerable incidence and mortality. Although surgery and systemic therapies are available, their efficacy is constrained by pronounced intratumoral heterogeneity and treatment resistance. Identifying robust biomarkers and clarifying the underlying biological mechanisms are therefore essential to improving diagnosis, risk stratification and therapeutic decision-making. In this work, we identified two ccRCC molecular subtypes displaying divergent chromatin regulator (CR) profiles and different clinical prognoses. Using the genes differentially expressed between these subgroups, we constructed a CR-related score (CRS) that effectively stratified patients according to survival. More analysis concluded that the low expression of CR was more linked with the immune-activated tumors, which encompassed the immune pathway enrichment, as well as the elevation of numerous immune cell subtypes. Moreover, elevated CRS was associated with improved immunotherapy responsiveness. Drug-sensitivity analyses nominated several candidate agents, and SMARCD3 knockdown in 786-O cells inhibited proliferation and migration and reduced sensitivity to masitinib. Collectively, these findings support the prognostic and therapeutic relevance of CR-related states in ccRCC and provide a framework for future experimental validation of chromatin-regulated tumor-immune interactions.

Humans↗

PubMed2025

HMGA2 links morphological evolution and microenvironment dynamics to systemic therapy response in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) exhibits significant heterogeneity due to morphological changes and tumor microenvironment dynamics, influencing systemic therapy responses. While the role of high-mobility group AT-hook 2 (HMGA2) in tumor progression has been implicated in other cancers, its significance in ccRCC remains unclear. This study investigates the role of HMGA2 in these processes and its clinical impact. METHODS: Spatial transcriptomics (ST) was performed on primary ccRCC samples to investigate expression trajectories associated with HMGA2 expression and morphological evolution. In metastatic ccRCC cohorts treated with systemic therapy, immunohistochemistry and bulk RNA sequencing data were analyzed to evaluate molecular and clinical features in relation to HMGA2. Single-cell RNA sequencing (scRNA-seq) data were used to explore immune cell populations and their interactions. Based on these findings, multiplex immunohistochemistry (mIHC) assessed spatial distribution, cell-cell interactions, and pathological responses of key immune populations. RESULTS: HMGA2 expression was associated with aggressive morphological patterns, such as solid sheets and rhabdoid/sarcomatoid. ST revealed a progressive increase in HMGA2 expression along the morphological trajectory, marked by a shift from clear to eosinophilic cytoplasm, with eccentric nuclei and prominent nucleoli, and loss of vascular architecture. HMGA2-high tumors exhibited aggressive phenotypes driven by cell cycle, epithelial-mesenchymal transition, and inflammatory signaling pathways. Clinically, patients with high HMGA2 had worse progression-free survival but responded better to immune checkpoint inhibitor combination (Combo-ICI) therapy than to tyrosine kinase inhibitor monotherapy. To assess the immune landscape, scRNA-seq data revealed that HMGA2-high tumors were enriched with progenitor exhausted CD8+ T cells (Tpex), along with increased frequencies of conventional dendritic cell type 1 (cDC1) and inflammatory cDC type 2, which were found to interact with Tpex via ICAM-1. mIHC confirmed that Tpex were enriched among Combo-ICI responders in HMGA2-high tumors, with higher densities and closer proximity to ICAM-1+ cDC1. CONCLUSIONS: These findings suggest that dynamic HMGA2 expression contributes to morphological evolution and modulates immune responses through enhanced Tpex-cDCs engagement, serving as a potential marker for systemic therapy response in ccRCC. However, additional experimental studies are required to validate these mechanisms.

Humans↗

PubMed2025

Paradoxical Effect of Myosteatosis on the Immune Checkpoint Inhibitor Response in Metastatic Renal Cell Carcinoma.

