Construction of a prognostic model for hepatocellular carcinoma based on immune and metabolism related genes and drug prediction
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摘要:目的
构建基于免疫和代谢相关基因的肝细胞癌(HCC)预后预测模型，分析HCC患者的预后免疫反应，并通过药物敏感性分析筛选治疗HCC的潜在药物。
方法从癌症基因组图谱(TCGA)数据库获得HCC表达谱数据及临床数据，从Immport数据库获取免疫相关基因列表; 采用Perl语言从分子签名数据库(MSig DB)提取代谢相关通路基因集，通过差异分析和共表达分析找到共表达相关基因; 采用单因素Cox回归分析、最小绝对收缩选择算子(LASSO)回归分析和多因素Cox回归分析筛选预后相关基因并构建HCC的风险预后模型，计算所有HCC患者样本的风险评分。以风险评分的中位值为临界值，通过风险曲线、Kaplan-Meier生存分析、受试者工作特征(ROC)曲线、独立预后分析、列线图评估预后模型的可靠性。分析风险评分与通路富集分析、免疫细胞浸润的相关性。采用药物敏感性分析获取HCC潜在治疗药物。
结果获得5个有独立预后价值的免疫与代谢基因，构建了一个基于免疫和代谢基因的预后模型。生存分析显示，总数据集、训练组和验证组中，低风险组的生存率均高于高风险组，差异有统计学意义(P < 0.05)。训练组的预后模型1、3、5年的ROC曲线的曲线下面积分别为0.780、0.699、0.706。Cox回归分析显示分级和风险评分可以作为HCC的独立预后影响因素(P < 0.05), 一致性指数为0.734(95%CI: 0.669~0.798)，模型性能较好。免疫细胞浸润结果显示，静息NK细胞、单核细胞、M0巨噬细胞、M1巨噬细胞在高低风险组中存在显著差异(P < 0.05)。药物敏感性分析筛选得到12种可能对HCC患者具有潜在治疗效果的药物(P < 0.01)。
结论基于5个免疫和代谢基因构建的HCC预后模型的预测性能较好，可以作为评价预后的新指标; 筛选得到的12种药物对HCC具有潜在疗效。
Abstract:ObjectiveTo construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on immune and metabolism related genes, analyze the prognostic immune response of HCC patients, and screen potential drugs for HCC treatment through drug sensitivity analysis.
MethodsHCC expression profiling and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, and a list of immune-related genes was obtained from the Immport database; the Perl language was used to extract metabolism-related pathway gene sets from the Molecular Signatures Database(MSig DB), and co-expression related genes were found through differential analysis and co-expression analysis; the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to screen prognosis-related genes and construct a risk prognosis model for HCC, and risk scores for all HCC samples were calculated. Using the median risk score as the critical value, the reliability of the prognostic model was evaluated through risk curves, Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, independent prognostic analysis, and Nomograms. The correlations between risk scores and pathway enrichment analysis as well as immune cell infiltration were analyzed. Drug sensitivity analysis was used to identify potential therapeutic drugs for HCC.
ResultsFive immune and metabolic genes with independent prognostic value were obtained, and a prognostic model based on immune and metabolic genes was constructed. Survival analysis showed that in the total dataset, training group and validation group, the survival rate of the low-risk group was significantly higher than that ofthe high-risk group (P < 0.05). The areas under the ROC curves of the prognostic model for the training group at 1, 3 and 5 years were 0.780, 0.699 and 0.706 respectively. Cox regression analysis showed that grading and risk score could be used as independent prognostic factors for HCC (P < 0.05), with a concordance index of 0.734 (95%CI, 0.669 to 0.798), indicating good model performance. Immune cell infiltration results showed significant differences in resting NK cells, monocytes, M0 macrophages, and M1 macrophages between the high-risk and low-risk groups (P < 0.05). Drug sensitivity analysis screened 12 drugs that may have potential therapeutic effects in HCC patients (P < 0.01).
ConclusionThe prognostic model of HCC based on five immune and metabolic genes has good predictive performance, and can be used as a new indicator for prognosis evaluation; the 12 drugs screened out have potential efficacy for HCC.
