Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients. International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn. Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients. We established an independent prognostic model of predicting OS for 1–3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.

中文翻译：

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预测肝细胞癌总体生存的基因组-临床病理列线图

肝细胞癌（HCC）是一种常见的消化道肿瘤，具有很大的异质性和不同的总生存时间（OS），从而导致选择最佳治疗方法的严峻问题。在这里，我们旨在建立一个诺模图来预测HCC患者的OS。在我们的研究中，搜索了国际癌症基因组协会（ICGC）数据库中的目标信息。在分析过程中应用了套索回归，单变量和多元cox分析。并绘制了结合模型评分和临床特征的列线图。通过套索回归筛选出六个mRNA，以建立预测肝癌患者OS的模型。并且该模型被证明是预测HCC患者OS的独立预后模型。该模型的ROC曲线下面积（AUC）为0.803。TCGA数据库验证了这种6-mRNA模型的重要价值。最终建立包括6-mRNA风险评分，性别，年龄，肿瘤分期和先前恶性肿瘤的列线图，以预测HCC患者的OS。我们建立了一个可预测HCC患者1-3年OS的独立预后模型，该模型可用于所有人群。并且我们在此模型的基础上开发了列线图，这可能对精确地进行个体化治疗措施有很大帮助。