BACKGROUND: Treatment for metastatic renal cell carcinoma (mRCC) has shifted from tyrosine kinase inhibitor (TKI) therapy to immune checkpoint inhibitor (ICI)-based therapy, improving outcomes but with variable individual responses. This study investigated the prognostic implications of pretreatment low skeletal muscle mass (LSMM) and myosteatosis in patients with mRCC undergoing first-line ICI-based therapies, comparing outcomes between PD-1 inhibitor + CTLA-4 inhibitor and PD-1 inhibitor + TKI, incorporating single-cell RNA sequencing. METHODS: A retrospective analysis was performed on 90 patients with mRCC treated with ICI-based therapies between November 2019 and March 2023. Patients were grouped based on whether they received PD-1 inhibitor + CTLA-4 inhibitor or PD-1 inhibitor + TKI combinations. LSMM was defined as skeletal muscle index below 40.8 cm2/m2 for men and 34.9 cm2/m2 for women. Myosteatosis was defined using skeletal muscle density, with cut-off values < 41 HU for BMI < 25 kg/m2 and < 33 HU for BMI ≥ 25 kg/m2. Progression-free survival (PFS) and overall survival (OS) were compared using Kaplan-Meier curves and multivariable models. Single-cell RNA sequencing was performed on pretreatment samples to compare the immune microenvironment between patients with and without myosteatosis. RESULTS: The study cohort (26.7% female; median age: 60.5 years) included 59 patients (65.6%) treated with PD-1 inhibitor + CTLA-4 inhibitor and 31 patients (34.4%) treated with PD-1 inhibitor + TKI. LSMM was present in 18.9% of patients, and myosteatosis in 41.1%, with comparable proportions across groups. During follow-up, 29 patients (32.2%) died: 16 in the PD-1 inhibitor + CTLA-4 inhibitor group and 13 in the PD-1 inhibitor + TKI group. The overall 1-year mortality rate was 22.2%, and PFS rate was 53.3%. Myosteatosis predicted poor OS (HR, 5.389; p = 0.008) and PFS (HR, 2.930; p = 0.022) in the PD-1 inhibitor + TKI group but was protective for PFS (HR, 0.461; p = 0.049) in the PD-1 inhibitor + CTLA-4 inhibitor group. LSMM did not significantly affect outcomes in either group. Single-cell RNA sequencing revealed higher CTLA-4 expression in regulatory T cells and more effector memory CD8+ T cells in patients with myosteatosis, whereas patients without myosteatosis had more anti-tumoural non-classical monocytes. CONCLUSIONS: Myosteatosis negatively impacts OS and PFS in patients with mRCC treated with PD-1 inhibitor + TKI therapy but is protective for PFS in those treated with PD-1 inhibitor + CTLA-4 inhibitor therapy. Altered checkpoint expression and immune cell composition associated with myosteatosis may contribute to these differential responses.

Humans↗

PubMed2026

KIM-1 in Advanced Papillary and Clear Cell Renal Cell Carcinoma.

Kidney injury molecule 1 (KIM-1) is a promising biomarker in adjuvant clear cell renal cell carcinoma (ccRCC), but its relevance in advanced ccRCC or papillary RCC (pRCC) remains unclear. CALYPSO (NCT02819596) was a prospective, multi-arm trial that evaluated durvalumab alone or in combination with tremelimumab or savolitinib in metastatic ccRCC and pRCC. Circulating KIM-1 levels were measured at baseline and on-treatment. The primary endpoint was to explore if KIM-1 levels were raised in pRCC. Analyses were exploratory and p values were nominal. KIM-1 was measured in 123 patients with ccRCC and 31 patients with pRCC. Higher median concentrations occurred in pRCC compared to ccRCC (7835 vs 5470 pg/ml, p = 0.05). Reductions in KIM-1 levels occurred with systemic therapy in both ccRCC and pRCC (-59.2% and -32% respectively). In pRCC, radiological responders had significantly lower baseline KIM-1 levels (p = 0.025). In ccRCC, high baseline KIM-1 levels were associated with significantly shorter overall survival (OS) (hazard ratio [HR] 1.77; 95% CI, 1.15-2.72; p = 0.01). Also, an increase in KIM-1 during therapy was linked to worse progression-free survival (HR 1.7; 95% CI, 1.13-2.58; p = 0.01) and OS (HR 1.95; 95% CI, 1.23-3.08; p = 0.004) in ccRCC. This exploratory analysis supports the utility of KIM-1 in advanced ccRCC and pRCC.

