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A Comprehensive Investigation to Identify Prognostic mRNA and miRNA Signatures for Renal Cell Carcinoma Utilizing a Stratification-Based Approach

Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, China
Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University and Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan 523808, China
School of Public Health, Guangdong Medical University, Dongguan 523808, China
Dongguan Key Laboratory of Sepsis Translational Medicine, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523710, China

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Abstract

This study systematically investigates the roles of messenger RNAs (mRNAs) and microRNAs (miRNAs) in Renal Cell Carcinoma (RCC) and their potential as diagnostic and prognostic biomarkers via a stratification approach. By utilizing The Cancer Genome Atlas (TCGA) database, differentially expressed mRNAs (namely DEGs) and miRNAs (namely DEMs) are identified, and survival prognosis-related biomarkers are determined through Kaplan-Meier analysis and lasso regression. Prognostic models are established for RCC ethnicity, pathologic stages, and metastatic status, with validation through plotting risk heatmaps, risk curves, survival curves, and Receiver Operating Characteristic (ROC) curves. A total of 45 mRNA and 33 miRNA biomarkers are identified across different prognostic models, resulting in enhanced prediction accuracy with increased stratification. The literature review confirms abnormal expressions of 28 prognostic RNAs reported in experiments and 15 prognostic RNAs reported in bioinformatics studies. The study also introduces 35 novel prognostic RNAs as potential treatment targets for RCC. The mRNA+miRNA prognostic models exhibit the most robust predictive capability, indicating their potential clinical relevance. Overall, the study contributes to a precise prognosis of RCC by exploring novel biomarkers and potential therapeutic targets.

Keywords

Renal Cell Cancer (RCC)stratificationprognostic modelmessenger RNA (mRNA)microRNA (miRNA)biomarker

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Big Data Mining and Analytics
Volume 7 Issue 4,
December 2024
Pages 1396-1416
DOI: 10.26599/BDMA.2024.9020039
Cite this article:
Yu J, Cai J, Fahira A, et al. A Comprehensive Investigation to Identify Prognostic mRNA and miRNA Signatures for Renal Cell Carcinoma Utilizing a Stratification-Based Approach. Big Data Mining and Analytics, 2024, 7(4): 1396-1416. https://doi.org/10.26599/BDMA.2024.9020039
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