Research Paper Volume 16, Issue 7 pp 6290—6313

Identifying an immunogenic cell death-related gene signature contributes to predicting prognosis, immunotherapy efficacy, and tumor microenvironment of lung adenocarcinoma

Xue Li1, *, , Dengfeng Zhang2, *, , Pengfei Guo2, *, , Shaowei Ma3, *, , Shaolin Gao2, , Shujun Li2, , Yadong Yuan1, ,

  • 1 Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
  • 2 Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
  • 3 Department of Gastrointestinal Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
* Equal contribution

Received: June 7, 2023       Accepted: March 12, 2024       Published: April 3, 2024
How to Cite

Copyright: © 2024 Li 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.


Background: Immunogenic cell death (ICD) is a regulated form of cell death that triggers an adaptive immune response. The objective of this study was to investigate the correlation between ICD-related genes (ICDGs) and the prognosis and the immune microenvironment of patients with lung adenocarcinoma (LUAD).

Methods: ICD-associated molecular subtypes were identified through consensus clustering. Subsequently, a prognostic risk model comprising 5 ICDGs was constructed using Lasso-Cox regression in the TCGA training cohort and further tested in the GEO cohort. Enriched pathways among the subtypes were analyzed using GO, KEGG, and GSVA. Furthermore, the immune microenvironment was assessed using ESTIMATE, CIBERSORT, and ssGSEA analyses.

Results: Consensus clustering divided LUAD patients into three ICDG subtypes with significant differences in prognosis and the immune microenvironment. A prognostic risk model was constructed based on 5 ICDGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of tumor purity. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor. The five hub genes were verified by TCGA database, cell sublocalization immunofluorescence analysis, IHC images and qRT-PCR, which were consistent with bioinformatics analysis.

Conclusions: The molecular subtypes and a risk model based on ICDGs proposed in our study are both promising prognostic classifications in LUAD, which may provide novel insights for developing accurate targeted cancer therapies.


ICD: immunogenic cell death; LUAD: lung adenocarcinoma; NSCLC: non-small cell lung cancer; OS: overall survival; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; CNV: copy number variation; PCA: principal component analysis; DEGs: differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene Set Enrichment Analysis; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; AUC: area under the curve; GDSC: Genome of Drug Sensitivity in Cancer; qRT-PCR: quantitative real-time polymerase chain reaction; TMB: tumor mutation burden; TIME: tumor immune microenvironment.