Work

Employ machine learning to identify NAD+ metabolism-related diagnostic markers for ischemic stroke and develop a diagnostic model

Year: 2024

Type: article

Abstract: Ischemic stroke (IS) is a severe condition regulated by complex molecular alterations. This study aimed to identify potential nicotinamide adenine dinucleotide (NAD+) metabolism-associated diagnostic markers of IS and explore their associations with immune dynamics. Weighted Gene Co-expression Network Analysis and single-sample gene set enrichment analysis (ssGSEA) were employed to identify key gene modules on the GEO dataset (GSE16561). LASSO regression was used to identify diagnostic genes. A diagnostic model was then developed using the training dataset, and its performance was assessed using a validation dataset (GSE22255 dataset). Associations between hub genes and immune cells, immune response genes, and human leukocyte antigen (HLA) genes were assessed by ssGSEA. A regulatory network was constructed using mirBase and TRRUST databases. A total of 20 NAD+ metabolic genes exhibited noteworthy expression variations. Within the module notably associated with NAD+ metabolism, 19 specific genes were included in the diagnostic model, which was validated on the GSE22255 dataset (AUC: 0.733). There were significant disparities in immune cell populations, immune response genes, and HLA gene expression, all of which were associated with the hub genes. A regulatory network composed of 153 edges and 103 nodes was constructed. This study advances our understanding of IS by providing insights into NAD+ metabolism and gene interactions, contributing to potential diagnostic innovations in IS. • Identified NAD+ metabolism-associated markers for ischemic stroke (IS). • Used WGCNA and ssGSEA for key gene modules on GSE16561 dataset. • Constructed a diagnostic model using LASSO regression on GSE22255 dataset. • Explored associations between hub genes, immune dynamics, and HLA genes. • Created a regulatory network using mirBase and TRRUST databases.

Source: Experimental Gerontology

DOI: 10.1016/j.exger.2024.112584

Authors: Yameng Sun, Shenghao Ding, Fei Shen, Xiaolan Yang, Wenhua Sun, Jieqing Wan

Institutions: Shanghai Jiao Tong University, Renji Hospital

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990