Purpose: To investigate the correlation between M1/M2 macrophages (M1/M2 M&phis;) and cell death mode under Mycobacterium tuberculosis (Mtb) infection.
Methods: Raw gene expression profiles were collected from the Gene Expression Omnibus (GEO) database. Genes related to different cell death modes were collected from the KEGG, FerrDb and GSEA databases. The differentially expressed genes (DEGs) of the gene expression profiles were identified using the limma package in R. The intersection genes of M1/M2 M&phis; with different cell death modes were obtained by the VennDiagram package. Hub genes were obtained by constructing the protein–protein interactions (PPI) network and Receiver Operating Characteristic (ROC) curve analysis. The expression of cell death modes marker genes and Hub genes were verified by Western Blot and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR).
Results: Bioinformatics analysis was performed to screen Hub genes of Mtb-infected M1 M&phis; and different cell death modes, naming NFKB1, TNF, CFLAR, TBK1, IL6, RELA, SOCS1, AIM2; Hub genes of Mtb-infected M2 M&phis; and different cell death modes, naming TNF, BIRC3, MAP1LC3C, DEPTOR, UVRAG, SOCS1. Combined with experimental validation, M1 M&phis; under Mtb infection showed higher expression of death (including apoptosis, autophagy, ferroptosis, and pyroptosis) genes compared to M2 M&phis; and genes such as NFKB1, TNF, CFLAR, TBK1, IL6, RELA, AIM2, BIRC3, DEPTOR show differential expression.
Conclusion: NFKB1, TNF, CFLAR, TBK1, IL6, RELA, AIM2 in Mtb-infected M1 M&phis;, and TNF, BIRC3, DEPTOR in Mtb-infected M2 M&phis; might be used as potential diagnostic targets for TB. At early stage of Mtb infection, apoptosis, autophagy, ferroptosis, and pyroptosis occurred more significantly in M1 M&phis; than that in M2 M&phis;, which may contribute to the transition of Mtb-infected M&phis; from M1-dominant to M2-dominant and contribute to the immune escape mechanisms of Mtb.

Keywords: macrophage polarization, cell death modes, Mycobacterium tuberculosis, bioinformatics analysis, biomarkers