Identification of the key genes of tuberculosis and construction of a diagnostic model via weighted gene co-expression network analysis

Tuberculosis (TB) is an infectious disease with high mortality, and mining key genes for TB diagnosis is vital to raise the survival rate of patients. The whole microarray datasets GSE83456 (training set) and GSE19444 (validation set) of TB patients were downloaded from the Gene Expression Omnibus (...

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Bibliographic Details
Published in:Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy 2023-11, Vol.29 (11), p.1046-1053
Main Authors: Li, Baiying, Sun, Lifang, Sun, Yaping, Zhen, Libo, Qi, Qi, Mo, Ting, Wang, Huijie, Qiu, Meihua, Cai, Qingshan
Format: Article
Language:English
Subjects:
Diagnostic model
Macrophage
ssGSEA
Tuberculosis
WGCNA
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Summary:Tuberculosis (TB) is an infectious disease with high mortality, and mining key genes for TB diagnosis is vital to raise the survival rate of patients. The whole microarray datasets GSE83456 (training set) and GSE19444 (validation set) of TB patients were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression was conducted on genes between TB and normal samples (unconfirmed TB) in GSE83456 to yield TB-related differentially expressed genes (DEGs). DEGs were subjected to weighted gene co-expression network analysis (WGCNA) and clustered to form distinct gene modules. The immune scores of 25 kinds of immune cells were obtained by single-sample gene set enrichment analysis (ssGSEA) of TB samples, and Pearson correlation analysis was carried out between the 25 immune scores and diverse gene modules. The gene modules significantly associated with immune cells were retained as Target modules. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in the modules (p-value
ISSN:1341-321X
1437-7780
DOI:10.1016/j.jiac.2023.07.011