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.