Purpose: Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC.
Methods: TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways.
Results: This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response.
Conclusion: Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.
Keywords: Bioinformatics; Immune infiltration; Lung squamous cell carcinoma; Prognostic signature; Tumor microenvironment.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.