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<p>Renal cell carcinoma (RCC), ranking sixth in global mortality, poses diagnostic and prognostic challenges. High-throughput omics data have transformed RCC research and diagnostics. This study harnessed National Center for Biotechnology Information (NCBI) genomic data and bioinformatics approaches to unravel essential genes associated with RCC. Four gene expression profiles (121 ccRCC and 40 normal kidney tissues) were obtained from NCBI Gene Expression Omnibus (GEO). Differentially regulated marker genes (DRMGs) were identified and subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein–protein interaction (PPI) network, and Kaplan-Meier survival analysis. A total of 223 DRMGs were identified (adjusted P value <i>&lt;</i> 0.05; log2FC ≥ 1.5 and log2FC ≤ − 1.5). Ontology analysis revealed links to processes like cell proliferation, cell death, and cell adhesion while KEGG analysis highlighted pathways like actin cytoskeleton, phagosome and cell adhesion. PPI network identified eight core DRMGs i.e. KL, IGSF6, APOC1, CYP4F2, AHNAK2, ESRRG, CTXN3, and ALDH4A1. Among them CYP4F2, KL, ESRRG and ALDH4A1 were found downregulated in RCC tissues while AHNAK2 and APOC1 exhibited enhanced expression in RCC tissues compared to normal kidneys, as verified by Clinical Proteomic Tumor Analysis Consortium (CPTAC) and human protein Atlas (HPA). Kaplan-Meier survival analysis revealed prognostic significance of Klotho (KL), ESRRG (Estrogen-Related Receptor Gamma) and Cortexin 3 (CTXN3), where elevated levels were found associated with improved overall survival. These findings highlight the potential of these genes as potential prognostic markers and therapeutic targets in RCC.</p>

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Bioinformatics based identification of key prognostic genes and vital pathways in renal cell carcinoma

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Shahzadi Noreen,
  • Aamir Shahzad,
  • Safa Akhtar,
  • Farah Deeba,
  • Saher Mahmood,
  • Iqra Munir

摘要

Renal cell carcinoma (RCC), ranking sixth in global mortality, poses diagnostic and prognostic challenges. High-throughput omics data have transformed RCC research and diagnostics. This study harnessed National Center for Biotechnology Information (NCBI) genomic data and bioinformatics approaches to unravel essential genes associated with RCC. Four gene expression profiles (121 ccRCC and 40 normal kidney tissues) were obtained from NCBI Gene Expression Omnibus (GEO). Differentially regulated marker genes (DRMGs) were identified and subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein–protein interaction (PPI) network, and Kaplan-Meier survival analysis. A total of 223 DRMGs were identified (adjusted P value < 0.05; log2FC ≥ 1.5 and log2FC ≤ − 1.5). Ontology analysis revealed links to processes like cell proliferation, cell death, and cell adhesion while KEGG analysis highlighted pathways like actin cytoskeleton, phagosome and cell adhesion. PPI network identified eight core DRMGs i.e. KL, IGSF6, APOC1, CYP4F2, AHNAK2, ESRRG, CTXN3, and ALDH4A1. Among them CYP4F2, KL, ESRRG and ALDH4A1 were found downregulated in RCC tissues while AHNAK2 and APOC1 exhibited enhanced expression in RCC tissues compared to normal kidneys, as verified by Clinical Proteomic Tumor Analysis Consortium (CPTAC) and human protein Atlas (HPA). Kaplan-Meier survival analysis revealed prognostic significance of Klotho (KL), ESRRG (Estrogen-Related Receptor Gamma) and Cortexin 3 (CTXN3), where elevated levels were found associated with improved overall survival. These findings highlight the potential of these genes as potential prognostic markers and therapeutic targets in RCC.