PDF(Pubmed)
Abstract：
In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients\' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients\' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.
摘要：
在癌症治疗中，整合肿瘤特异性特征的治疗策略(即精确肿瘤学）被广泛实施，为癌症患者提供临床益处。这里,通过肿瘤转录组和癌症患者预后的深度整合，我们调查了失调和预后相关的基因,并以癌症类型依赖的方式对这些重要基因进行了分类.利用这些基因的表达矩阵，我们建立了模型来定量评估癌症的恶性程度，这可以为临床分期系统增加价值，以改善对患者生存的预测。此外，我们对肝细胞癌进行了基于转录组的分子亚型分型，这揭示了三种亚型具有显著多样化的临床结果，突变景观，免疫微环境，和失调的途径。由于肿瘤转录组通常在临床实践中具有较低的实验复杂性和成本，这项工作提出了易于执行的方法，用于临床推广，以改善癌症患者的医疗保健和精准肿瘤学。
