DataSheet1_Machine Learning Screens Potential Drugs Targeting a Prognostic Gene Signature Associated With Proliferation in Hepatocellular Carcinoma.docx

Background: This study aimed to screen potential drugs targeting a new prognostic gene signature associated with proliferation in hepatocellular carcinoma (HCC).Methods: CRISPR Library and TCGA datasets were used to explore differentially expressed genes (DEGs) related to the proliferation of HCC cells. Differential gene expression analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct a prognostic gene signature. Then the predictive power of the gene signature was validated in the TCGA and ICGC datasets. Furthermore, potential drugs targeting this gene signature were screened.Results: A total of 640 DEGs related to HCC proliferation were identified. Using univariate Cox analysis and random forest algorithm, 10 hub genes were screened. Subsequently, using multiplex combinatorial screening, five hub genes (FARSB, NOP58, CCT4, DHX37 and YARS) were identified. Taking the median risk score as a cutoff value, HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis performed in the training set showed that the overall survival of the high-risk group was worse than that of the low-risk group (p < 0.001). The ROC curve showed a good predictive efficiency of the risk score (AUC > 0.699). The risk score was related to gene mutation, cancer cell stemness and immune function changes. Prediction of immunotherapy suggetsted the IC50s of immune checkpoint inhibitors including A-443654, ABT-888, AG-014699, ATRA, AUY-922, and AZ-628 in the high-risk group were lower than those in the low-risk group, while the IC50s of AMG-706, A-770041, AICAR, AKT inhibitor VIII, Axitinib, and AZD-0530 in the high-risk group were higher than those in the low-risk group. Drug sensitivity analysis indicated that FARSB was positively correlated with Hydroxyurea, Vorinostat, Nelarabine, and Lomustine, while negatively correlated with JNJ-42756493. DHX37 was positively correlated with Raltitrexed, Cytarabine, Cisplatin, Tiotepa, and Triethylene Melamine. YARS was positively correlated with Axitinib, Fluphenazine and Megestrol acetate. NOP58 was positively correlated with Vorinostat and 6-thioguanine. CCT4 was positively correlated with Nerabine.Conclusion: The five-gene signature associated with proliferation can be used for survival prediction and risk stratification for HCC patients. Potential drugs targeting this gene signature deserve further attention in the treatment of HCC.

背景：本研究旨在筛选针对与肝细胞癌（HCC）增殖相关的新预后基因特征的目标药物。方法：利用CRISPR文库和TCGA数据集，探讨与HCC细胞增殖相关的差异表达基因（DEGs）。通过差异基因表达分析、单变量COX回归分析、随机森林算法和多组合筛选构建预后基因特征。随后，在TCGA和ICGC数据集中验证该基因特征的预测能力。此外，筛选出针对该基因特征的目标药物。结果：共识别出与HCC增殖相关的640个DEGs。通过单变量Cox分析和随机森林算法筛选出10个枢纽基因。随后，采用多组合筛选，确定了五个枢纽基因（FARSB、NOP58、CCT4、DHX37和YARS）。以中位风险分数为截断值，将HCC患者分为高风险组和低风险组。在训练集中进行的Kaplan-Meier分析表明，高风险组的总生存率低于低风险组（p < 0.001）。ROC曲线显示出风险分数良好的预测效率（AUC > 0.699）。风险分数与基因突变、癌细胞干性和免疫功能变化相关。免疫治疗预测表明，包括A-443654、ABT-888、AG-014699、ATRA、AUY-922和AZ-628在内的免疫检查点抑制剂的IC50值在高风险组中低于低风险组，而包括AMG-706、A-770041、AICAR、AKT抑制剂VIII、阿西替尼和AZD-0530在内的IC50值在高风险组中高于低风险组。药物敏感性分析表明，FARSB与羟基脲、Vorinostat、Nelarabine和洛莫司汀呈正相关，与JNJ-42756493呈负相关。DHX37与拉替瑞辛、阿糖胞苷、顺铂、替加氟和三乙烯膦呈正相关。YARS与阿西替尼、氟哌啶醇和甲地孕酮呈正相关。NOP58与Vorinostat和6-巯基鸟嘌呤呈正相关。CCT4与纳拉宾呈正相关。结论：与增殖相关的五个基因特征可用于预测HCC患者的生存率和风险分层。针对该基因特征的目标药物值得在HCC治疗中进一步关注。