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Harnessing Calmodulin-Related Genes to Build a Prognostic Model in Esophageal Squamous Cell Carcinoma for a Comprehensive Analysis of Single-Cell Immune Characteristics and Drug Efficacy

免疫系统 基因 癌症研究 比例危险模型 细胞 体细胞 食管鳞状细胞癌 基因表达谱 生物 医学 计算生物学 肿瘤科 免疫学 基因表达 内科学 遗传学
作者
Shasha Cao,Shumin Lun,Lijuan Duan,Zhaowei Gao,Xiaoxiao Wang,Yutong Li,Yaowen Zhang
出处
期刊:Journal of Immunotherapy [Lippincott Williams & Wilkins]
标识
DOI:10.1097/cji.0000000000000561
摘要

Summary: Calmodulin (CALM) has a bearing on the prognosis of various cancers. However, the prognostic value of CALM in esophageal squamous cell carcinoma (ESCC) remains unelucidated. Differentially expressed genes (DEGs) were screened between normal and tumor groups of TCGA-ESCC sets. The intersection of DEGs with calmodulin-related genes (CRGs) yielded differentially expressed CRGs (DE-CRGs). A prognostic model was established using LASSO Cox regression analysis and multivariate Cox regression analysis. qPCR validated the expression of prognostic feature genes. Analysis of gene expression patterns of different cellular clusters was based on single-cell sequencing data. Lastly, GSEA enrichment, immune infiltration, mutational profiling, drug sensitivity, and molecular docking as well as cellular thermal shift assay (CETSA) were conducted for ESCC patients. A prognosis model with excellent predictive capability was created based on 4 feature genes (ATP2B3, CALB1, KCNQ1, and MYO1G). The qPCR results demonstrated that ATP2B3 and KCNQ1 were significantly downregulated in human ESCC cells, whereas CALB1 and MYO1G were upregulated ( P <0.05). Single-cell analysis uncovered that MYO1G and KCNQ1 were mainly expressed in different cell clusters. Furthermore, this risk model was strongly associated with functional pathway enrichment, immune cell infiltration, and somatic mutations. We also identified AZD-8055 may be potential therapy for ESCC patients. The CETSA experiment demonstrated the existence of a favorable binding thermal stability between AZD-8055 and MYO1G. This research may identify potential biomarkers for predicting the prognosis of ESCC patients.
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