Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV

• Published in: Frontiers in cellular and infection microbiology Vol. 11; p. 585919
• Main Authors: Chen, Yahong, Wang, Qiaowen, Lin, Shujin, Lai, Jinglan, Lin, Jing, Ao, Wen, Han, Xiao, Ye, Hanhui
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.03.2021
• Subjects: Cellular and Infection Microbiology; human immunodeficiency virus; meta-analysis; microarray; transcriptome; tuberculosis; human immunodeficiency virus; meta-analysis; microarray; transcriptome; tuberculosis
• ISSN: 2235-2988; 2235-2988
• DOI: 10.3389/fcimb.2021.585919

Biomarkers are critical for rapid diagnosis of tuberculosis (TB) and could benefit patients with AIDS where diagnosis of TB co-infection is challenging. Meta-analysis is an approach to combine the results of the studies with standard statistical method by weighting each study with different sample size. This study aimed to use meta-analysis to integrate transcriptome datasets from different studies and screen for TB biomarkers in patients who were HIV-positive. Five datasets were subjected to meta-analysis on whole-blood transcriptomes from 640 patients infected with HIV. A total of 293 differentially expressed genes (DEGs) were identified as significant (P<0.0001) using the random effective model to integrate the statistical results from each study. DEGs were enriched in biological processes related to TB, such as "Type I interferon signaling" and "stimulatory C-type lectin receptor signaling". Eighteen DEGs had at least a two-fold change in expression between patients infected with HIV who were TB-positive and those who were TB-negative. , , and were selected as a biomarker panel to perform multivariable logistic regression analysis on TB status and relative gene expression levels. The biomarker panel showed excellent accuracy (AUC>0.90 for HIV+TB) in clinical trial and suggests that meta-analysis is an efficient method to integrate transcriptome datasets from different studies.