The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941–0.972; p < 0.001) with a sensitivity of 90.91%, a specificity of 92.97%. The SP.eHCC model performed well for predicting the overall survival risk of HCC patients (HR = 10.79; 95% CI: 6.16–18.89; p < 0.001). The oncogenesis of early HCC was revealed mainly involving in pathways associated with cell proliferation and tumor microenvironment. And the transcription factors including EZH2, EGR1, and SOX17 were screened in the genome networks as the promising targets used for precise treatment in patients with HCC. Our findings provide robust models for the early diagnosis and prognosis of HCC, and are crucial for the development of novel targets applied in the precision therapy of HCC.

肝癌的发作是隐性的。目前，尚无早期发现肝细胞癌（HCC）的有效方法。利用来自基因表达综合（GEO），癌症基因组图谱（TCGA），基因型组织表达（GTEx）和国际癌症基因组联合会（ICGC）数据库的826个组织样本的转录组概况，建立了早期检测和监测模型的模型。肝癌 通过弹性网和鲁棒秩次聚合（RRA）分析筛选重叠的差异表达基因（DEG），以构建早期肝癌的诊断预测模型（DP.eHCC）。通过单变量Cox回归和套索Cox回归分析筛选预后预测基因，以构建早期HCC（SP.eHCC）的生存风险预测模型。结合加权相关网络分析（WGCNA），基因组富集分析（GSEA）和基因组网络（GeNets），分析了转录组谱变化与早期HCC致癌风险评分之间的关​​系。结果显示，用于诊断早期HCC的DP.eHCC模型的AUC为0.956（95％CI：0.941-0.972；p<0.001），灵敏度为90.91％，特异性为92.97％。SP.eHCC模型在预测HCC患者的总体生存风险方面表现良好（HR = 10.79； 95％CI：6.16-18.89；p<0.001）。揭示早期肝癌的发生主要涉及与细胞增殖和肿瘤微环境有关的途径。在基因组网络中筛选了包括EZH2，EGR1和SOX17在内的转录因子，将其作为HCC患者精确治疗的有希望的靶标。我们的发现为肝癌的早期诊断和预后提供了可靠的模型，对于开发应用于肝癌精密治疗的新型靶标至关重要。