Dataset Information

Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma.

ABSTRACT:

Objectives

In this study, we aimed to identify the metabolic genes associated with the metastasis and immunotherapy resistance of oral squamous cell carcinoma (OSCC) and to construct a metabolic gene-related predictive model for the prognosis of OSCC.

Methods

RNA-seq data were download from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the modules related to EMT, stemness, and checkpoint signatures in OSCC. Univariate Cox and the least absolute shrinkage and selection operator (LASSO) methods were used to construct the metabolic gene signature. Furthermore, the scRNA-seq data were obtained from Gene Expression Omnibus (GEO) database and analyzed using "Seurat" and "CopyKAT" packages.