肝细胞癌患者中甲基化驱动基因与预处理预后因素的综合分析。

Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma.

机构信息

Department of Medical Oncology, The First Hospital of Putian, Teaching Hospital, Fujian Medical University, Putian, 351100, China.

出版信息

BMC Cancer. 2021 May 25;21(1):599. doi: 10.1186/s12885-021-08314-5.

DOI:10.1186/s12885-021-08314-5

PMID:34034705

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8146257/

Abstract

BACKGROUND

The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.

METHODS

The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson's correlation coefficient less than - 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual's OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student's t-test.

RESULTS

In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.

CONCLUSION

We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

摘要

背景

异常 DNA 甲基化的潜在可逆性为肿瘤治疗提供了机会。本研究旨在整合甲基化驱动基因和预处理预后因素，然后构建肝癌（HCC）患者的新个体预后模型。

方法

从癌症基因组图谱（TCGA）数据库下载 HCC 患者的基因甲基化、基因表达数据集和临床信息。使用 Pearson 相关系数小于-0.3 和 P 值小于 0.05 筛选甲基化驱动基因。对 HCC 患者的临床参数进行单变量和多变量 Cox 回归分析，以构建风险评分模型并确定独立的预后因素。使用最小绝对收缩和选择算子（LASSO）技术构建预测个体 OS 的列线图，然后使用 C 指数、ROC 曲线和校准图测试实用性。使用 Student's t 检验探索 HCC 患者的临床参数与核心甲基化驱动基因之间的相关性。

结果

本研究发现 44 个甲基化驱动基因，并筛选出 3 个预后标志物（LCAT、RPS6KA6 和 C5orf58），以构建 HCC 患者的预后风险模型。从 13 个临床参数中确定了 5 个临床因素，包括 T 分期、风险评分、癌症状态、手术方法和新肿瘤事件，作为预处理独立的预后因素。为避免过度拟合，使用 LASSO 分析构建了一个可以计算 HCC 患者 OS 的列线图。C 指数优于以往研究（0.75 比 0.717，0.676）。此外，LCAT 与 T 分期和新肿瘤事件相关，RPS6KA6 与 T 分期相关。