Aged↗

PubMed2026

Targeting RELA and STAT3 regulates TNFRSF10A-mediated apoptosis in a novel apoptosis-based prognostic model for clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal malignancy and remains a major cause of cancer-related mortality worldwide. Although advances in surgery, targeted therapy, and immunotherapy have improved outcomes for patients, reliable biomarkers for predicting prognosis remain limited. Therefore, robust gene-based prognostic models are urgently needed to improve risk stratification and guide individualized treatment strategies. METHODS: We developed a novel prognostic model integrating apoptosis and immune - related genes (AIRGs) to predict overall survival (OS) in patients with ccRCC. RESULT: Using Gene Set Enrichment Analysis (GSEA) combined with least absolute shrinkage and selection operator (LASSO) Cox regression, we identified 7 key prognostic genes, namely, CCR4, TNFRSF10A, TEK, TGFA, CD14, IFITM1, and SEMA3G, that collectively demonstrated strong predictive performance in TCGA cohort with c-index = 0.711. Functional enrichment analyses revealed that apoptosis, immune regulation, and multiple oncogenic signaling pathways were significantly associated with the risk score, highlighting the critical role of the tumor microenvironment in ccRCC progression. Transcription factor binding analysis based on the JASPAR database suggested that RELA and STAT3 with scores of 0.829 and 0.951, respectively are potential upstream regulators within the prognostic network, particularly influencing TNFRSF10A expression. External validation using the International Cancer Genome Consortium (ICGC) dataset confirmed the robustness of the prognostic model with c-index = 0.612 Furthermore, in vitro experiments demonstrated that RELA and STAT3 regulate TNFRSF10A-mediated apoptotic signaling in ccRCC cells, providing mechanistic support for the bioinformatic findings. CONCLUSION: This study establishes a biologically informed and clinically relevant prognostic framework for ccRCC. Our findings highlight the therapeutic potential of targeting the RELA/STAT3-TNFRSF10A axis and contribute to the advancement of precision medicine in ccRCC.

Humans↗

PubMed2025

Long-term oncologic outcomes of metastatic clear-cell renal cell carcinoma after local therapy alone.

PURPOSE: Oligometastatic clear-cell renal cell carcinoma (ccRCC) represents a heterogeneous entity that can, in select cases, be managed with primary tumor resection and complete local treatment at all metastatic sites, rendering a patient metastatic with no evidence of disease (M1 NED). M1 NED patients have improved overall survival, although previous cohorts are relatively small and heterogeneous. We sought to identify the natural history of M1 NED ccRCC to clinical trial findings and to optimize management strategies. MATERIALS AND METHODS: Patients with synchronous metastatic ccRCC treated with local therapy alone and considered radiographically M1 NED at our institution between 1989 and 2023 were retrospectively evaluated. Survival probabilities used a combination of Kaplan-Meier estimator, log-rank test, and multivariable Cox proportional hazards regression. When available, limited genomic data obtained using the MSK-IMPACT targeted panel was correlated with outcomes. RESULTS: 85 patients met inclusion criteria. One-year disease free survival (DFS) was 53% (95% CI: 42 to 63%). Sarcomatoid features predicted shorter DFS (HR 2.62, CI: 1.08, 6.34, P = 0.03). Time from first disease recurrence to second recurrence was longer among patients with initial DFS ≥2 years (median 42 vs. 15 months, log-rank P = 0.005). A total of 18 patients (21%) underwent targeted genomic sequencing; higher fraction of genome altered and CDKN2A copy number loss were associated with shorter DFS. Findings were limited by cohort size. CONCLUSIONS: Most M1 NED ccRCC patients will experience disease recurrence, although certain baseline risk factors appear to predict earlier recurrence. Prognostic biomarkers are needed to predict outcomes and facilitate patient management.

Humans↗

PubMed2026

Enhancer-mediated DDIT4 activation by SMYD2-dependent H3K4me1 promotes pazopanib resistance in clear cell renal cell carcinoma.

