Dataset for: Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma

Molecular markers are not used in the determination of prognosis and treatment for HCC patients. We proposed that the identification of the common pro-oncogenic pathways in primary tumor (PT) and adjacent non-malignant tissues (AT) predicting HCC patient risks result in the HCC marker discovery. We examined the genome-wide mRNA expression profiles of paired PT and AT samples from 321 HCC patients. The workflow integrated differentially expressed gene selection, gene ontology enrichment, computational classification, survival predictions, image analysis and experimental validation methods. We developed a 24-ribosomal gene-based HCC classifier (RGC), which are prognostically significant in both PT and AT. The RGC gene overexpression in PT was associated with a poor prognosis in the training (HR=8.2, p=9.4E-06) and cross-cohort validation (HR=2.63, p=0.004) datasets. The multivariate survival analysis demonstrated the significant and independent prognostic value of the RGC. The RGC displayed a significant prognostic value in AT of the training (HR=5.0, p=0.03) and cross-validation (HR=1.9, p=0.03) HCC groupsconfirming the accuracy and robustness of RGC. Our experimental and bioinformatics analyses suggested a key role for c-MYC in the pro-oncogenic pattern of ribosomal biogenesis co-regulation in PT and AT. Microarray, qRT-PCR and quantitative immunohistochemical studies of the PT showed that DKK1 in PT is the perspective biomarker for poor HCC outcomes. The common co-transcriptional patterns of ribosome biogenesis genes in PT and AT from HCC patients suggests a new scalable prognostic system, supported by the model of tumor-like metabolic redirection/assimilation in non-malignant AT. The RGC, comprising 24 ribosomal genes, is introduced as a robust and reproducible prognostic model for stratifying HCC patient risks. The adjacent non-malignant liver tissue alone or in combination with HCC tissue biopsy could be an important target to develop predictive and monitoring strategies and evidence-based therapeutic interventions to reduce the risk of post-surgery relapse of HCC patients.

目前尚无分子标志物用于肝细胞癌（Hepatocellular Carcinoma, HCC）患者的预后判断与治疗方案制定。本研究提出，通过识别原发性肿瘤（Primary Tumor, PT）与癌旁非恶性组织（Adjacent Non-malignant Tissue, AT）中共有的促癌通路以预测肝细胞癌患者风险，可助力肝细胞癌标志物的发掘。我们对321例肝细胞癌患者的配对原发性肿瘤与癌旁非恶性组织样本开展了全基因组mRNA表达谱分析。本研究的分析流程整合了差异表达基因筛选、基因本体（Gene Ontology, GO）富集分析、计算分类、生存预测、图像分析与实验验证等方法。我们构建了基于24个核糖体基因的肝细胞癌分类器（Ribosomal Gene-based HCC Classifier, RGC），该分类器在原发性肿瘤与癌旁非恶性组织中均具有显著的预后价值。在训练集（风险比HR=8.2，p=9.4E-06）与跨队列验证集（HR=2.63，p=0.004）中，原发性肿瘤内核糖体基因的高表达与不良预后显著相关。多变量生存分析证实，该分类器具有独立且显著的预后价值。在训练集（HR=5.0，p=0.03）与跨队列验证集（HR=1.9，p=0.03）的癌旁非恶性组织中，该分类器同样展现出显著的预后价值，证实了其准确性与稳健性。本研究的实验与生物信息学分析表明，c-MYC在原发性肿瘤与癌旁非恶性组织的核糖体生物发生共调控促癌模式中发挥关键作用。对原发性肿瘤的微阵列、qRT-PCR与定量免疫组化研究显示，原发性肿瘤中的DKK1是预测肝细胞癌不良结局的潜在生物标志物。肝细胞癌患者原发性肿瘤与癌旁非恶性组织中核糖体生物发生基因共有的转录模式，为基于非恶性癌旁组织的肿瘤样代谢重定向/同化模型的新型可扩展预后系统提供了理论支撑。本研究提出的由24个核糖体基因组成的RGC，是一款稳健且可重复的预后模型，可用于肝细胞癌患者的风险分层。单独或联合肝细胞癌组织活检的癌旁非恶性肝组织，可作为开发预测与监测策略、以及基于证据的治疗干预手段的重要靶点，以降低肝细胞癌患者术后复发风险。