Hepatocellular carcinoma (HCC) with metastasis indicates worse prognosis for patients. However, the current methods are insufficient to accurately predict HCC metastasis at early stage. Based on the expression profiles of three Gene Expression Omnibus datasets, the differentially expressed genes associated with HCC metastasis were screened by online analytical tool GEO2R and weighted gene co-expression network analysis. Second, a risk score model including 27-mRNA was established by univariate Cox regression analyses, time-dependent ROC curves and least absolute shrinkage and selection operator Cox regression analysis. Then, we validated the model in cohort The Cancer Genome Atlas-liver hepatocellular carcinoma and analyzed the functions and key signaling pathways of the genes associated with the risk score model. According to the risk score model, patients were divided into two subgroups (high risk and low risk groups). The metastasis rate between two subgroups was significantly different in training cohort (p < 0.0001, hazard ratio [HR]: 10.3, confidence interval [95% CI]: 6.827–15.55) and external validation cohort (p = 0.0008, HR: 1.768, 95% CI: 1.267–2.467). Multivariable analysis showed that the risk score model was superior to and independent of other clinical factors (such as tumor stage, tumor size, and other parameters) in predicting early HCC metastasis. Moreover, the risk score model could predict the overall survival of patients with HCC. Finally, most of 27-mRNA were enriched in exosome and membrane bounded organelle, and these were involved in transportation and metabolic biological process. Protein–protein interaction network analysis showed most of these genes might be key genes affecting the progression of HCC. In addition, 3 genes of 27-mRNA were also differentially expressed in peripheral blood mononuclear cell. In conclusion, by using two combined methods and a broader of HCC datasets, our study provided reliable and superior predictive model for HCC metastases, which will facilitate individual medical management for these high metastatic risk HCC patients.

中文翻译：

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生物信息学分析，以鉴定可预测肝细胞癌转移的多重mRNA信号。

转移性肝细胞癌（HCC）表明患者的预后较差。然而，目前的方法不足以在早期准确预测肝癌的转移。基于三个Gene Expression Omnibus数据集的表达谱，通过在线分析工具GEO2R和加权基因共表达网络分析筛选与肝癌转移相关的差异表达基因。其次，通过单变量Cox回归分析，时间依赖的ROC曲线以及最小绝对收缩率和选择算子Cox回归分析，建立了包括27-mRNA的风险评分模型。然后，我们在队列癌症基因组图集-肝肝癌中验证了该模型，并分析了与风险评分模型相关的基因的功能和关键信号通路。根据风险评分模型，将患者分为两个亚组（高风险和低风险组）。在训练队列中，两个亚组之间的转移率显着不同（p  <0.0001，危险比[HR]：10.3，置信区间[95％CI]：6.827–15.55）和外部验证队列（p = 0.0008，HR：1.768，95％CI：1.267-2.467）。多变量分析表明，风险评分模型在预测早期HCC转移方面优于并独立于其他临床因素（例如肿瘤分期，肿瘤大小和其他参数）。此外，风险评分模型可以预测HCC患者的整体生存率。最后，大多数27-mRNA富含外泌体和膜结合的细胞器，它们参与运输和代谢生物学过程。蛋白质间相互作用网络分析表明，这些基因中的大多数可能是影响HCC进程的关键基因。此外，在外周血单核细胞中也有27个mRNA的3个基因差异表达。总之，通过使用两种组合方法和更广泛的HCC数据集，