26468Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
School of Basic Medical Sciences, 70570Southern Medical University, Guangzhou, China.
Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211041425. doi: 10.1177/15330338211041425.
Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.
肝祖细胞 (LPCs) 在肝细胞癌 (HCC) 的发生和发展中发挥重要作用。然而，目前尚无关于 LPC 相关基因用于评估 HCC 预后的价值的研究。我们开发了 LPC 相关基因的基因特征来预测 HCC 的预后。 为了鉴定 LPC 相关基因，我们分析了包含 LPCs、成熟肝细胞和胚胎干细胞样本的数据集（GSE57812 和 GSE37071）中的 mRNA 表达谱。使用来自癌症基因组图谱 (TCGA) 的 HCC RNA-Seq 数据通过 DEG 分析和单因素 Cox 回归分析探索与预后相关的差异表达基因 (DEG)。在 TCGA 训练数据集中进行 Lasso 和多因素 Cox 回归分析构建 LPC 相关基因预后模型。在 TCGA 测试集和外部数据集（国际癌症基因组联盟 [ICGC] 数据集）中验证该模型。最后，我们研究了该预后模型与 HCC 的肿瘤-淋巴结-转移分期、肿瘤分级和血管侵犯之间的关系。 总体而言，鉴定出 1770 个 LPC 相关基因，其中 92 个基因在 HCC 组织与正常组织相比差异表达。此外，我们将 TCGA 数据集中的患者随机分配到训练和测试队列中。单因素 Cox 回归分析中 26 个 DEG 与总生存期 (OS) 相关。在 TCGA 训练集中进行 Lasso 和多因素 Cox 回归分析，构建了一个 3 基因特征来将患者分为 2 个风险组：高风险和低风险。高风险组患者的 OS 明显低于低风险组。接受者操作特征曲线分析证实了该特征的预测能力。此外，风险评分被证实是 HCC 患者的独立预测因子。 我们证明 LPC 相关基因特征可用于预测 HCC 的预后。因此，靶向 LPCs 可能成为 HCC 的治疗选择。
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