Zhu Kai, Tao Qiqi, Yan Jiatao, Lang Zhichao, Li Xinmiao, Li Yifei, Fan Congcong, Yu Zhengping
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wenzhou Business College, Wenzhou, China.
Front Cell Dev Biol. 2022 Sep 19;10:1020415. doi: 10.3389/fcell.2022.1020415. eCollection 2022.
Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. There is still a lack of effective biomarkers to predict its prognosis. Exosomes participate in intercellular communication and play an important role in the development and progression of cancers. In this study, two machine learning methods (univariate feature selection and random forest (RF) algorithm) were used to select 13 exosome-related genes (ERGs) and construct an ERG signature. Based on the ERG signature score and ERG signature-related pathway score, a novel RF signature was generated. The expression of BSG and SFN, members of 13 ERGs, was examined using real-time quantitative polymerase chain reaction and immunohistochemistry. Finally, the effects of the inhibition of BSG and SFN on cell proliferation were examined using the cell counting kit-8 (CCK-8) assays. The ERG signature had a good predictive performance, and the ERG score was determined as an independent predictor of HCC overall survival. Our RF signature showed an excellent prognostic ability with the area under the curve (AUC) of 0.845 at 1 year, 0.811 at 2 years, and 0.801 at 3 years in TCGA, which was better than the ERG signature. Notably, the RF signature had a good performance in the prediction of HCC prognosis in patients with the high exosome score and high NK score. Enhanced BSG and SFN levels were found in HCC tissues compared with adjacent normal tissues. The inhibition of BSG and SFN suppressed cell proliferation in Huh7 cells. The RF signature can accurately predict prognosis of HCC patients and has potential clinical value.
肝细胞癌（HCC）是预后较差的最恶性肿瘤之一。目前仍缺乏有效的生物标志物来预测其预后。外泌体参与细胞间通讯，在癌症的发生和发展中起重要作用。在本研究中，使用两种机器学习方法（单变量特征选择和随机森林（RF）算法）来选择13个外泌体相关基因（ERGs）并构建ERG特征。基于ERG特征评分和ERG特征相关通路评分，生成了一种新的RF特征。使用实时定量聚合酶链反应和免疫组织化学检测了13个ERGs成员BSG和SFN的表达。最后，使用细胞计数试剂盒-8（CCK-8）检测法检测了抑制BSG和SFN对细胞增殖的影响。ERG特征具有良好的预测性能，ERG评分被确定为HCC总生存的独立预测因子。我们的RF特征在TCGA中显示出优异的预后能力，1年时曲线下面积（AUC）为0.845，2年时为0.811，3年时为0.801，优于ERG特征。值得注意的是，RF特征在预测外泌体评分高和NK评分高的患者的HCC预后方面表现良好。与相邻正常组织相比，在HCC组织中发现BSG和SFN水平升高。抑制BSG和SFN可抑制Huh7细胞的增殖。RF特征可以准确预测HCC患者的预后，具有潜在的临床价值。
