Yuhan Wang,* Shuang Wang,* Ran Ding, Zequn Zhang, Jing Kong, Tian Xie, Bin Xu, Liming Fu, Erli Zhang

First Hospital of Jilin University, Changchun, Jilin, 130021, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Erli Zhang, Email zel@jlu.edu.cn

Purpose: The objective of this study was to identify biomarkers associated with immunogenic cell death (ICD) in lung squamous cell carcinoma (LUSC), focusing on subtypes with distinct immunological characteristics and prognosis. Given the heterogeneous nature of LUSC, understanding ICD’s role is crucial for developing tailored therapeutic strategies.
Patients and Methods: RNA sequencing data from 504 LUSC samples were analyzed using unsupervised clustering to identify ICD-related gene expression patterns. These patterns were linked to immune scores, immune cell infiltration, and clinical outcomes. A separate dataset was used to validate the association between ICD-related subtypes and immunotherapy efficacy.
Results: Unsupervised clustering revealed two distinct ICD-related subtypes with significantly different immune scores, immune cell infiltration levels, and prognosis. A prognostic model was developed based on differentially expressed ICD-related genes, which demonstrated strong predictive power for patient survival and immune response. This model may offer valuable insights for clinical decision-making, particularly for immunotherapy strategies.
Conclusion: This study identified key ICD-related biomarkers and developed a prognostic model that can enhance our understanding of ICD in LUSC, ultimately guiding personalized treatment approaches.

Keywords: immunogenic cell death, ICD, lung squamous cell carcinoma, LUSC, tumor microenvironment, TME, prognosis prediction model