Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs - Deng, Siqi; Shen, Shijie; Liu, Keyu; El-Ashram, Saeed; Alouffi, Abdulaziz; Cenci-Goga, Beniamino Terzo; Ye, Guomin; Cao, Chengzhang; Luo, Tingting; Zhang, Hui; Li, Weimin; Li, Siyuan; et al. (Frontiers in Genetics, 2023)
Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for TB because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets (GSE19435, 83456, and 34608) from the GEO database. The GSE19435 and GSE83456 were used to build a weighted gene co-expression network for searching for hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1 and confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR), naming RTP4, CXCL10, CD38, and IFI44. CMap was used to predict potential therapeutic compounds for tuberculosis using 300 DEGs (150 downregulated and 150 upregulated genes), and 6 small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-TB therapeutic compounds. However, more clinical trials were necessary to determine their effect on TB.