This paper explores the potential of mitochondrial-associated programmed-cell-death (mtPCD) patterns as biomarkers for predicting the prognosis of non-small-cell lung cancer (NSCLC). NSCLC is the most common type of lung cancer, accounting for 85% of all cases, and despite advances in treatment, prognosis remains poor for many patients. The study investigates various mtPCD-related genes and their association with patient outcomes, aiming to identify a prognostic signature that can guide personalized treatment.
The research involved analyzing data from four datasets containing information on 977 NSCLC patients and 1210 mtPCD genes. The study used a comprehensive bioinformatics approach to identify mtPCD genes correlated with patient prognosis. mtPCD-related genes were identified through correlation analysis between mitochondrial and PCD gene sets. Univariate Cox regression analysis was used to select genes significantly associated with overall survival (OS). Using LASSO regression analysis, a prognostic model containing 18 mtPCD genes was developed. These genes were found to be significant predictors of patient outcomes. The model's accuracy was validated internally using training and validation sets, as well as externally using three additional datasets (GSE29013, GSE31210, and GSE37745).
The study examined the relationship between the prognostic model's risk scores and various clinical features, including age, sex, tumor stage, and survival status. The results showed that the risk score was significantly correlated with these clinical features, indicating its potential utility in clinical decision-making. Differences in gene mutations and expression levels between high- and low-risk groups were analyzed. Genomic analysis revealed higher mutation frequencies in high-risk patients, particularly in genes like TP53. Transcriptomic analysis identified differentially expressed genes (DEGs) between high- and low-risk groups, with enrichment in pathways related to cancer progression and immune response.
The study assessed immune cell infiltration and tumor microenvironment characteristics in relation to the prognostic model. Immune cell infiltration analysis showed higher Treg cell infiltration in high-risk patients, suggesting potential immune evasion mechanisms. The role of a specific gene, RIPK2, was investigated through in vitro and in vivo experiments. Functional studies on RIPK2 demonstrated its role as an oncogene in NSCLC, with its knockdown reducing tumor growth and promoting apoptosis.
The study found that the 18-gene mtPCD signature could accurately predict patient survival, with high-risk patients showing significantly poorer outcomes. The model demonstrated robust performance across different datasets. The prognostic model successfully stratified patients into high- and low-risk groups based on their mtPCD gene expression profiles. High-risk patients had significantly lower survival rates and shorter survival times.
The study concludes that mtPCD-related genes hold promise as prognostic biomarkers for NSCLC. The developed prognostic model provides a robust tool for predicting patient outcomes and could guide personalized treatment strategies. The findings highlight the importance of RIPK2 as a potential therapeutic target and suggest that high-risk patients may benefit more from immunotherapy. Future research should focus on prospective clinical validation and exploring therapeutic interventions targeting mtPCD pathways.
DOI: 10.1007/s11684-024-1093-3