Identification of Hub Genes, Non-coding RNAs and Pathways in Renal Cell Carcinoma (RCC): A Comprehensive in Silico Study

Overview

Journal Biochem Biophys Rep

Specialty Biochemistry

Date 2025 Feb 21

PMID 39980583

Authors

Ahmad Golestanifar

Hengameh Khedri

Parisa Noorabadi

Mohammadreza Saberiyan

Affiliations

Soon will be listed here.

Abstract

Backgrounds: Renal cell carcinoma (RCC) is the most common type of kidney cancer in adults. RCC begins in the renal tubule epithelial cells, essential for blood filtration and urine production.

Methods: In this study, we aim to uncover the molecular mechanisms underlying kidney renal clear cell carcinoma (KIRC) by analyzing various non-coding RNAs (ncRNAs) and protein-coding genes involved in the disease. Using high-throughput sequencing datasets from the Gene Expression Omnibus (GEO), we identified differentially expressed mRNAs (DEMs), miRNAs (DEMIs), and circRNAs (DECs) in KIRC samples compared to normal kidney tissues. Our approach combined differential expression analysis, functional enrichment through Gene Ontology (GO) and KEGG pathway mapping, and a Protein-Protein Interaction (PPI) network to identify crucial hub genes in KIRC progression.

Results: Key findings include the identification of hub genes such as EGFR, FN1, IL6, and ITGAM, which were closely associated with immune responses, cell signaling, and metabolic dysregulation in KIRC. Further analysis indicated that these genes could be potential biomarkers for prognosis and therapeutic targets. We constructed a competitive endogenous RNA (ceRNA) network involving lncRNAs, circRNAs, and miRNAs, suggesting complex regulatory interactions that drive KIRC pathogenesis.Additionally, the study examined drug sensitivity associated with the expression of hub genes, revealing the potential for personalized treatments. Immune cell infiltration patterns showed significant correlations with hub gene expression, highlighting the importance of immune modulation in KIRC.

Conclusion: This research provides a foundation for developing targeted therapies and diagnostic biomarkers for KIRC while underscoring the need for experimental validation to confirm these bioinformatics insights.

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