State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai Cancer Institute, Shanghai Institute of Digestive Disease, Shanghai, China.
State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Aging (Albany NY). 2020 Mar 22;12(6):4970-4995. doi: 10.18632/aging.102923.
We aimed to develop an HCC classification model based on the integrated gene expression and methylation data of methylation-driven genes. Genome, methylome, transcriptome, proteomics and clinical data of 369 HCC patients from The Cancer Genome Atlas Network were retrieved and analyzed. Consensus clustering of the integrated gene expression and methylation data from methylation-driven genes identified 4 HCC subclasses with significant prognosis difference. HS1 was well differentiated with a favorable prognosis. HS2 had high serum α-fetoprotein level that was correlated with its poor outcome. High percentage of mutations corresponded with its activation in WNT signaling pathway. HS3 was well differentiated with low serum α-fetoprotein level and enriched in metabolism signatures, but was barely involved in immune signatures. HS3 also had high percentage of mutations and therefore enriched in WNT activation signature. HS4 was poorly differentiated with the worst prognosis and enriched in immune-related signatures, but was barely involved in metabolism signatures. Subsequently, a prediction model was developed. The prediction model had high sensitivity and specificity in distributing potential HCC samples into groups identical with the training cohort. In conclusion, this work sheds light on HCC patient prognostication and prediction of response to targeted therapy.
我们旨在基于甲基化驱动基因的基因表达和甲基化综合数据开发 HCC 分类模型。从癌症基因组图谱网络中检索并分析了 369 名 HCC 患者的基因组、甲基组、转录组、蛋白质组和临床数据。对甲基化驱动基因的综合基因表达和甲基化数据进行共识聚类，确定了 4 个具有显著预后差异的 HCC 亚类。HS1 分化良好，预后良好。HS2 具有高血清甲胎蛋白水平，与其不良预后相关。突变的高百分比与 WNT 信号通路的激活相关。HS3 分化良好，血清甲胎蛋白水平低，富含代谢特征，但几乎不参与免疫特征。HS3 也有高百分比的突变，因此富含 WNT 激活特征。HS4 分化不良，预后最差，富含免疫相关特征，但几乎不参与代谢特征。随后，建立了一个预测模型。该预测模型在将潜在 HCC 样本分配到与训练队列相同的组中时具有较高的灵敏度和特异性。总之，这项工作为 HCC 患者的预后预测和靶向治疗反应的预测提供了线索。