BACKGROUND: The progression and resistance to targeted therapy, including pazopanib, frequently lead to poor prognosis in clear cell renal cell carcinoma (ccRCC) patients. However, the underlying molecular mechanisms of these processes remain unclear. METHODS: In this study, we first performed RNA-seq to identify genes that were differentially expressed in both SMYD2-knockdown and pazopanib-resistant cells, indicating their potential role in SMYD2-mediated drug resistance. We analyzed TCGA-KIRC data and 150 patient samples to identify the relationship between SMYD2 and DDIT4 expression levels, as well as the prognostic significance of DDIT4. In vitro functional assays and murine models were applied to evaluate the effects of SMYD2 and DDIT4 on tumor growth and on pazopanib resistance. CUT&Tag and chromosome conformation capture (4 C) assays were applied to identify enhancers associated with SMYD2-mediated regulation of DDIT4, while the JASPAR database was utilized to predict transcription factors involved in the enhancer regulation. CRISPR-mediated enhancer deletion and ChIP-qPCR were subsequently performed to validate the regulatory roles of the identified enhancer and the transcription factor SPI1 in DDIT4 expression. RESULTS: Our study revealed that the expression level of DDIT4 is positively correlated with SMYD2. DDIT4 is highly expressed in renal cell carcinoma and is associated with poorer survival outcomes. Further research revealed that SMYD2 regulates H3K4me1 in a DDIT4 distal enhancer (chr10:72830412-72830891), promoting the recruitment of the transcription factor SPI1, thereby activating DDIT4 expression. We found that DDIT4 promotes the proliferation, metastasis, and pazopanib resistance of ccRCC, and DDIT4 knockdown enhances drug sensitivity in both in vitro and in vivo experiments. Furthermore, the SMYD2-DDIT4 axis activates the downstream STAT3 signaling pathway, thereby promoting tumor progression. In addition, DDIT4-related prognostic features showed potential associations with patient survival and predicted drug sensitivity in computational analyses. CONCLUSIONS: Our study identifies a previously unrecognized SMYD2-enhancer-DDIT4 regulatory axis, which promotes tumor progression and pazopanib resistance in ccRCC. These findings may provide potential therapeutic implications to overcome pazopanib resistance and improve treatment outcomes in ccRCC by targeting the SMYD2-enhancer-DDIT4 axis.

Carcinoma, Renal Cell↗

PubMed2025

Methylated ARHGAP40 in renal cell carcinoma associated with tumor necrosis and grade: a potential biomarker for non-invasive early detection.

This study investigated the expression, methylation patterns, and clinicopathological implications of ARHGAP40 in renal cell carcinoma (RCC), the most common urinary malignancy. A total of 60 clear cell renal cell carcinomas (ccRCC), 30 papillary renal cell carcinomas (pRCC), 30 chromophobe renal cell carcinomas (chRCC), and 13 other RCC subtypes were enrolled. ARHGAP40 expression was analyzed in both RCC tissues and matched paracancerous normal tissues using immunohistochemistry (IHC). The methylation status of the ARHGAP40 promoter region was assessed in both normal and tumor samples by bisulfite sequencing PCR (BSP). Circulating tumor DNA (ctDNA) extracted from peripheral blood samples of RCC patients (20), patients with benign renal tumors (1), and healthy controls (14) was quantitatively analyzed for methylation using quantitative methylation-specific PCR (qMSP). ARHGAP40 expression was significantly downregulated in RCC compared to matched normal tissues (P < 0.001). This reduced expression correlated with tumor necrosis (P = 0.009) but showed no significant association with age, gender, tumor location, tumor diameter, TNM stage, or vascular invasion. In the ccRCC subtype, ARHGAP40 expression exhibited a progressive decrease with larger tumor diameter (P = 0.045), advancing histological grade (P = 0.032), and tumor necrosis (P = 0.011). The methylation status of ARHGAP40 was consistent with its expression level in both tumor and adjacent normal tissues. Methylated ARHGAP40 DNA was detectable only in RCC patient ctDNA samples. ARHGAP40 is epigenetically silenced in RCC through methylation-mediated downregulation, which correlates with tumor necrosis and grade. The detection of methylated ARHGAP40 in ctDNA holds promise as a potential biomarker for early RCC diagnosis.

Humans↗

PubMed2026

Survival prediction for clear cell renal cell carcinoma based on deep multimodal synergistic survival network.

