N6-Methyladenosine-Related lncRNAs as potential biomarkers for predicting prognoses and immune responses in patients with cervical cancer

Background Several recent studies have confirmed epigenetic regulation of the immune response. However, the potential role of RNA N6-methyladenosine (m 6 A) modifications in cervical cancer and tumour microenvironment (TME) cell infiltration remain unclear. Results We evaluated and analysed m 6 A modification patterns in 307 cervical cancer samples from The Cancer Genome Atlas (TCGA) dataset based on 13 m 6 A regulators. Pearson correlation analysis was used to identify lncRNAs associated with m 6 A, followed by univariate Cox regression analysis to screen their prognostic role in cervical cancer patients. We also correlated TME cell infiltration characteristics with modification patterns. We screened six m 6 A-associated lncRNAs as prognostic lncRNAs and established the prognostic profile of m 6 A-associated lncRNAs by least absolute shrinkage and choice of operator (LASSO) Cox regression. The corresponding risk scores of the patients were derived based on their prognostic features, and the correlation between this feature model and disease prognosis was analysed. The prognostic model constructed based on the TCGA-CESC (The Cancer Genome Cervical squamous cell carcinoma and endocervical adenocarcinoma) dataset showed strong prognostic power in the stratified analysis and was confirmed as an independent prognostic indicator for predicting the overall survival of patients with CESC. Enrichment analysis showed that biological processes, pathways, and markers associated with malignancy were more common in the high-risk subgroup. Risk scores were strongly correlated with the tumour grade. ECM receptor interactions and pathways in cancer were enriched in Cluster 2, while oxidative phosphorylation and other biological processes were enriched in Cluster 1. The expression of immune checkpoint molecules, including programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1), was significantly increased in the high-risk subgroup, suggesting that this prognostic model could be a predictor of immunotherapy. Conclusions This study reveals that m 6 A modifications play an integral role in the diversity and complexity of TME formation. Assessing the m 6 A modification patterns of individual tumours will help improve our understanding of TME infiltration characteristics and thus guide immunotherapy more effectively. We also developed an independent prognostic model based on m 6 A-associated lncRNAs as a predictor of overall survival, which can also be used as a predictor of immunotherapy.