Landscape and Construction of a Novel N6-methyladenosine-related LncRNAs in Cervical Cancer

Cervical cancer is a crucial clinical problem with high mortality. Despite much research in therapy, the prognosis of patients with cervical cancer is still not ideal. The data on cervical cancer were downloaded from The Cancer Genome Atlas (TCGA) portal. R language was used to screen out the N6-methyladenosine (m6A)-related lncRNAs (long non-coding RNA). A consensus clustering algorithm was performed to identify m6A-related lncRNAs in cervical cancer; 10 m6A-related lncRNAs showing a significant association with survival were filtrated through a gradually univariate Cox regression model, least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression preliminarily. Furthermore, we conducted Kaplan-Meier curves, receiver operating curve (ROC) analyses, and proportional hazards model to quantify the underlying character of the m6A-related model in the prevision of cervical cancer patients. Gene set enrichment analysis (GSEA) was used to explore several pathways significantly. Finally, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was applied to estimate the immune cell infiltration in the profiling. In the present study, 10 m6A-related lncRNAs make up our prediction model. This prediction model can do duty for an independent predictive biomolecular element. Subsequently, we then found that the model was still valid in further validation of the training group and the test group. Our signature was correlated with immune cell infiltration and partial signaling pathway. These lncRNAs played a no negligible biomolecular role in contributing to the prognosis of cervical cancer.