Identification of mA-related lncRNAs prognostic signature for predicting immunotherapy response in cervical cancer.

Background: N6-methylandenosine-related long non-coding RNAs (mA-related lncRNAs) play a crucial role in the cancer progression and immunotherapeutic efficacy. The potential function of mA-related lncRNAs signature in cervical cancer has not been systematically clarified.

Methods: RNA-seq and the clinical data of cervical cancer were extracted from The Cancer Genome Atlas. All of the patients were randomly classified into training and testing cohorts. The mA-related lncRNAs prognostic model was constructed by LASSO regression using data in the training cohort.The predictive value of the signature was validated in the whole cohort and testing cohort. Cervical cancer patients were divided into low- and high-risk subgroups by the median value of risk scores. Kaplan-Meier analysis, principal-component analysis (PCA), functional enrichment annotation, and nomogram were used for further evaluation. We also examined the immune response and potential drug sensitivity targeting this model.

Results: Seventy-nine prognostic mA-related lncRNAs were screened. The risk model comprising four mA-related lncRNAs (AL139035.1, AC015922.2, AC073529.1, AC008124.1) was identified and verified as an independent prognostic predictor of cervical cancer. A nomogram based on age, tumor grade, clinical stage, TNM stage, and four mA-related lncRNAs risk signatures was generated. It displayed good accuracy and reliability in predicting the overall survival of patients with CC. Based on our risk model, cervical cancer patients with potential immunotherapy benefits from the candidate drugs could be effectively screened.

Conclusion: The four mA-related lncRNAs signature may provide new targets and allow the prediction of immunotherapy response, which can assist developing individualized treatment for cervical cancer.