文献类型：期刊文献

英文题名：ER stress-related mRNA-lncRNA co-expression gene signature predicts the prognosis and immune implications of esophageal cancer

作者：Li, Feng[1];Ma, Jiahao[1];Yan, Cheng[1];Qi, Yonghua[1]

第一作者：李飞

通讯作者：Yan, C[1];Qi, YH[1]

机构：[1]Xinxiang Univ, Diagnost Lab Anim Dis, Key Lab Nanocarbon Modified Film Technol Henan Pr, Sch Pharm, Xinxiang, Henan, Peoples R China

第一机构：新乡学院

通讯机构：[1]corresponding author), Xinxiang Univ, Diagnost Lab Anim Dis, Key Lab Nanocarbon Modified Film Technol Henan Pr, Sch Pharm, Xinxiang, Henan, Peoples R China.|[11071]新乡学院;

年份：2022

卷号：14

期号：11

起止页码：8064-+

外文期刊名：AMERICAN JOURNAL OF TRANSLATIONAL RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000965154300014)】；

基金：Thanks to Xinxiang University participants and staff. The Key Scientific Research Projects of Henan Colleges and Universities (Grant No. 21A310008), Natural Science Foundation for Young Scientists of Henan Province (Grant No. 222300420261) and the Natural Science Foundation of Henan Province (222300420510).

语种：英文

外文关键词：Esophageal cancer; endoplasmic reticulum stress; mRNA; lncRNA; prognostic model

摘要：Background: Esophageal cancer (EC) is one of the most common malignant cancers in the world. Endoplasmic reticulum (ER) stress is an adaptive response to various stress conditions and has been implicated in the development of various types of cancer. Long noncoding RNAs (lncRNAs) refer to a group of noncoding RNAs (ncRNAs), which regulate gene expression by interacting with DNA, RNA and proteins. Accumulating evidence suggests that lncRNAs are critical regulators of gene expression in development, differentiation, and human diseases, such as cancers and heart diseases. However, the prognostic model of EC based on ER stress-related mRNA and lncRNA has not been reported. Methods: Firstly, we downloaded RNA expression profiles from The Cancer Genome Atlas (TCGA) and obtained ER stress-related genes from the Molecular Signature Database (MSigDB). Next, Weighted Correlation Network Analysis (WGCNA) co-expression analysis was used to identify survival-related ER stress-related modules. Prognostic models were developed using univariate and Least absolute shrinkage and selection operator (LASSO) regression analyses on the training set and validated on the test set. Afterwards, The Receiver Operating Characteristic (ROC) curve and nomogram were used to evaluate the performance of risk prediction models. Differentially expressed gene (DEG) and enrichment analysis were performed between different groups in order to identify the biological processes correlated with the risk score. Finally, the fraction of immune cell infiltration and the difference of tumor microenvironment were identified in high-risk and low-risk groups. Results: The WGCNA co-expression analysis identified 49 ER genes that are highly associated with EC prognosis. Using univariate Cox regression and LASSO regression analysis, we developed prognostic risk models based on nine signature genes (four mRNAs and five lncRNAs). Both in the training and in the test sets, the overall survival (OS) of EC patients in the high-risk group was significantly lower than that in the low-risk group. The Kaplan-Meier curve and the ROC curve demonstrate the prognostic model we built can precisely predict the survival with more than 70% accuracy. The correlation analysis between the risk score and the infiltration of immune cells showed that the model can indicate the state of the immune microenvironment in EC. Conclusion: In this study, we developed a novel prognostic model for esophageal cancer based on ER stress-related mRNA-lncRNA co-expression profiles that could predict the prognosis, immune cell infiltration, and immunotherapy response in patients with EC. Our results also may provide clinicians with a quantitative tool to predict the survival time of patients and help them individualize treatment strategies for the patients with EC.