Identification of prognostic immune subtypes of lung squamous cell carcinoma by unsupervised consistent clustering.

Abstract

We performed UCC on the expression data of lung squamous cell carcinoma tumor samples to identify the classification of lung squamous cell carcinoma (LUSC) tumor samples, and calculated the levels of different classified immune cells by single-sample gene enrichment analysis (ssGSEA) to obtain a set of immune-related subtype gene tags, which can be used for subtype classification of lung squamous cell carcinoma. TCGA-LUSC and GSE30219 data of lung squamous cell carcinoma were obtained from TCGA and GEO databases. Prognostic-associated subtypes were identified by unsupervised consensus clustering (UCC). Using ssGSEA analysis to calculate the level of immune cells of different subtypes, obtain the connection between subtypes and immunity, identify the gene signatures recognized by subtypes, and verify this group of gene signatures through GSE30219. We effectively identified 2 subtypes that were significantly associated with prognostic survival by UCC, and calculated according to ssGSEA, the 2 subtypes were significantly different at the level of immune cells, followed by introducing a This weighted thinking computes a set of gene signatures that are significantly associated with subtype 1. During validation, this set of gene signatures could efficiently and robustly identify distinct prognostic immune subtypes, demonstrated the validity of this set of gene signatures, as well as 2 subtypes of lung squamous cell carcinoma. We used lung squamous cell carcinoma data from public databases and identified 2 prognostic immunosubtypes of lung squamous cell carcinoma and a set of gene tags that can be used to classify immune subtypes of lung squamous cell carcinoma, which may provide effective evidence for accurate clinical treatment of lung squamous cell carcinoma.