Identification and validation of a novel signature based on immune‑related genes from epithelial cells to predict prognosis and treatment response in patients with lung squamous cell cancer by integrated analysis of single‑cell and bulk RNA sequencing

Wu,Jiajun; Li,Zhifeng; Zhou,Weijun; Hu,Zhuozheng; Gao,Kun; Yang,Jin; Zhang,Wenxiong

doi:10.3892/ol.2025.14904

Identification and validation of a novel signature based on immune‑related genes from epithelial cells to predict prognosis and treatment response in patients with lung squamous cell cancer by integrated analysis of single‑cell and bulk RNA sequencing

Authors:
- Jiajun Wu
- Zhifeng Li
- Weijun Zhou
- Zhuozheng Hu
- Kun Gao
- Jin Yang
- Wenxiong Zhang
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Affiliations: Department of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330000, P.R. China, Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China
Published online on: January 23, 2025 https://doi.org/10.3892/ol.2025.14904
Article Number: 158
Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Epithelial cells are associated with tumor immunity through interstitial transformation, yet the role of epithelial immune‑related genes (EIGs) in this process remains unclear. Comprehending the mechanisms behind EIGs within lung squamous cell carcinoma (LUSC) may offer an explanation to these issues. The present study aimed to explore the biological role of EIGs in patients with LUSC. Based on data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, a survival model and nomogram was established. This model and nomogram were used to study the mechanism of EIGs in LUSC and its medical significance by enrichment analysis, tumor microenvironment, immune cell infiltration and immune function correlation analysis. Finally, reverse transcription‑quantitative PCR (RT‑qPCR) and external dataset were used to assess the expression of the EIGs. The survival model was used to develop 4 EIGs as predictors for patient outcomes. Survival curves revealed that higher risk patients had more negative outcomes. This model and the nomogram developed based entirely on this model had an accurate prognosis predictive LUSC. The enrichment analysis indicated that pathways related to antigen processing and presentation, as well as Epstein‑Barr virus infection, were prevalent in the high‑risk populations. The research on immune infiltration demonstrated a notable rise in activated dendritic cells and neutrophils in the high‑risk group. Furthermore, the results revealed that the high‑risk populations are particularly susceptible to the effects of afureserpine, gefitinib and savolitinib. Finally, the outcomes of RT‑qPCR were consistent with those of the bioinformatics analysis. In conclusion, the risk evaluation model and nomogram are effective in forecasting the prognosis and guiding drug selection for patients with LUSC. A worse prognosis in patients with high risk may be associated with certain viral infections and antigen processing and presentation.

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