Additional file 1 of A lipid metabolism–based prognostic risk model for HBV–related hepatocellular carcinoma

Additional file 1: Fig. S1. Feature selection by LASSO logistic regression. Fig. S2. The prognostic contributions of eleven marker genes in the risk model. Fig. S3.Survival difference analysis between hbv + and hbv- HCC patients in the TCGA cohort. Fig. S4. Expression patterns comparison of 11 marker genes between hbv + HCC and normal samples. (A-B) The mRNAs expression level of 11 genes in the TCGA and Gao et al. cohorts, respectively. (C) The protein expression level of 11 genes in Gao et al. cohort. Fig. S5. Survival analysis of high- and low-risk groups. Fig. S6. Independent prognostic prediction analysis of our risk model. Fig. S7. Immune cells infiltration difference between high- and low-risk groups quantified by cibersort algorithm. Fig. S8. Functional enrichment analysis. Fig. S9. Analysis of immune gene expression difference in high- and low-risk groups. Fig. S10. TMB and intratumor genetic heterogeneity difference between high- and low-risk groups.附加文件1：图S1：基于LASSO逻辑回归的特征筛选。图S2：风险模型中11个标记基因的预后贡献。图S3：TCGA队列中乙型肝炎病毒（hepatitis B virus, HBV）阳性与HBV阴性肝细胞癌（hepatocellular carcinoma, HCC）患者的生存差异分析。图S4：HBV阳性HCC组织与正常样本间11个标记基因的表达模式比较：(A-B) 分别为TCGA队列及Gao等队列中11个基因的mRNA表达水平；(C) Gao等队列中11个基因的蛋白表达水平。图S5：高低风险组的生存分析。图S6：本研究风险模型的独立预后预测分析。图S7：基于CIBERSORT算法量化的高低风险组免疫细胞浸润差异。图S8：功能富集分析。图S9：高低风险组免疫基因表达差异分析。图S10：高低风险组肿瘤突变负荷（tumor mutation burden, TMB）及瘤内遗传异质性差异分析。