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图 2 肝细胞癌预后显著基因
A: 单因素Cox回归筛选的预后显著基因(前20个); B: Lasso回归中的Coef系数剖面图; C: Lasso回归中调整参数进行10倍交叉验证。
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图 2 肝细胞癌预后显著基因
A: 单因素Cox回归筛选的预后显著基因(前20个); B: Lasso回归中的Coef系数剖面图; C: Lasso回归中调整参数进行10倍交叉验证。
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图 3 风险评分图、生存状态散点图和基因表达热图
A、B、C: 总数据集、训练组和验证组的风险评分图; D、E、F: 总数据集、训练组和验证组的生存状态散点图; G、H、I: 总数据集、训练组和验证组的热图。
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图 3 风险评分图、生存状态散点图和基因表达热图
A、B、C: 总数据集、训练组和验证组的风险评分图; D、E、F: 总数据集、训练组和验证组的生存状态散点图; G、H、I: 总数据集、训练组和验证组的热图。
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图 10 药物敏感性分析
A: 唑来膦酸敏感性; B: 阿法替尼敏感性; C: 阿培利司敏感性; D: AZD7762(一种细胞周期检测点激酶抑制剂)敏感性; E: 硼替佐米敏感性; F: 氟维司群敏感性; G: 达沙替尼敏感性; H: 奥希替尼敏感性; I: 紫杉醇敏感性; J: 星孢菌素敏感性; K: 维奈托克敏感性; L: 长春瑞滨敏感性。
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图 10 药物敏感性分析
A: 唑来膦酸敏感性; B: 阿法替尼敏感性; C: 阿培利司敏感性; D: AZD7762(一种细胞周期检测点激酶抑制剂)敏感性; E: 硼替佐米敏感性; F: 氟维司群敏感性; G: 达沙替尼敏感性; H: 奥希替尼敏感性; I: 紫杉醇敏感性; J: 星孢菌素敏感性; K: 维奈托克敏感性; L: 长春瑞滨敏感性。
表 1 总数据集、验证组与训练组临床资料比较[n(%)]
临床数据 分类 总数据集(n=370) 验证组(n=111) 训练组(n=259) P 年龄 ≤65岁 232(62.70) 67(60.36) 165(63.71) 0.622 >65岁 138(37.30) 44(39.64) 94(36.29) 分级 G1 55(14.86) 13(11.71) 42(16.22) 0.582 G2 177(47.84) 53(47.75) 124(47.88) G3 121(32.70) 38(34.24) 83(32.05) G4 12(3.24) 5(4.50) 7(2.70) 未分级 5(1.36) 2(1.80) 3(1.15) 分期 Ⅰ期 171(46.22) 57(51.35) 114(44.02) 0.407 Ⅱ期 85(22.97) 21(18.92) 64(24.71) Ⅲ期 85(22.97) 22(19.82) 63(24.32) Ⅳ期 5(1.35) 1(0.90) 4(1.54) 未分期 24(6.49) 10(9.01) 14(5.41) 
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表 1 总数据集、验证组与训练组临床资料比较[n(%)]
临床数据 分类 总数据集(n=370) 验证组(n=111) 训练组(n=259) P 年龄 ≤65岁 232(62.70) 67(60.36) 165(63.71) 0.622 >65岁 138(37.30) 44(39.64) 94(36.29) 分级 G1 55(14.86) 13(11.71) 42(16.22) 0.582 G2 177(47.84) 53(47.75) 124(47.88) G3 121(32.70) 38(34.24) 83(32.05) G4 12(3.24) 5(4.50) 7(2.70) 未分级 5(1.36) 2(1.80) 3(1.15) 分期 Ⅰ期 171(46.22) 57(51.35) 114(44.02) 0.407 Ⅱ期 85(22.97) 21(18.92) 64(24.71) Ⅲ期 85(22.97) 22(19.82) 63(24.32) Ⅳ期 5(1.35) 1(0.90) 4(1.54) 未分期 24(6.49) 10(9.01) 14(5.41)
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表 2 多因素Cox回归纳入的预后基因
基因 系数 P Exp(B) Exp(B)的95%CI 下限 上限 FABP6 0.228 0.008 1.256 0.974 1.618 MAPT 0.507 0.001 1.66 1.242 2.219 GAL 0.315 0.007 1.37 1.089 1.725 PPAT 0.581 0.001 1.788 1.266 2.526 IMPDH1 0.324 0.005 1.383 1.105 1.732
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表 2 多因素Cox回归纳入的预后基因
基因 系数 P Exp(B) Exp(B)的95%CI 下限 上限 FABP6 0.228 0.008 1.256 0.974 1.618 MAPT 0.507 0.001 1.66 1.242 2.219 GAL 0.315 0.007 1.37 1.089 1.725 PPAT 0.581 0.001 1.788 1.266 2.526 IMPDH1 0.324 0.005 1.383 1.105 1.732
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