文献类型：期刊文献

英文题名：Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

作者：Wang, Yanan[1];Jin, Faxiang[1];Mao, Weifang[1];Yu, Yefu[1];Xu, Wenfang[1]

机构：[1]Shaoxing Univ, Dept Clin Lab, Affiliated Hosp, Shaoxing, Zhejiang, Peoples R China

年份：2024

卷号：15

外文期刊名：FRONTIERS IN MICROBIOLOGY

收录：SCI-EXPANDED(收录号：WOS:001169022400001)、、Scopus(收录号：2-s2.0-85185485131)、WOS

基金：The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by grants from the Shaoxing Health and Medical Program (Grant No. 2022KY064) and the Zhejiang Province Medical Science and Technology Project (Grant No. 2023KY1273).

语种：英文

外文关键词：extra-pulmonary tuberculosis; WGCNA; LASSO; SVM-RFE; biomarker

外文摘要：The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.