Research Paper Volume 15, Issue 19 pp 10501—10523
A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer
- 1 Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- 2 Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- 3 Department of Thoracic Surgery, Songjiang Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Preparatory Stage), Shanghai, China
- 4 Department of Otolaryngology Head and Neck Surgery, Beijing TongRen Hospital, Capital Medical University, Beijing, China
- 5 School of Clinical Medical Sciences, Southwest Medical University, Luzhou, China
Received: May 31, 2023 Accepted: September 7, 2023 Published: October 6, 2023
https://doi.org/10.18632/aging.205090How to Cite
Copyright: © 2023 Ren et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: 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).
Methods: 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.
Results: 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.
Conclusion: 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.
Abbreviations
ESCC: esophageal squamous cell carcinoma; SCRGs: SARS-CoV-2-related-genes; scRNA-seq: single-cell RNA-sequencing; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; tSNE: t-distributed Stochastic Neighbor Embedding; CNV: copy number variation; TIME: tumor immune microenvironment.