Identification of m6A-related lncRNAs-based signature for predicting the prognosis of patients with skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the fastest developing malignancies with strong aggressive ability and no proper curative treatments. Numerous studies illustrated the importance of N6-methyladenosine (m6A) RNA modification to tumorigenesis. The aim of this study was to identify novel prognostic signature by using m6A-related lncRNAs, thus to improve the survival for SKCM patients and guide SKCM therapy. METHODS: We downloaded the Presentational Matrix data from The Cancer Genome Atlas (TCGA) and analyzed all the expressed lncRNAs among 468 SKCM samples. Pearson correlation analysis was performed to assess the correlations between lncRNAs and 29 m6A-related genes. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analysis were performed to construct m6A-related lncRNAs prognostic signature (m6A-LPS). The accuracy and prognostic value of this signature were validated by using receiver operating characteristic (ROC) curves, Kaplan-Meier (K-M) survival analysis, univariate COX or multivariate COX analyses. After calculating risk scores, patients were divided into low- and high-risk subgroups by the median value of risk scores. RESULTS: A total of 2973 lncRNAs were found expressed among SKCM tissues. Prognostic analysis showed that 98 lncRNAs had a significant effect on the survival of SKCM patients. The m6A-LPS was validated using K-M and ROC analysis and the predictive accuracy of the risk score was also high according to the AUC of the ROC curve in training and testing sets. A nomogram based on tumor stage, gender and risk score that had a strong ability to forecast the 1-, 2-, 3-, 5-year OS of SKCM patients confirmed by calibrations. Enrichment analysis indicated that malignancy-associated biological processes and pathways were more common in the high-risk subgroup. CONCLUSION: Collectively, m6A-related lncRNAs exert as potential biomarkers for prognostic stratification of SKCM patients and may assist clinicians achieving individualized treatment for SKCM.