Objective To screening potential gene expression signatures in peripheral blood that can be used to in the differential diagnosis of infection of human immunodeficiency virus (HIV) and the co-infection of HIV/Mycobacterium tuberculosis (MTB).

Methods Dataset GSE50834 was downloaded from the Gene Expression Omnibus (GEO) database and divided into two groups: infection of HIV and co-infection of HIV/MTB. Differential analysis was performed to identify genes that were differentially expressed in the two groups. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to understand the functions and pathways associated with the differentially expressed genes. A protein-protein interaction (PPI) network was constructed, and key genes were identified. The diagnostic value of these key genes was assessed using receiver operating characteristic (ROC) analysis. Additionally, weighted gene co-expression network analysis (WGCNA) was performed to further validate the potential gene expression signature.

Results A total of 100 differentially expressed genes (DEGs) were identified, including 58 up-regulated genes and 42 down-regulated genes. GO functional annotation revealed that the DEGs were mainly associated with inflammation, plasma membrane, and carbohydrate binding. KEGG pathway enrichment analysis indicated that the DEGs were mainly involved in the complement coagulation cascade and chemokine signaling pathways. From the PPI network, 4 key genes were identified, all of which showed diagnostic value according to ROC analysis. Furthermore, WGCNA analysis identified CXC motif chemokine ligand 2 (CXCL2), pro-platelet basic protein (PPBP), and CXC motif chemokine ligand 10 (CXCL10) as genes with high connectivity.

Conclusion Our findings suggest that CXCL2, PPBP, and CXCL10 genes coan be used as gene expression signatures for the differential diagnosis of infection of HIV and co-infection ofHIV/MTB, which can provides ideas for the exploration of related biomarkers.