用于预测前列腺癌患者无复发生存率的11种甲基化mRNA和3种甲基化lncRNA的特征面板
Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients.
作者信息
Cai Jiarong, Yang Fei, Chen Xuelian, Huang He, Miao Bin
机构信息
Department of Urology, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, 510630, People's Republic of China.
General Surgery Department, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, 510630, People's Republic of China.
出版信息
Pharmgenomics Pers Med. 2021 Jul 12;14:797-811. doi: 10.2147/PGPM.S312024. eCollection 2021.
BACKGROUND
Radical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis.
METHODS
Methylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis.
RESULTS
Five co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6-7 or 8-10) could be further divided into the high-risk group and the low-risk group.
CONCLUSION
These results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.
背景
根治性前列腺切除术是前列腺癌（PCa）的主要治疗方法，PCa是男性常见的癌症类型。部分患者经常会出现复发。因此，迫切需要对这些患者进行早期筛查，以专门安排辅助治疗，提高无复发生存率（RFS）。本研究旨在基于甲基化数据开发一种生物标志物，以预测PCa患者的RFS，甲基化是致癌作用的一个重要遗传因素。
方法
从癌症基因组图谱（TCGA）、基因表达综合数据库（GSE26126）和欧洲生物信息学研究所（E-MTAB-6131）下载PCa患者的甲基化表达数据。通过加权基因共表达网络分析确定稳定的共甲基化模块。模块中的基因与通过MetaDE软件包在三个数据集中筛选出的差异甲基化RNA（DMR）重叠，这些数据集用于通过最小绝对收缩和选择算子分析筛选预后基因。通过生存曲线分析评估预后特征的预后性能。
结果
在三个数据集中共保留了五个共甲基化模块。这5个模块中的192个基因与985个DMR重叠，从中鉴定出一个由11个甲基化信使RNA和3个甲基化长链非编码RNA组成的特征组。该特征组能够独立预测PCa患者的5年RFS，训练TCGA数据集的受试者操作特征曲线下面积（AUC）为0.969，测试E-MTAB-6131数据集的AUC为0.811，两者均高于 Gleason评分的预测准确性（AUC = 0.689）。此外，相同Gleason评分（6-7或8-10）的患者可进一步分为高风险组和低风险组。
结论
这些结果表明，我们的预后模型可能是一种有前景的生物标志物，可用于临床预测PCa患者的RFS。
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