HLA-G gene polymorphisms as predictors of survival in colorectal cancer: A unified machine learning approach

Abstract

Objectives: Human Leukocyte Antigen (HLA-G) is a potent molecule involved in immune-tolerance. Here, we investigated the contribution of HLA-G gene polymorphisms (14 bp Ins/Del and +3142C/G) for accurate prediction of colorectal cancer (CRC) overall survival (OS) status. Our study presents a comprehensive investigation of the prognostic value of HLA-G genotypes and haplotypes in predicting OS status in 266 Tunisian patients with CRC. Methods: We used a machine learning (ML)-based framework described below: (1) A dimensionality reduction approach was used to examine evidence of an association between HLA-G genotypes and OS status. (2) Decision-tree ML models were used to explore the performance of the HLA-G genotype as a relevant contributing feature to accurately predict OS status. Results: HLA-G polymorphisms were highly predictive of OS status when a random forest classifier was used. The HLA-G 14 bp Ins/Del polymorphism outperformed the HLA-G + 3142C/G polymorphism as a predictor of OS. The Del/Del genotype was associated with worse OS and the G/G genotype was associated with favorable OS. The InsC haplotype predicted a favorable prognosis, and the DelG haplotype predicted a worse OS. The combined prediction demonstrated, with 100 % precision and high accuracy, that Del/Del genotype associated with key clinical features, can efficiently predict worse OS. The results were evaluated through an external validation process to ensure their reliability. Conclusions: We demonstrated the potential of HLA-G gene polymorphisms as robust candidate biomarkers to predict OS in CRC patients. The research on the HLA-G gene presents a promising avenue for developing an innovative decision-making tool to identify candidates for personalized therapeutic interventions.

Keywords

• HLA-G gene polymorphism
• Colorectal cancer
• Overall survival status
• Machine learning
• Decision tree
• Random forest