Identification of genes and pathways involved in kidney renal clear cell carcinoma.
Identification of genes and pathways involved in kidney renal clear cell carcinoma.
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DOI：
10.1186/1471-2105-15-s17-s2
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发表时间：
2014
影响因子：
3
通讯作者：
Yang M
中科院分区：
文献类型：
--
作者：
Yang W;Yoshigoe K;Qin X;Liu JS;Yang JY;Niemierko A;Deng Y;Liu Y;Dunker A;Chen Z;Wang L;Xu D;Arabnia HR;Tong W;Yang M
肾透明细胞癌（KIRC）是致死性泌尿生殖系统疾病之一，是最常见的恶性肾肿瘤。KIRC已显示出对放疗和化疗的耐药性。像许多类型的癌症一样，转移性KIRC没有治愈的治疗方法。利用先进的测序技术，NIH/NCI-NHGRI的癌症基因组图谱（TCGA）项目产生了大规模的测序数据，为揭示新的癌症分子机制提供了前所未有的机会。我们结合差异表达的基因、途径和网络分析，以获得对疾病发展的潜在分子机制的新见解。然后进行实验设计，获取重要基因和通路，对TCGA提供的537例KIRC患者的测序数据进行综合分析。从RNA-Seq数据中获得差异表达基因。进行通路和网络分析。我们鉴定出186个差异表达基因，P值显著且折叠变化大（P < 0.01, |log(FC)| > 5）。本研究不仅证实了文献报道中已确定的一些差异表达基因，而且提供了新的发现。我们利用本研究中鉴定的全基因组基因表达和差异表达基因进行了分层聚类分析。我们揭示了不同组的差异表达基因，可以帮助识别癌症的亚型。基于基因表达谱和差异表达基因的分层聚类分析表明，该癌症可分为四种亚型。我们发现与这些基因组相关的丰富的独特基因本体（GO）术语。基于这些发现，我们构建了基于支持向量机的监督学习分类器来预测未知样本，该分类器获得了较高的准确率和鲁棒性分类结果。此外，我们确定了一些受疾病显著影响的途径（P < 0.04）。我们发现，从文献中发现的一些已确定的途径与癌症有关，而其他途径以前未在癌症中报道过。网络分析导致识别显著中断的途径和相关基因参与疾病的发展。此外，本研究可以为确定有效的药物靶点提供一种可行的替代方法。我们的研究在肾透明细胞癌中发现了一组差异表达的基因和途径，代表了一种全面的计算方法来分析大规模的下一代测序数据。通路和网络分析表明，来自不同表达基因的信息可以用于识别异常的上游调控因子。识别明显表达的基因和改变的途径对于有效识别早期癌症诊断和治疗计划的生物标志物非常重要。使用智能计算方法将差异表达基因与途径和网络分析相结合，为识别上游疾病致病基因和有效药物靶点提供了前所未有的机会。
Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.