Zhou Qing, Chen Qiuyan, Chen Xi, Hao Lu
Central Laboratory, People's Hospital of Baoan District, The Second Affiliated Hospital of Shenzhen University, Shenzhen, China.
Science and Education Department, Shenzhen Baoan Shiyan People's Hospital, Shenzhen, China.
Transl Androl Urol. 2021 Sep;10(9):3604-3619. doi: 10.21037/tau-21-326.
Bladder cancer (BLCA) is the most prevalent tumor affecting the urinary system, and has contributed to a rise in morbidity and mortality rates. Herein, we sought to identify the methylation-driven genes (MDGs)of BLCA in an effort to develop prognostic biomarkers suitable for the individualized assessment of patients with this particular cancer.
The Cancer Genome Atlas (TCGA) dataset was distributed into training set (n=272) and testing set(n=117). The ConsensusClusterPluspackage was used to identify BLCA subtypes. The ChAMP package was used to analyze differential methylation probe (DMP) and differential methylation region (DMR). The differentially expressed genes (DEGs) were detected using DESeq2. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to identify the pathways enriched of DEGs. Correlation analysis between 5'-C-phosphate-G-3's (CpGs) and DEGs was employed to identify the MDGs. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to build the protein-protein interaction (PPI) network of MDGs. Screening for BLCA prognosis-related MDGs and clinical features was conducted via the Cox regression model. A prognosis-related nomogram was developed and validated for prediction of the BLCA patients' survival.
We identified 2 BLCA clusters. Differential methylations at CpGs sites (dm-CpGs) were observed between cluster2 and cluster1, with 14,189 of them hypermethylated and 878 hypomethylated, predominantly in the CpG islands. In addition, a total 4,234 DEGs were identified between cluster2 and cluster1. The KEGG pathway and GO term enrichment analyses found that some DEGs were significantly enriched in multiple cancer-related pathways. A total of 33 MDGs were detected from correlation analysis between CpGs and DEGs. We selected BLCA-specific prognostic DMGs signatures for risk model development. The nomogram comprised a risk model to predict survival for BLCA patients. The efficiency of the prognostic prediction model was validated in the training and testing set.
This study discovered differential methylation patterns and MDGs in BLCA patients, which provided a bioinformatics basis for guiding BLCA early diagnosis and prognosis analyses.
膀胱癌（BLCA）是影响泌尿系统最常见的肿瘤，导致发病率和死亡率上升。在此，我们试图识别膀胱癌的甲基化驱动基因（MDGs），以开发适用于该特定癌症患者个体化评估的预后生物标志物。
将癌症基因组图谱（TCGA）数据集分为训练集（n = 272）和测试集（n = 117）。使用ConsensusClusterPlus软件包识别膀胱癌亚型。使用ChAMP软件包分析差异甲基化探针（DMP）和差异甲基化区域（DMR）。使用DESeq2检测差异表达基因（DEGs）。利用基因本体（GO）术语富集和京都基因与基因组百科全书（KEGG）通路分析来识别富含DEGs的通路。采用5'-C-磷酸-G-3'（CpGs）与DEGs之间的相关性分析来识别MDGs。使用检索相互作用基因/蛋白质的搜索工具（STRING）构建MDGs的蛋白质-蛋白质相互作用（PPI）网络。通过Cox回归模型筛选与膀胱癌预后相关的MDGs和临床特征。开发并验证了一个与预后相关的列线图，用于预测膀胱癌患者的生存情况。
我们识别出2个膀胱癌簇。在簇2和簇1之间观察到CpG位点的差异甲基化（dm-CpGs），其中14,189个高甲基化，878个低甲基化，主要位于CpG岛。此外，在簇2和簇1之间共鉴定出4,234个DEGs。KEGG通路和GO术语富集分析发现，一些DEGs在多个癌症相关通路中显著富集。通过CpGs与DEGs之间的相关性分析共检测到33个MDGs。我们选择了膀胱癌特异性预后DMG特征用于风险模型开发。该列线图包含一个预测膀胱癌患者生存的风险模型。在训练集和测试集中验证了预后预测模型的有效性。
本研究发现了膀胱癌患者的差异甲基化模式和MDGs，为指导膀胱癌的早期诊断和预后分析提供了生物信息学基础。
