Data Mining Pathogenic Gene Transcription Factors and Their Regulatory Network Nodes.

Tuberculosis (TB) is one of the deadliest infectious diseases worldwide. In , changes in gene expression are highly variable and involve many genes, so traditional single-gene screening of targets has been unable to meet the needs of clinical diagnosis. In this study, using the National Center for Biotechnology Information (NCBI) GEO Datasets, whole blood gene expression profile data were obtained in patients with active pulmonary tuberculosis. Linear model-experience Bayesian statistics using the Limma package in R combined with -tests were applied for nonspecific filtration of the expression profile data, and the differentially expressed human genes were determined. Using DAVID and KEGG, the functional analysis of differentially expressed genes (GO analysis) and the analysis of signaling pathways were performed. Based on the differentially expressed gene, the transcriptional regulatory element databases (TRED) were integrated to construct the pathogenic gene regulatory network, and the correlation of the network genes with disease was analyzed with the DAVID online annotation tool. It was predicted that IL-6, JUN, and TP53, along with transcription factors SRC, TNF, and MAPK14, could regulate the immune response, with their function being extracellular region activity and protein binding during infection with .