Research Paper Volume 16, Issue 9 pp 8110—8141
Revealing prognostic insights of programmed cell death (PCD)-associated genes in advanced non-small cell lung cancer
- 1 Senior Department of Oncology, The Fifth Medical Center of PLA General Hospital, Beijing 100071, P.R. China
- 2 Department of Oncology, The Forth Medical Center of PLA General Hospital, Beijing 100048, P.R. China
- 3 Beijing ChosenMed Clinical Laboratory Co. Ltd., Beijing 100176, P.R. China
- 4 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, P.R. China
- 5 University of the Chinese Academy of Sciences, Beijing 100049, P.R. China
Received: November 24, 2023 Accepted: March 26, 2024 Published: May 8, 2024
https://doi.org/10.18632/aging.205807How to Cite
Copyright: © 2024 Dong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
The management of patients with advanced non-small cell lung cancer (NSCLC) presents significant challenges due to cancer cells’ intricate and heterogeneous nature. Programmed cell death (PCD) pathways are crucial in diverse biological processes. Nevertheless, the prognostic significance of cell death in NSCLC remains incompletely understood. Our study aims to investigate the prognostic importance of PCD genes and their ability to precisely stratify and evaluate the survival outcomes of patients with advanced NSCLC. We employed Weighted Gene Co-expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate Cox regression analyses for prognostic gene screening. Ultimately, we identified seven PCD-related genes to establish the PCD-related risk score for the advanced NSCLC model (PRAN), effectively stratifying overall survival (OS) in patients with advanced NSCLC. Multivariate Cox regression analysis revealed that the PRAN was the independent prognostic factor than clinical baseline factors. It was positively related to specific metabolic pathways, including hexosamine biosynthesis pathways, which play crucial roles in reprogramming cancer cell metabolism. Furthermore, drug prediction for different PRAN risk groups identified several sensitive drugs explicitly targeting the cell death pathway. Molecular docking analysis suggested the potential therapeutic efficacy of navitoclax in NSCLC, as it demonstrated strong binding with the amino acid residues of C-C motif chemokine ligand 14 (CCL14), carboxypeptidase A3 (CPA3), and C-X3-C motif chemokine receptor 1 (CX3CR1) proteins. The PRAN provides a robust personalized treatment and survival assessment tool in advanced NSCLC patients. Furthermore, identifying sensitive drugs for distinct PRAN risk groups holds promise for advancing targeted therapies in NSCLC.
Abbreviations
AUC: area under the ROC curve; CI: confidence interval; DEGs: differentially expressed genes; HR: hazard ratio; ICIs: immune checkpoint inhibitors; KEGG: Kyoto Encyclopedia of Genes and Genomes; LUAD: lung adenocarcinoma; NSCLC: non-small cell lung cancers; NGS: next-generation sequencing; PD-1: programmed cell death protein-1; PD-L1: programmed cell death protein-1 ligand; TMB: tumor mutational burden; TME: tumor microenvironment; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; GO: Gene Ontology; GSEA: gene set enrichment analysis; HPA: Human Protein Atlas; IFN-γ: interferon-gamma; KEGG: Kyoto Encyclopedia of Genes and Genomes; LASSO: least absolute shrinkage and selection operator; PCA: principal component analysis; ROC: receiver operating characteristic; ssGSEA: single sample gene set enrichment analysis; WGCNA: weight gene correlation network analysis.