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Guoping Sun

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Open access Jul 2026

Immune‐Related Prognostic Gene Identification in Lung Squamous Cell Carcinoma With the Cancer Genome Atlas Mining and Immunohistochemistry Validation

In the cancer microenvironment, stromal and immune cells hold clinical importance. In lung squamous cell carcinoma (LSCC), the aim of this study was to find immune‐linked gene expression that has prognostic pertinence. From the Cancer Genome Atlas, this study obtained the LSCC gene expression profile. The Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data algorithm was applied to derive stromal and immune scores for the included cases. Stromal and immune scores were not statistically associated with sex or tumor stage, whereas a borderline significant association was observed between the immune score and smoking status ( p  = 0.0614). Interestingly, the immune score showed an independent association with overall survival in LSCC as shown by multivariate analyses. There were 517 differentially expressed genes (DEGs) linked to immune scores in total, 42 of which were upregulated. The DEGs were commonly linked to inflammatory and immune responses, extracellular exosomes, and chemokine activities. Cox analyses revealed that 21 DEGs were significantly related to overall survival (OS) in LSCC. Through validation, four genes (AP1S2, CLEC10A, FBXO2, and IQGAP2) were found to be significant OS predictors in independent Gene Expression Omnibus (GEO) datasets. However, immunohistochemistry (IHC) validation showed inconsistent prognostic trends for AP1S2 compared with bioinformatic predictions, whereas CLEC10A exhibited no differential expression between tumor and adjacent tissues. FBXO2 and IQGAP2 maintained consistent associations with OS. Multivariate Cox regression confirmed several of these genes as independent prognostic factors for LSCC. The study refines immune score‐associated DEGs and identifies independent prognostic factors in LSCC, and it provides tissue‐level evidence that may support future prognostic evaluation.

Weijia Jiang, Jing Xu, Wenwen Ma et al. · 0 citations