Characterization of The Prognostic m6A-Related lncRNA Signature For Predicting Immune Responses In Esophageal Cancer

Abstract Background: This study aimed to construct a long non-coding RNAs (lncRNAs) signature related to N6-methyladenosine, so as to precisely forecast the prognosis of esophageal cancer patients. Methods: In this study, we downloaded RNA inference data and test data of patients with Erson skin color from the TCGA database, and passed skin-related tests, single-variation coxs and selection operator (LASSO) Cox regression to all subjects related to the subtypes and risks The results were analyzed for survival and predicted with different properties and values of risk models. Finally, we also discussed the potential immunotherapeutic characteristics and drug sensitivity prediction of the model.Results: Firstly, 93 lncRNAs related to prognosis were obtained through co-expression and univariate Cox analysis. Then we also performed least absolute shrinkage and selection operator (LASSO) analysis to obtain AC012467.2, AL161891.1,ARHGAP27P1-BPTFP1-KPNA2P3,LINC00930,POLR2J4, AP003696.1，AP003696.1 and MAGI1-AS1 to construct m6A-related lncRNA model. The subsequent consensus clustering analysis divided these samples into two parts. We observed that the survival curves of patients from different clusters and risk groups were significantly different. With the help of Kaplan-Meier survival analysis and ROC curve, we confirmed the difference of survival rates among different risk groups, and proved the prognostic value of risk signals, We observed that there were significant differences in the characteristics of immunotherapy between high-risk and low-risk groups, including the immune cell infiltration, immune gene expression, immunosuppressive points, and drug sensitivity. Conclusions: m6A-related lncRNAs prognostic signature was shaped that probably functions as vital mediators of tumor microenvironment of esophageal cancer, and might become underlying biomarkers to forecast the prognosis of esophageal cancer patients.