Objective.To propose a deep multimodal synergistic survival analysis framework (Deep Multimodal Synergistic Survival Network, DMSSN) to achieve accurate prognostic analysis for clear cell renal cell carcinoma (ccRCC).Methods.This study (DMSSN) utilized matched multimodal data from the Cancer Genome Atlas-KIRC database, including CT imaging data, whole slide images, copy number variation (CNV) features, and clinical data. Deep Canonical Correlation Analysis was employed to map heterogeneous modalities into a shared latent space. Contrastive learning was introduced to enhance semantic consistency across multimodal features, and a gating network was utilized for the adaptive fusion of multimodal information to achieve precise survival risk prediction for patients.Results.Experimental results demonstrated that DMSSN achieved a Concordance Index (C-index) of 0.8153 ± 0.0994, with a Log-rank testp-value of 1.6553×10-11. DMSSN exhibited significant performance advantages over traditional statistical methods like Log-rank-Cox (0.7055 ± 0.0670) and machine learning methods such as Random Survival Forest (RSF) (0.6836 ± 0.1048). Furthermore, in comparison with similar deep learning approaches, DMSSN outperformed late fusion strategies (0.7493 ± 0.1211) and discrete-time survival models such as DeepHit (0.7655 ± 0.1041) and Nnet-surv (0.7694 ± 0.0635). Notably, DMSSN still achieved the best predictive performance when compared to the classic deep survival model DeepSurv (0.7919 ± 0.0978) and advanced state-of-the-art multimodal fusion frameworks like Context-Aware Transformer (0.7735 ± 0.0818) and Multimodal Co-Attention Transformer (0.8102 ± 0.0972). Ablation studies showed that removing any single modality led to a decline in performance, with the largest numerical decrease occurring after removing CT imaging features (C-index decreased to 0.7327), validating the complementarity of multimodal data and the pivotal role of radiomic features in prognostic assessment. Module ablation experiments further confirmed the effectiveness of the core components.Conclusion:By effectively integrating imaging, pathology, genomic, and clinical features, the DMSSN framework demonstrates superior performance and robustness in the survival prediction of ccRCC.

Carcinoma, Renal Cell↗

PubMed2025

miR-9-5p/HMMR regulates the tumorigenesis and progression of clear cell renal cell carcinoma through EMT and JAK1/STAT1 signaling pathway.

BACKGROUND: The most common malignant type of kidney cancer is clear cell renal cell carcinoma (ccRCC). The expression levels of hyaluronan-mediated motility receptor (HMMR) in many tumor types are significantly elevated. HMMR is closely associated with tumor-related progression, treatment resistance, and poor prognosis, and has yet to be fully investigated in terms of its expression patterns and molecular mechanisms of action in ccRCC. Further research is imperative to elucidate these aspects. METHODS: We used The Cancer Genome Atlas (TCGA) database to preliminarily investigate HMMR expression and function in ccRCC and the data for 19 samples from the NCBI GEO database (GSE207493) for single-cell analysis. We assessed the differential expression level of HMMR between ccRCC cancerous tissues and their matched non-tumor tissues. Subsequently, a series of in vivo and in vitro experiments were designed to elucidate the biological function of HMMR in ccRCC, including Transwell assays, CCK-8 assays, clone formation assays and subcutaneous xenograft experiments in nude mice. Through bioinformatics analysis, we identified potential microRNAs (miRNAs) that may regulate HMMR, as well as the possible signaling pathways involved. Finally, we conducted a series of cellular functional experiments to validate our hypotheses regarding the HMMR axis. RESULTS: HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients, and elevated HMMR expression level showed a strong correlation with ccRCC progression and adverse prognoses of patients. Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, while its overexpression amplified these oncogenic properties. In nude mice model, reduced HMMR expression inhibited ccRCC tumor proliferation in vivo. Furthermore, overexpression of an upstream transcriptional regulator, miR-9-5p, effectively downregulated HMMR expression and thus impeded ccRCC cells proliferation and migration. HMMR might influence ccRCC growth via the Epithelial-Mesenchymal Transition (EMT) pathway and the Janus Kinase 1/Signal Transducer and Activator of Transcription 1 (JAK1/STAT1) pathway. CONCLUSIONS: HMMR is overexpressed in ccRCC, and there is a significant link between high HMMR expression and tumor progression, as well as poor patient prognosis. Specifically, HMMR could be targeted and inhibited by miR-9-5p and might modulate the tumorigenesis and progression of ccRCC through both EMT and JAK1/STAT1 signaling pathway.

