Identification and Analysis of Autophagy-Related Genes as Diagnostic Markers and Potential Therapeutic Targets for Tuberculosis Through Bioinformatics.

Abstract

According to the World Health Organization, Mycobacterium tuberculosis infections affect approximately 25% of the world's population. There is mounting evidence linking autophagy and immunological dysregulation to tuberculosis (TB). As a result, this research set out to discover TB-related autophagy-related biomarkers and prospective treatment targets. We used five autophagy databases to get genes linked to autophagy and Gene Expression Omnibus databases to get genes connected to TB. Then, functional modules associated with autophagy were obtained by analyzing them using weighted gene co-expression network analysis. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to examine the autophagy-related genes (ATGs) of important modules. Limma was used to identify differentially expressed ATGs (DE-ATGs), and the external datasets were used to further confirm their identification. We used DE-ATGs and a protein-protein interaction network to search the hub genes. CIBERSORT was used to estimate the kinds and amounts of immune cells. After that, we built a drug-gene interaction network and a network that included messenger RNA, small RNA, and DNA. At last, the differential expression of hub ATGs was confirmed by RT-qPCR, immunohistochemistry, and western blotting. The diagnostic usefulness of hub ATGs was evaluated using receiver operating characteristic curve analysis. Including 508 ATGs, four of the nine modules strongly linked with TB were deemed essential. Interleukin 1B (IL1B), CAPS1, and signal transducer and activator of transcription 1 (STAT1) were identified by intersection out of 22 DE-ATGs discovered by differential expression analysis. Research into immune cell infiltration found that patients with TB had an increased proportion of plasma cells, CD8 T cells, and M0 macrophages. A competitive endogenous RNA network utilized 10 long non-coding RNAs and 2 miRNAs. Then, the IL1B-targeted drug Cankinumad was assessed using this network. During bioinformatics analysis, three hub genes were validated in mouse and macrophage infection models. We found that IL1B, CASP1, and STAT1 are important biomarkers for TB. As a result, these crucial hub genes may hold promise as TB treatment targets.