We evaluated and analysed
m6A modification patterns in 307
cervical cancer samples from The
Cancer Genome Atlas (TCGA) dataset based on 13
m6A regulators. Pearson correlation analysis was used to identify lncRNAs associated with
m6A, 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 m6A-associated lncRNAs as prognostic lncRNAs and established the prognostic profile of m6A-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
m6A modifications play an integral role in the diversity and complexity of TME formation. Assessing the
m6A 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 m6A-associated lncRNAs as a predictor of overall survival, which can also be used as a predictor of
immunotherapy.