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<p>Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cancer, with a persistently low survival rate despite advancements in therapeutic strategies. Identifying key biomarkers and regulatory networks is crucial for early diagnosis and the development of novel treatment interventions. A previous study highlighted six genes, RUNX1, ADA, MSC, VWF, TGFA, and TREML1, whose expression was associated with poor prognosis and an immunosuppressive tumor microenvironment. Network inference models were employed to further elucidate their regulatory mechanisms, integrating prior knowledge of gene and miRNA interactions. The analysis identified gene predictors with distinct effect sizes for RUNX1, MSC, VWF, and TGFA, which were then subjected to Markov Chain Monte Carlo (MCMC) simulations. This approach uncovered novel regulatory interactions within the ccRCC molecular landscape, alongside previously established transcriptional relationships. Notably, key interactions including RUNX1-MYC, RUNX1-CBFB, VWF-ERG, VWF-ADAMTS13, TGFA-EGFR and TGFA-ADAM17 were found to significantly impact patient survival. Given the oncogenic and immunosuppressive roles of these pathways, they represent promising therapeutic targets for ccRCC treatment. Further exploration of these interactions may lead to biomarker-driven precision medicine approaches, ultimately improving prognosis and survival outcomes for ccRCC patients.</p>

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Gene regulatory network and multi-omics analysis reveals key genetic drivers in clear cell renal cell carcinoma

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Nikhil Gadewal,
  • Vamika Karn,
  • Varun P. Talati,
  • Virupaksha Bastikar

摘要

Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cancer, with a persistently low survival rate despite advancements in therapeutic strategies. Identifying key biomarkers and regulatory networks is crucial for early diagnosis and the development of novel treatment interventions. A previous study highlighted six genes, RUNX1, ADA, MSC, VWF, TGFA, and TREML1, whose expression was associated with poor prognosis and an immunosuppressive tumor microenvironment. Network inference models were employed to further elucidate their regulatory mechanisms, integrating prior knowledge of gene and miRNA interactions. The analysis identified gene predictors with distinct effect sizes for RUNX1, MSC, VWF, and TGFA, which were then subjected to Markov Chain Monte Carlo (MCMC) simulations. This approach uncovered novel regulatory interactions within the ccRCC molecular landscape, alongside previously established transcriptional relationships. Notably, key interactions including RUNX1-MYC, RUNX1-CBFB, VWF-ERG, VWF-ADAMTS13, TGFA-EGFR and TGFA-ADAM17 were found to significantly impact patient survival. Given the oncogenic and immunosuppressive roles of these pathways, they represent promising therapeutic targets for ccRCC treatment. Further exploration of these interactions may lead to biomarker-driven precision medicine approaches, ultimately improving prognosis and survival outcomes for ccRCC patients.