Identification and validation of parthanatos-related genes in lung adenocarcinoma and construction of a prognostic risk model.

As a major cause of cancer-related death, lung adenocarcinoma (LUAD) remains a significant health challenge. Parthanatos plays a crucial role in tumor progression, influencing cancer cell survival and therapy resistance. This study constructed a parthanatos-based prognostic model and explored its associated biological processes. Data on LUAD transcriptomics and parthanatos-related genes were collected from publicly databases and related literature. Differential expression analysis and weighted gene co-expression network analysis were employed to identify candidate genes. Univariate Cox regression analysis and machine learning algorithms were employed to screen prognostic genes and construct the risk model. Gene expression patterns and intercellular communication within distinct cell types were explored by single-cell sequencing. Lastly, prognostic gene expression in tissue samples was verified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting. PPP1R14B, MIF, ALG3, C11orf24, and MZT2A were identified as prognostic genes. The risk model had good predictive performance. The risk score effectively stratified patients into high- and low-risk groups with significantly divergent overall survival (p< 0.0001). Prognostic genes were involved in vital processes such as DNA replication, protein metabolism, and immune response. Single-cell analysis highlighted expression variations across cell types, particularly in epithelial cells, with strong communication from myeloid cells and fibroblasts. RT-qPCR confirmed the high expression of prognostic genes in LUAD. Importantly, PPP1R14B expression was particularly significant, and it potentially influenced the LUAD malignant phenotype. This study constructed and validated a risk model for LUAD associated with parthanatos, providing new insights into the pathological mechanisms of LUAD and highlighting potential therapeutic targets.