An m1A/m6A/m5C-associated long non-coding RNA signature: Prognostic and immunotherapeutic insights into cervical cancer

BackgroundCervical cancer (CC) remains a significant clinical challenge, even though its fatality rate has been declining in recent years. Particularly in developing countries, the prognosis for CC patients continues to be suboptimal despite numerous therapeutic advances.MethodsUsing The Cancer Genome Atlas database, we extracted CC-related data. From this, 52 methylation-related genes (MRGs) were identified, leading to the selection of a 10 long non-coding RNA (lncRNA) signature co-expressed with these MRGs. R programming was employed to filter out the methylation-associated lncRNAs. Through univariate, least absolute shrinkage and selection operator (i.e. LASSO) and multivariate Cox regression analysis, an MRG-associated lncRNA model was constructed. The established risk model was further assessed via the Kaplan-Meier method, principal component analysis, functional enrichment annotation and a nomogram. Furthermore, we explored the potential of this model with respect to guiding immune therapeutic interventions and predicting drug sensitivities.ResultsThe derived 10-lncRNA signature, linked with MRGs, emerged as an independent prognostic factor. Segmenting patients based on their immunotherapy responses allowed for enhanced differentiation between patient subsets. Lastly, we highlighted potential compounds for distinguishing CC subtypes.ConclusionsThe risk model, associated with MRG-linked lncRNA, holds promise in forecasting clinical outcomes and gauging the efficacy of immunotherapies for CC patients. Using The Cancer Genome Atlas database, cervical cancer (CC)-related data were extracted, in which 52 methylation-related genes (MRGs) were identified, leading to the selection of a 10 long non-coding RNA (lncRNA) signature co-expressed with these MRGs. Through univariate, least absolute shrinkage and selection operator (i.e. LASSO) and multivariate Cox regression analysis, an MRG-associated lncRNA model was constructed, and the established risk model was assessed via the Kaplan-Meier method, principal component analysis and Gene Ontology analysis, as well as a nomogram. Furthermore, the potential of this model in guiding immune therapeutic interventions and predicting drug sensitivities was explored, and the derived 10-lncRNA signature, linked with MRGs, emerged as an independent prognostic factor for CC patients.image