Constructing a mitochondrial-related genes model based on machine learning for predicting the prognosis and therapeutic effect in colorectal cancer

Abstract

Abstract The role of mitochondria in tumorigenesis and progression is has been increasingly demonstrated by numerous studies, but its prognostic value in colorectal cancer (CRC) remains unclear. To address this, we developed a mitochondrial-related gene prognostic model using 101 combinations of 10 machine learning algorithms. Patients in the high-risk group exhibited significantly shorter overall survival time. The high-risk group exhibited elevated tumor immune dysfunction and exclusion score, indicating diminished immunotherapy efficacy. To address the suboptimal treatment outcomes in these patients, we identified PYR-41 and pentostatin as potential therapeutic agents, which are anticipated to enhance therapeutic efficacy in the high-risk group. Additionally, four biomarkers (HSPA1A, CHDH, TRAP1, CDC25C) were validated by quantitative real-time PCR, with significant expression differences between normal intestinal epithelial cells and colon cancer cells. Our prognostic model provides accurate CRC outcome prediction and guides personalized therapeutic strategies.

Keywords

• Colorectal cancer
• Mitochondrion
• Machine learning
• Prognosis
• Biomarkers