Machine learning analysis of CD4+ T cell gene expression in diverse diseases: insights from cancer, metabolic, respiratory, and digestive disorders.

Abstract

CD4⁺ T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4⁺ T cell-related immune responses exhibit diverse characteristics in different diseases. This study utilizes gene expression analysis of CD4⁺ T cells to classify and understand complex diseases. We analyzed the dataset consisting of samples from various diseases, including cancers, metabolic disorders, circulatory and respiratory diseases, and digestive ailments, as well as 53 healthy controls. Each sample contained expression data for 22,881 genes. Four feature ranking algorithms, incremental feature selection method, synthetic minority oversampling technique, and four classification algorithms were utilized to pinpoint essential genes, extract classification rules and build efficient classifiers. The following analysis focused on genes across rules, such as AK4, CALU, LINC01271, and RUSC1-AS1. AK4 and CALU show fluctuating levels in diseases like asthma, Crohn's disease, and breast cancer. The analysis results and existing research suggest that they may play a role in these diseases. LINC01271 generally has higher expression in conditions including asthma, Crohn's disease, and diabetes. RUSC1-AS1 is more expressed in chronic diseases like asthma and Crohn's, but less in acute illnesses like tonsillitis and influenza. This highlights the distinct roles of these genes in different diseases. Our approach highlights the potential for developing novel therapeutic strategies based on the transcriptional profiles of CD4⁺ T cells.