A scoring model for the expression of genes related to programmed cell death predicts immunotherapy response and prognosis in lung adenocarcinoma

Abstract

Abstract Background Lung adenocarcinoma (LUAD) continues to be the leading cause of cancer death worldwide, driven by environmental factors like smoking and genetic predispositions. LUAD has a high mortality rate, and new biomarkers are urgently needed to improve treatment strategies and patient management. Programmed cell death (PCD) is involved in tumor progression and response to treatment. Therefore, there is a need for an extensive study of the role and functions of PCD-related genes (PCDRGs) in lung adenocarcinoma so as to understand the pathophysiologic features of lung adenocarcinoma. Methods Based on TCGA and GEO databases, this research is aimed at screening differentially expressed PCD-related genes in lung adenocarcinoma. We conducted GO, and KEGG analysis to establish the link between these genes and biological processes. By applying various machine learning algorithms such as CoxBoost analysis, we developed PCD-related indices (PCDI) that were used to verify their ability to predict prognosis with the use of other datasets. This was done in addition to exploring the biological functions of PCD genes associated with lung adenocarcinoma by assessing the relationship between immune cell components of tumor microenvironment and PCD genes together with examining how they affect drug sensitivity. Results The research presented in this article offers significant insights into LUAD. The authors identified 113 PCDRGs that were differentially expressed in LUAD. These genes are implicated in various biological functions, including High risk ing apoptosis, ferroptosis, and pathways specific to non-small cell lung cancer. Notably, the PCDI proved effective in distinguishing between High risk and Low risk LUAD patients, demonstrating a higher accuracy in prognosis prediction compared to traditional clinical indicators such as age and gender. This high prediction accuracy was validated in both test and validation cohorts. Additionally, these genes showed significant correlations with immune cell infiltration and drug sensitivity in LUAD patients. Conclusion We analysed the expression and function of PCDRGs in LUAD and revealed their correlation with patient survival, the immune microenvironment and drug sensitivity. The constructed PCDI model provides a scientific basis for the personalised treatment of lung adenocarcinoma, and future optimisation of treatment strategies based on these genes may improve patient clinical outcomes.

Keywords

• Lung adenocarcinoma
• Programmed cell death
• Gene expression regulation
• Neoplastic
• Biomarkers
• Pharmacological