Prediction of immune subtypes and overall survival in lung squamous cell carcinoma

Abstract

Objective

Lung squamous cell carcinoma (LUSC), one of the most common subtypes of lung cancer, is a leading cause of cancer-caused deaths in the world. It has been well demonstrated that a deep understanding of the tumor environment in cancer would be helpful to predict the prognosis of patients. This study aimed to evaluate the tumor environment in LUSC, and to construct an efficient prognosis model involved in specific subtypes.

Methods

Four expression files were downloaded from the Gene Expression Omnibus (GEO) database. Three datasets (GSE19188, GSE2088, GSE6044) were considered as the testing group and the other dataset (GSE11969) was used as the validation group. By performing LUSC immune subtype consensus clustering (CC), LUSC patients were separated into two immune subtypes comprising subtype 1 (S1) and subtype 2 (S2). Weighted gene co-expression network (WGCNA) and least absolute shrinkage and selection operator (LASSO) were performed to identify and narrow down the key genes among subtype 1 related genes that were closely related to the overall survival (OS) of LUSC patients. Using immune subtype related genes, a prognostic model was also constructed to predict the OS of LUSC patients.

Results

It showed that LUSC patients in the S1 immune subtype exhibited a better OS than in the S2 immune subtype. WGCNA and LASSO analyses screened out important immune subtype related genes in specific modules that were closely associated with LUSC prognosis, followed by construction of the prognostic model. Both the testing datasets and validation dataset confirmed that the prognostic model could be efficiently used to predict the OS of LUSC patients in subtype 1.

Conclusion

We explored the tumor environment in LUSC and established a risk prognostic model that might have the potential to be applied in clinical practice.

Keywords:

• Lung squamous cell carcinoma
• tumor environment
• immune subtypes
• overall survival

Transparency

Declaration of funding

This paper was not funded.

Declaration of financial/other relationships

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

CMRO peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Author contributions

X.S., H.G., P.C.: concept, manuscript writing, editing and review. Z.Q., T.X., G.W., H.L.: data collection and analysis, manuscript preparation. All authors have read and approved the submission of the manuscript.

Data availability statement

The analyzed data sets generated during the study are available from the corresponding author on reasonable request. By changing the clustering, we evaluated the consistency of the clustering performance of any data in different samples to determine whether the parameters of the clustering were appropriate. Pictures shown in the Supplementary file were generated when performing unsupervised consistent clustering on TCGA-lusc and GSE11969.