DNA methylation-related lncRNAs predict prognosis and immunotherapy response in gastric cancer.

Abstract

LncRNAs and DNA methylation are both key regulators of tumorigenesis and immune regulation. However, the interaction between lncRNA and DNA methylation, their regulation and their clinical and immune relevance in gastric cancer (GC) remain unclear. In this study, we identified DNA methylation regulator-related lncRNAs through Pearson correlation analysis in The Cancer Genome Atlas datasets. Univariate Cox regression was used to screen DNA methylationrelated prognostic lncRNAs. Further, through least absolute shrinkage and selection operator Cox regression, a prognostic model based on 13 lncRNAs was established. Survival analysis and receiver operating characteristic curve analysis verified the accuracy of the model in predicting the survival of GC patients. Univariate and multivariate analyses also confirmed that the risk score obtained from the risk model could be applied as an independent prognostic factor for patients with GC. Furthermore, based on the risk score and other clinicopathological characteristics that can be used as independent prognostic factors, we constructed a nomogram that could accurately determine the survival time of each patient. In addition, a lncRNA score was constructed using a principal component analysis algorithm to quantify the DNA methylation-related lncRNA expression patterns of individual tumors. We found that a higher lncRNA score indicated a worse the prognosis and was associated with a reduced tumor mutation burden and immunosuppression. A low lncRNA score was related to an increase in neoantigen load and an increase in the anti-PDL1/CTLA4 immunotherapy response. Additionally, a low lncRNA score was related to a significant therapeutic advantage and clinical benefit. This study describes a DNA methylation regulator-related lncRNA signature model, which provides a new approach for predicting therapeutic response and patient stratification in GC. Assessing lncRNA expression patterns in individual tumors will contribute to enhancing our understanding of tumor microenvironment infiltration and guide more effective immunotherapy strategies.