Dissecting the molecular profiling and tumor immune microenvironment of three subtypes of esophageal cancer. (16th March 2023)

• Record Type: Journal Article
• Authors: Yu, Chaoqun; Xi, Yong; Zhang, Peng; He, Ningning; Shen, Weiyu
• Is Part Of: Journal of gene medicine. Volume 25:Number 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-03-16
• Subjects: cancer subtype -- drug sensitivity -- esophageal cancer -- immune microenvironment -- prognosis; Genetic transformation -- Periodicals; Gene Transfer -- Periodicals; Gene Therapy -- Periodicals; 616.042
• DOI: 10.1002/jgm.3482
• Languages: English
• ISSNs: 1099-498X

Abstract

Abstract: Background: Great improvements have been made in the prognosis of esophageal cancer (ESCA) with the application of chemotherapy and immunotherapy. However, the majority of cases remain resistant to these regimens. Hence there is an urgent need to characterize the subtypes of ESCA with favorable survival outcome and drug responsiveness. Methods: We characterized the malignant cells of ESCA and explored their communication with immune cells using the Cellchat algorithm. The ligand–receptor interaction pairs were then used as inputting information to identify the subtypes of ESCA by unsupervised clustering analysis. Further investigation aimed to dissect the different patterns of tumor immune microenvironment (TIME), tumor mutation burden, immunotherapy responsiveness and drug sensitivity among the various subtypes of ESCA. A nomogram was also constructed to predict the survival rate of ESCA patients by conducting Cox regression and decision curve analysis. Results: Three subtypes were identified based on the ligand–receptor interaction pairs. Patients in cluster 2 showed a longer survival time and less likelihood of response to immunotherapy compared with cluster 1 or 3. Eight hub genes were screened to construct a prognostic signature, which can stratify patients well into high‐ and low‐risk groups with distinct survival outcomes and drug sensitivities. The nomogram showed quite good performance in predicting patient survival rates of 1 and 3 years. Conclusion: This study characterized the molecular profiling and TIME patterns of three subtypes of ESCA. The relative findings will provide emergent insights for the treatment of ESCA. Abstract : Based on scRNA‐seq data, using ligand–receptor interaction pairs and Cellchat algorithm analysis to divide ESCA patients into three subgroups.