A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer.

Abstract

The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC). Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature. Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy. The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.