Postoperative tumor recurrence is one of the major concerns associated with the poor prognosis of HCC patients. There is yet to elucidate a standard surveillance system for HCC recurrence risk owing to complexity of this malignancy. Generation of multi-omics data from patients facilitate the identification of robust signatures for various diseases. Thus, the current study is an attempt to develop the prognostic models employing multi-omics data to significantly (p-value <0.05) stratify the recurrence high-risk (median Recurrence Free Survival time (RFS) =<12 months) and low-risk groups (median RFS >12 months). First, we identified key 90RNA, 50miRNA and 50 methylation features and developed prognostic models; attained reasonable performance (C-Index >0.70, HR >2.5), on training and validation datasets. Subsequently, we developed a prognostic (PI) model by integrating the four multi-omics features (SUZ12, hsa-mir-3936, cg18465072, and cg22852503), that are biologically inter-linked with each other. This model achieved reasonable performance on training and validation dataset, i.e. C-Index 0.72, HR of 2.37 (1.61 - 3.50), p-value of 6.72E-06, Brier score 0.19 on training dataset, and C-Index 0.72 (95% CI: 0.63 - 0.80), HR of 2.37 (95% CI: 1.61 - 3.50), p-value of 0.015, Brier score 0.19 on validation dataset. Eventually, Drugbank data was investigated to elucidate therapeutic potential of these signatures. We have identified nine potential drugs against three genes (CA9, IL1A, KCNJ15) that are positively correlated with the tumor recurrence. We anticipate these results from our study will help researchers and clinicians to improve the HCC recurrence surveillance, eventually outcome of patients.

中文翻译：

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集成多组学方法对肝细胞癌患者的肿瘤复发风险人群进行分层

术后肿瘤复发是与HCC患者预后差有关的主要问题之一。由于这种恶性肿瘤的复杂性，尚未阐明用于HCC复发风险的标准监测系统。从患者生成多组学数据有助于识别各种疾病的可靠特征。因此，当前的研究是尝试使用多组学数据来开发预后模型，以显着（p值<0.05）对复发高风险（中位无复发生存时间（RFS）= <12个月）和低复发率进行分层。风险组（中位RFS> 12个月）。首先，我们确定了关键的90RNA，50miRNA和50甲基化特征并建立了预后模型。在训练和验证数据集上获得合理的表现（C指数> 0.70，HR> 2.5）。随后，我们通过整合生物学上相互联系的四个多组学功能（SUZ12，hsa-mir-3936，cg18465072和cg22852503），开发了一种预后（PI）模型。该模型在训练和验证数据集上取得了合理的性能，即C指数0.72，HR为2.37（1.61-3.50），p值为6.72E-06，训练数据集上的Brier得分为0.19和C-Index 0.72（95％ CI：0.63-0.80），HR为2.37（95％CI：1.61-3.50），p值为0.015，在验证数据集上的Brier得分为0.19。最终，对Drugbank数据进行了研究，以阐明这些特征的治疗潜力。我们已经确定了针对三种基因（CA9，IL1A，KCNJ15）的九种潜在药物，这些基因与肿瘤复发呈正相关。