Meta-Analysis of Peripheral Blood Transcriptome Datasets Reveals a Biomarker Panel for Tuberculosis in Patients Infected With HIV
Yahong Chen, Qiaowen Wang, Shujing Lin, Jinglan Lai, Jing Lin, Wen Ao, Xiao Han, Hanhui Ye
Abstract
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. GBP4 , SERPING1 , ATF3 and CDKBN3 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.