School of Medical Imaging, Xu Zhou Medical University, Xuzhou, Jiangsu, 221004, China.
Anticancer Agents Med Chem. 2021;21(14):1921-1930. doi: 10.2174/1871520621666210329100052.
Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and the prognostic value of autophagy genes in liver cancer.
The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer for establishing a prognostic model of primary liver cancer based on autophagy genes.
We identified autophagy genes related to the prognosis of liver cancer through bioinformatics methods.
Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. For optimizing the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes, after which a prognostic model was established. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO) and performed a multi-factor cox regression analysis on the 9 selected genes for constructing a prognostic model. The risk score of each patient, who participated in the establishing of the model, was calculated based on 4 genes (BIRC5, HSP8, SQSTM1, and TMEM74). Then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was assessed by the independent prognostic factors (HR = 1.872, 95% CI = 1.544 - 2.196, p < 0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. By combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting the prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer.
This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and reveal part of the molecular mechanism of liver cancer autophagy. At the same time, certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.
肝癌是世界上最常见的疾病之一。目前，肝癌自噬基因的机制尚不清楚。因此，研究自噬基因在肝癌中的作用和预后价值具有重要意义。
本研究旨在对原发性肝癌相关自噬基因进行生物信息学分析，建立基于自噬基因的原发性肝癌预后模型。
我们通过生物信息学方法鉴定与肝癌预后相关的自噬基因。
通过差异分析，筛选出 31 个差异自噬基因，然后通过 GO 和 KEGG 分析进行分析。同时，我们构建了一个 PPI 网络。为了优化肝癌患者预后的评估，我们整合了多个自噬基因，建立了一个预后模型。通过单因素 cox 回归分析，筛选出与预后相关的 15 个自噬基因。然后我们将这 15 个基因纳入最小绝对收缩和选择算子（LASSO）中，并对 9 个选定基因进行多因素 cox 回归分析，构建预后模型。根据 4 个基因（BIRC5、HSP8、SQSTM1 和 TMEM74）计算每位参与模型建立的患者的风险评分。然后将患者分为高风险组和低风险组。在多因素 cox 回归分析中，风险评分通过独立预后因素进行评估（HR = 1.872，95%CI = 1.544-2.196，p <0.001）。生存分析表明，低风险组的生存时间明显长于高风险组。通过结合临床特征和自噬基因，我们构建了预测原发性肝癌预后的列线图。外部数据集 GSE14520 证明了该列线图对原发性肝癌患者具有良好的预测能力。
本研究为肝癌患者提供了潜在的自噬相关标志物，用于预测其预后，并揭示了肝癌自噬的部分分子机制。同时，自噬相关的某些基因途径和蛋白途径可能为抗癌药物的开发提供一些启示。
