Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St., Suite 820, Houston, TX, 77030, USA.
Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
BMC Genomics. 2017 Oct 3;18(Suppl 6):678. doi: 10.1186/s12864-017-4026-6.
In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC's prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).
With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.
Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients' survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.
据估计，2016 年美国将有 62700 例肾癌新发病例，14240 例患者将死于该病。由于美国肾透明细胞癌（KIRC）发病率预计将继续上升，因此迫切需要寻找有效的 KIRC 诊断生物标志物，以帮助更早地发现该疾病，并制定针对该疾病的个体化治疗策略。因此，在本研究中，我们使用反向蛋白质阵列（RPPA）数据和癌症基因组图谱（TCGA）的高通量测序数据系统地研究了 KIRC 生存预后的生物标志物。
综合 TCGA 中的全面数据，我们系统地筛选了 10 种主要癌症类型中基于蛋白表达的生存预后生物标志物，其中 KIRC 呈现出许多与生存时间相关的蛋白预后生物标志物。这与之前的一份报告一致，即表达水平变化（mRNA、miRNA 和蛋白质）可能更适合预测 KIRC 的预后。在本研究中，我们还确定了 52 个与 KIRC 预后相关的基因，其中许多基因参与细胞周期和癌症信号转导，以及 15 个肿瘤分期特异性预后生物标志物。值得注意的是，我们发现早期 KIRC 的预后生物标志物比晚期 KIRC 的少。根据蛋白和 mRNA 表达数据，我们发现 4 个生物标志物（RPPA 蛋白 ID：FASN、ACC1、Cyclin_B1 和 Rad51）对生存具有预后价值。
通过泛癌筛选，我们发现 KIRC 中许多蛋白生物标志物与患者的生存预后相关。还确定了 KIRC 中的分期特异性生存生物标志物。我们的研究表明，这些蛋白生物标志物在预测 KIRC 患者的生存和制定个体化治疗策略方面可能具有潜在的临床价值。重要的是，我们在 KIRC 的 mRNA 表达水平和蛋白表达水平上都发现了许多生物标志物。这些生物标志物具有显著的重叠，表明它们在技术上是可复制的。
