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

HuiPing Liao; QingLan Ma; Lei Chen; Wei Guo; KaiYan Feng; YuSheng Bao; Yu Zhang; WenFeng Shen; Tao Huang; Yu-Dong Cai

doi:10.1016/j.cancergen.2024.12.004

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

Cancer Genet. 2024 Dec 22:290-291:56-60. doi: 10.1016/j.cancergen.2024.12.004. Online ahead of print.

Authors

HuiPing Liao¹, QingLan Ma², Lei Chen³, Wei Guo⁴, KaiYan Feng⁵, YuSheng Bao⁶, Yu Zhang⁷, WenFeng Shen⁸, Tao Huang⁹, Yu-Dong Cai¹⁰

Affiliations

¹ Changping Laboratory, Beijing 102206, China. Electronic address: [email protected].
² School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: [email protected].
³ College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China. Electronic address: [email protected].
⁴ Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. Electronic address: [email protected].
⁵ Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China. Electronic address: [email protected].
⁶ School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: [email protected].
⁷ School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China. Electronic address: [email protected].
⁸ School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China. Electronic address: [email protected].
⁹ Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: [email protected].
¹⁰ School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: [email protected].

PMID: 39729927
DOI: 10.1016/j.cancergen.2024.12.004

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.

Keywords: CD4(+) T cell; Complex disease; Feature selection; Machine learning.