Classification of tuberculosis-related programmed cell death-related patient subgroups and associated immune cell profiling

BackgroundTuberculosis (TB) is the deadliest communicable disease in the world with the exception of the ongoing COVID-19 pandemic. Programmed cell death (PCD) patterns play key roles in the development and progression of many disease states such that they may offer value as effective biomarkers or therapeutic targets that can aid in identifying and treating TB patients. Materials and methodsThe Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. Profiling of differentially expressed PCD-related genes was performed, after which candidate hub PCD-associated genes were selected via a machine learning approach. TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. The potential roles of these PCD-associated genes in other TB-related diseases were further examined. ResultsIn total, 14 PCD-related differentially expressed genes (DEGs) were identified and highly expressed in TB patient samples and significantly correlated with the abundance of many immune cell types. Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. Furthermore, we used CMap to predict five potential drugs for TB-related diseases. ConclusionThese results highlight clear enrichment of PCD-related gene expression in TB patients and suggest that this PCD activity is closely associated with immune cell abundance. This thus indicates that PCD may play a role in TB progression through the induction or dysregulation of an immune response. These findings provide a foundation for further research aimed at clarifying the molecular drivers of TB, the selection of appropriate diagnostic biomarkers, and the design of novel therapeutic interventions aimed at treating this deadly infectious disease.