Identification and validation of programmed cell death-related biomarkers in severe pneumonia: A diagnostic observational study using bioinformatics analysis

Severe pneumonia is the leading cause of mortality in individuals with lung disease. Programmed cell death (PCD) is implicated in various pathologies; however, the precise relationship between severe pneumonia and PCD remains unclear. This study aimed to identify PCD-related biomarkers in severe pneumonia. Gene expression data were sourced from publicly available databases. Biomarkers were identified through weighted gene co-expression network analysis (WGCNA), machine learning, expression validation, and receiver operating characteristic analyses. Potential mechanisms underlying these biomarkers in severe pneumonia were explored using functional similarity analysis, enrichment analysis, and immune microenvironment analysis. Expression levels of the biomarkers were further validated by reverse transcription-quantitative polymerase chain reaction. Four biomarkers - BCL2, CDKN2D, DYRK2, and S100A8 - were identified. Functional similarity analysis highlighted strong functional parallels among these biomarkers. Notably, these 4 biomarkers were involved in key processes such as graft-versus-host disease, complement and coagulation cascades, ribosome activity, and spliceosome function. Immune microenvironment analysis revealed 12 differential immune cell types with significant negative correlations, with neutrophils exhibiting the strongest inverse correlation with monocytes. In contrast, M1 macrophages showed the strongest positive correlation with M0 macrophages. Reverse transcription-quantitative polymerase chain reaction validation demonstrated that CDKN2D and S100A8 were significantly upregulated, whereas DYRK2 was markedly downregulated in severe pneumonia. This study identified and validated 4 biomarkers associated with severe pneumonia, offering critical insights for the development of personalized therapeutic strategies for affected patients.