M6a Regulator-Mediated RNA Methylation modification Patterns Correlated With Prognosis Value and Immune Response in Adenocarcinoma of lung

Abstract

Abstract Background: Lung adenocarcinoma (LUAD) is one of the most common subtypes of non-small cell lung cancer (NSCLC) and the main cause of death of cancer patients worldwide. N6-methyladenosine (m6A) modification and long noncoding RNAs (lncRNAs) are of significance in the prognosis and immunotherapy response of LUAD. Therefore, it is important to identify lncRNAs related to m6A modification and construct a prognostic risk model in LUAD patients. Method：The expression of writers, readers and erasers of m6A related to LUAD were get from TCGA database. The m6A-related lncRNAs were identified from TCGA by the co-expression. Univariate and multivariate Cox regression analysis screened out the m6A-related lncRNAs which were valuable for prognosis in LUAD. LUAD patients were divided into different subgroups by consistent cluster analysis. The potential biological function mechanisms of different subgroups were analyzed by KEGG enrichment, and explored the differences of tumor immune microenvironment between subgroups. Finally, the prognostic risk model of prognosis-related m6A-related lncRNA was constructed by lasso regression, and detected its effect on immune microenvironment. Results: We found that there were differences in the expression of writers, readers and erasers of m6A in LUAD tissues and adjacent normal tissues and identified abundant coexpressed lncRNAs. Thirty-two lncRNAs with prognostic value related to m6A were identified. Based on prognosis-related m6A-related lncRNAs, we divided LUAD patients into different subgroups by consistent cluster analysis, analyzed the difference in prognosis in the subgroups, and assessed the expression of m6A-related lncRNAs in different subgroups and the relationship between m6A-related lncRNAs and clinical factors. Tumor-related NOTCH, ERBB, cell cycle, MOTR, p53 and WNT signaling pathways were found to be enriched in Cluster 2 by KEGG. Moreover, we found that there was a significant correlation with immune checkpoint genes and the tumor immune microenvironment between the two clusters. A risk prognosis model was constructed by prognostic-related m6A-associated lncRNAs and further confirmed in the external cohort. Furthermore, the m6A-related risk model can effectively predict the prognosis and survival status of patients with LUAD. Finally, there are differences in risk models between immune checkpoint genes and the tumor immune microenvironment. Conclusion: Our study constructed a prognostic risk model based on m6A-related lncRNAs with independent prognostic value and revealed the role of this model and lncRNAs in the tumor immune microenvironment. This finding suggests a new development prospect forimmunotherapy strategies in LUAD.