Carcinoma, Renal Cell↗

PubMed2026

Reduced VEPH1 expression is associated with an invasive phenotype and poor prognosis in clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) remains a clinically heterogeneous urologic malignancy, and improved biomarkers are needed to refine prognostic stratification. VEPH1 has been implicated in cancer biology, but its role in ccRCC is incompletely defined. This study aimed to investigate the expression, prognostic relevance, and functional effects of VEPH1 in ccRCC. METHODS: VEPH1 transcript expression and prognostic relevance were evaluated using The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset and the University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) and validated in paired ccRCC and adjacent normal renal tissues. The ability of VEPH1 transcript expression to distinguish tumor from normal tissues within the TCGA-KIRC dataset was assessed by receiver operating characteristic analysis. Gain- and loss-of-function experiments were performed in 786-O and 769-P ccRCC cells to determine the effects of VEPH1 on epithelial-mesenchymal transition (EMT)-related markers, migration, and invasion. AKT and ERK phosphorylation was evaluated by western blotting. RESULTS: VEPH1 transcript expression was significantly lower in ccRCC tissues than in normal renal tissues and distinguished tumor from normal samples within the TCGA-KIRC dataset. Low VEPH1 transcript expression was associated with poorer overall survival. Validation in 11 paired clinical specimens confirmed reduced VEPH1 messenger RNA (mRNA) and VEPH1 protein expression in tumor tissues. Functionally, VEPH1 overexpression increased E-cadherin, decreased N-cadherin, and suppressed migration and invasion, whereas partial VEPH1 knockdown produced the opposite changes. In exploratory signaling analyses, VEPH1 overexpression was associated with reduced AKT and ERK phosphorylation without altering total AKT or ERK levels. CONCLUSIONS: Reduced VEPH1 transcript expression was associated with poorer overall survival, whereas experimental VEPH1 depletion was associated with invasive and EMT-related features in ccRCC cells. VEPH1 may represent a candidate prognostic indicator in ccRCC; however, its relationship with AKT and ERK signaling and its clinical relevance require further mechanistic and independent-cohort validation.

Clear cell renal cell carcinoma (ccRCC)↗

PubMed2025

The Role of Homologous Recombination Deficiency (HRD) in Renal Cell Carcinoma (RCC): Biology, Biomarkers, and Therapeutic Opportunities.

Renal Cell Carcinoma (RCC) is a common malignancy, often diagnosed incidentally. In recent years, the prognosis of metastatic disease has been improved due to the development of immune checkpoint inhibitors (ICI) and tyrosine kinase inhibitors (TKI) as first-line treatments. However, when progression occurs, the therapeutic options are limited. Understanding crucial biological pathways could lead to a greater understanding of the natural history of the disease, which could help to overcome the mechanism of resistance and to develop new treatments. The clinical significance of homologous recombination deficiency (HRD) in RCC remains to be investigated. To improve the knowledge about this topic, we conducted a narrative review to summarize the current evidence on HRD-related variations and signatures in RCC, together with their prognostic and predictive implications. Preliminary evidence indicates that canonical HRD variants (BRCA1/2) are infrequent in RCC, while broader DNA damage response (DDR) alterations like BAP1, PBRM1, ATM, and SETD2 are more prevalent. Elevated HRD genomic scores in clear-cell RCC correlate with a worse prognosis and an immunologically exhausted microenvironment. From a therapeutic point of view, PARP inhibitor monotherapy has exhibited initial efficacy in small cohorts with high levels of DDR mutation, yet remains investigational for RCC.

Humans↗

PubMed2026

Pathology-Driven Diagnosis of Hereditary Leiomyomatosis and Renal Cell Carcinoma: A Clinicopathological and Genetic Analysis of Three Cases.

INTRODUCTION: Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is an autosomal dominant disorder characterized by three principal clinical features: cutaneous leiomyomas (cLMs), uterine leiomyomas, and fumarate hydratase (FH)-deficient renal cell carcinoma (RCC). Although 200-300 families have been identified worldwide, its true prevalence remains unknown. CASE PRESENTATIONS: We present three HLRCC cases in which detailed pathological examination raised initial clinical suspicion. Cases 1 and 2 presented with advanced RCC exhibiting diverse morphologies. Case 3 presented with multiple painful cLMs and no renal tumors. All three cases were confirmed via germline genetic testing, which revealed distinct FH mutations. CONCLUSIONS: These cases underscore the importance of careful histopathological and immunohistochemical evaluation for the diagnosis of HLRCC. Multidisciplinary discussion integrating clinical, radiological, pathological, and genetic findings is essential for identifying affected families and initiating timely surveillance.

cutaneous leiomyoma↗