m6A修饰相关lncRNA与宫颈癌的预后不良及免疫治疗的相关性分析

  目的   本研究旨在通过挖掘宫颈癌TCGA数据库，分析N6-甲基腺嘌呤（N6-methyladenosine, m6A）修饰相关长链非编码RNA（lncRNA）与宫颈癌预后不良及免疫治疗的相关性，从而有效地评估宫颈癌患者的预后和宫颈癌免疫治疗的可行性。   方法   基于TCGA数据库宫颈癌样本，利用生物信息学的方法鉴定与宫颈癌预后相关的m6A修饰相关lncRNA，并以此构建宫颈癌的预后风险模型。   结果   从304例患者样本中筛选出343个m6A修饰相关lncRNA，通过单因素Cox回归分析得到26个m6A修饰相关lncRNA与宫颈癌患者预后相关，并利用Lasso回归分析得到7个m6A修饰相关lncRNA（DLEU1、AC099850.4、DDN-AS1、EP300-AS1、AC131159.1、AL441992.2、AL021707.6）用以构建预后风险模型。Kaplan-Meier曲线显示低风险组的OS高于高风险组（P<0.001）；ROC曲线下面积（AUC）表明本风险模型准确性高、可信度强；多因素Cox分析显示风险评分是评估宫颈癌患者预后的独立因素。TIDE评分预测高风险组接受免疫治疗后获益更大。免疫检查点PD1与DDN-AS1等m6A修饰相关lncRNA表达相关，且在高风险组中表达更高（P<0.05）。   结论   基于上述7个m6A修饰相关lncRNA构建的预后风险模型能够有效预测宫颈癌患者的预后，并能评估以PD1为靶点的免疫治疗疗效。

Correlation analysis of Poor Prognosis and Immunotherapy of lncRNAs Related with m6A Modification in Cervical Cancer

  Objective   To study the correlation between N6-methyladenosine (m6A)-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 m6A-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 m6A-modification-associated lncRNAs were identified from the samples of 304 cervical cancer patients. Univariate Cox regression analysis showed that 26 out of the 343 m6A-modification-associated lncRNAs were significantly associated with the prognosis of cervical cancer patients. We identified 7 m6A-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 m6A-modification-associated lncRNAs can be used to effectively predict the prognosis of cervical cancer patients and assess the efficacy of immunotherapy targeting PD1.