Li Fulei, Bai Lu, Li Shasha, Chen Yanling, Xue Xiaofei, Yu Zujiang
Department of Infectious Disease, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
J Cell Biochem. 2020 Apr 29. doi: 10.1002/jcb.29608.
Current studies indicate that long non-coding RNA (lncRNA) is often abnormally expressed in hepatocellular carcinoma (HCC). We intend to generate a multi-lncRNA signal to improve the prognosis of HCC. By analyzing 12 pairs of HCC and adjacent normal mucosal tissues, 3900 differentially expressed lncrnas were identified as candidate biomarkers for the prognosis of HCC. Then, the 12-lncrna signature was constructed using the LASSO Cox regression method and verified in the TCGA training dataset. Finally, we established a novel 12-lncrna signature that was significantly associated with overall survival (OS) in the training data set. With the use of 12-lncrna markers, patients in the training cohort were divided into high-risk and low-risk groups with significant OV differences (P < .0001). Similar results were consistent in the TCGA verification dataset (P = .046). Multivariate Cox model was used to analyze and construct the risk scores of selected key lncRNA and AJCC stages. The results showed that, compared with AJCC stages, lncRNA-based risk scores were another important factor affecting the OS of patients. We found that risk scores based on lncRNA have a stronger prediction ability than the AJCC stage alone on 4-year OS. For 4-year survival rates, prediction combined with the lncRNA risk score and AJCC stage, model effectiveness (sensitivity and specificity) has reached to 0.750. To further explore the biological processes involved in prognostic lncRNA, all HCC samples in TCGA are divided into two groups according to the median lncRNA risk score, and analyzed the gene enrichment of high expression genes and low expression genes in KEGG data using goana in limma. The results suggest that the genes associated with tumor pathways, such as PI3K-Akt and ECM-receptor interaction, are highly expressed in the high risk group.
目前的研究表明，长链非编码RNA（lncRNA）在肝细胞癌（HCC）中常异常表达。我们打算生成一个多lncRNA信号以改善HCC的预后。通过分析12对HCC及相邻正常黏膜组织，鉴定出3900个差异表达的lncRNA作为HCC预后的候选生物标志物。然后，使用LASSO Cox回归方法构建12-lncrna特征，并在TCGA训练数据集中进行验证。最后，我们建立了一个新的12-lncrna特征，其与训练数据集中的总生存期（OS）显著相关。使用12-lncrna标记，训练队列中的患者被分为高风险和低风险组，两组的OS差异显著（P < 0.0001）。在TCGA验证数据集中也得到了类似的结果（P = 0.046）。使用多变量Cox模型分析并构建所选关键lncRNA和AJCC分期的风险评分。结果表明，与AJCC分期相比，基于lncRNA的风险评分是影响患者OS的另一个重要因素。我们发现，基于lncRNA的风险评分在预测4年OS方面比单独的AJCC分期具有更强的预测能力。对于4年生存率，结合lncRNA风险评分和AJCC分期进行预测，模型有效性（敏感性和特异性）达到了0.750。为了进一步探索预后lncRNA所涉及的生物学过程，根据lncRNA风险评分中位数将TCGA中的所有HCC样本分为两组，并使用limma中的goana分析KEGG数据中高表达基因和低表达基因的基因富集情况。结果表明，与肿瘤通路相关的基因，如PI3K-Akt和ECM-受体相互作用，在高风险组中高表达。
