Liu Pengfei, Tian Weidong
State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.
Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, China.
PeerJ. 2020 Aug 3;8:e9654. doi: 10.7717/peerj.9654. eCollection 2020.
Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.
The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and .
Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, -value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.
The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.
肿瘤发生具有高度异质性，仅使用临床病理特征不足以有效区分透明细胞肾细胞癌（ccRCC）并改善患者的风险分层。具有稳定性和可逆性的DNA甲基化（DNAm）常发生在肿瘤发生的早期阶段。转录和代谢紊乱也是肿瘤发生的重要分子机制。因此，有必要通过多组学分析鉴定参与肿瘤发生的有效生物标志物，这些生物标志物也提供了新的潜在治疗靶点。
发现阶段涉及160对ccRCC及其匹配的正常组织，用于研究DNAm和生物标志物，以及318例包含临床特征的ccRCC。对表观遗传、转录组和代谢组数据进行相关性分析，揭示多组学之间的联系和不一致以及失调的功能模块。通过相关性分析、最小绝对收缩和选择算子（LASSO）以及LASSO - Cox方法获得诊断或预后生物标志物。在训练数据集中基于随机森林（RF）和LASSO - Cox算法建立了两个分类器。使用七个独立数据集评估其稳健性和普遍性。使用DAVID和......研究生物标志物的分子生物学功能。
基于多组学分析，表观遗传测量独特地识别出导致转录组改变的细胞机制的DNAm失调，包括细胞增殖、免疫反应和炎症。基因共表达网络和代谢网络的结合确定了134个CpG位点（CpGs）作为潜在生物标志物。基于LASSO和RF算法，获得了五个CpGs以构建具有更好分类性能（AUC > 99%）的诊断分类器。获得了一个基于八个CpG的预后分类器以改善风险分层（风险比（HR）> 4；对数秩检验，P值 < 0.01）。基于独立数据集和另外七种癌症，诊断和预后分类器也具有更好的稳健性和稳定性。甲基化异常基因的分子生物学功能与糖酵解/糖异生和信号转导显著相关。
本研究使用多组学数据对ccRCC进行了全面分析。这些发现表明多组学分析可以识别一些新的表观遗传因素，这些因素是晚期癌症和不良临床预后的最重要原因。鉴定出了诊断和预后生物标志物，为开发ccRCC的有效治疗方法提供了一条有前景的途径。
