Department of Hepatobiliary Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, PR China.
Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre, Singapore 169610, Singapore.
Eur J Cancer. 2014 Mar;50(5):928-36. doi: 10.1016/j.ejca.2013.11.026. Epub 2013 Dec 11.
Identifying early stages of disease in high-risk individuals for the development of hepatocellular carcinoma (HCC) would greatly improve the clinical outcomes of these individuals. The aim of this study was to develop a blood-based gene set that could identify early-stage HCC.
Comprehensive gene expression profiling of purified RNA of peripheral blood mononuclear cells (PBMC) was performed using microarrays. Gene signatures were developed through bioinformatics-driven approaches and their diagnostic value was evaluated by custom-designed, quantitative, multiplex polymerase chain reaction (PCR) assays.
Bioinformatics-driven analysis of microarray data derived from PBMC RNA samples of patients with HCC (N=10), pancreatic cancer (N=3), gastric cancer (N=3) and 10 normal individuals identified six genes that were differentially expressed in HCC. Subsequent multiplex-PCR validation and univariate analyses performed with an independent cohort of 114 HCC patients, 48 normal individuals and 14 patients with chronic hepatitis B (CHB) validated that three genes, namely Chemokine (C-X-C motif) receptor 2 (CXCR2), C-C chemokine receptor type 2 (CCR2) and E1A-Binding Protein P400 (EP400), were able to identify HCC individually with accuracies of 82.4%, 78.4% and 65%, respectively. In combination, these three genes gave an area under the curve (AUC) of 0.96 (95% confidence interval (CI), 0.93-0.99) using multivariate logistic regression and yielded a sensitivity of 93% and a specificity of 89%. When these three genes were used in combination with alpha-fetoprotein (AFP) to predict HCC, the accuracy of predicting HCC improved slightly with an AUC of 0.99 (95% CI, 0.98-1.0), sensitivity of 93% and specificity of 95%.
CXCR2, CCR2 and EP400 can provide a promising non-invasive multiplex PCR diagnostic assay to monitor high-risk individuals for the development of HCC.
在肝细胞癌 (HCC) 发展的高危个体中识别早期疾病阶段将极大地改善这些个体的临床结局。本研究旨在开发一种基于血液的基因集,以识别早期 HCC。
使用微阵列对纯化的外周血单核细胞 (PBMC) RNA 进行综合基因表达谱分析。通过生物信息学驱动的方法开发基因特征,并通过定制的、定量的、多重聚合酶链反应 (PCR) 检测评估其诊断价值。
对来自 HCC(N=10)、胰腺癌(N=3)、胃癌(N=3)和 10 名正常个体的 PBMC RNA 样本的微阵列数据进行生物信息学驱动分析,鉴定出在 HCC 中差异表达的六个基因。随后,使用包含 114 名 HCC 患者、48 名正常个体和 14 名慢性乙型肝炎 (CHB) 患者的独立队列进行多重 PCR 验证和单变量分析,验证了三个基因,即趋化因子 (C-X-C 基序) 受体 2 (CXCR2)、C-C 趋化因子受体 2 (CCR2) 和 E1A 结合蛋白 P400 (EP400),分别以 82.4%、78.4%和 65%的准确率能够单独识别 HCC。三者结合,多变量逻辑回归的曲线下面积 (AUC) 为 0.96(95%置信区间 (CI),0.93-0.99),灵敏度为 93%,特异性为 89%。当这三个基因与甲胎蛋白 (AFP) 结合用于预测 HCC 时,使用 AUC 为 0.99(95%CI,0.98-1.0)、灵敏度为 93%和特异性为 95%的 AFP 略微提高了预测 HCC 的准确性。
CXCR2、CCR2 和 EP400 可以提供一种有前途的非侵入性多重 PCR 诊断检测方法,用于监测 HCC 发展的高危个体。