Abstract
OBJECTIVE: To study the correlation between N (6)-methyladenosine (m (6)A)-modification-associated long non-coding RNAs (lncRNAs) and poor prognosis and immunotherapy in cervical cancer based on data mining of The Cancer Genome Atlas (TCGA) cervical cancer dataset, so as to assess effectively the prognosis of cervical cancer patients and the feasibility of immunotherapy. METHODS: We identified m (6)A-modification-associated lncRNAs correlated to the prognosis of cervical cancer by conducting bioinformatics analysis of cervical cancer samples from the TCGA datasets and constructed a prognostic risk model of cervical cancer accordingly. RESULTS: A total of 343 m (6)A-modification-associated lncRNAs were identified from the samples of 304 cervical cancer patients. Univariate Cox regression analysis showed that 26 out of the 343 m (6)A-modification-associated lncRNAs were significantly associated with the prognosis of cervical cancer patients. We identified 7 m (6)A-modification-associated lncRNAs, including DLEU1, AC099850.4, DDN-AS1, EP300-AS1, AC131159.1, AL441992.2, and AL021707.6 through Lasso regression analysis and then developed a prognostic risk model based on them. According to the Kaplan-Meier survival analysis, cervical cancer patients in the low-risk group exhibited significantly improved overall survival (OS) in comparison with those in the high-risk group ( P<0.001). The area under the curve ( AUC) of receiver operating characteristic (ROC) curve analysis demonstrated the high sensitivity and credibility of the risk model. Multivariate Cox analysis showed that the risk score was an independent prognostic factor of cervical cancer patients. Tumor immune dysfunction and exclusion (TIDE) analysis predicted that the high-risk group would benefit more from immunotherapy. In addition, we found that immune checkpoint PD1 was associated with the expression of m6A-modification-related lncRNAs such as DDN-AS1, and the expression was higher in the high-risk group ( P<0.05). CONCLUSION: The prognostic risk model constructed on the basis of the aforementioned 7 m (6)A-modification-associated lncRNAs can be used to effectively predict the prognosis of cervical cancer patients and assess the efficacy of immunotherapy targeting PD1.