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Identifying Candidate Driver Genes by Integrative Ovarian Cancer Genomics Data

机译：通过综合卵巢癌基因组学数据识别候选司机基因

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Integrative analysis of molecular mechanics underlying cancer can distinguish interactions that cannot be revealed based on one kind of data for the appropriate diagnosis and treatment of cancer patients. Tumor samples exhibit heterogeneity in omics data, such as somatic mutations, Copy Number Variations CNVs), gene expression profiles and so on. In this paper we combined gene co-expression modules and mutation modulators separately in tumor patients to obtain the candidate driver genes for resistant and sensitive tumor from the heterogeneous data. The final list of modulators identified are well known in biological processes associated with ovarian cancer, such as CCL17, CACTIN, CCL16, CCL22, APOB, KDF1, CCL11, HNF1B, LRG1, MED1 and so on, which can help to facilitate the discovery of biomarkers, molecular diagnostics, and drug discovery.
机译：癌症潜在癌症的分子力学的综合分析可以区分基于一种数据无法揭示的相互作用，以适当的癌症患者的适当诊断和治疗。肿瘤样品在OMICS数据中表现出异质性，例如细胞突变，拷贝数变异CNV），基因表达谱等。本文在肿瘤患者中分别组合基因共表达模块和突变调节剂，以获得来自异构数据的抗性和敏感肿瘤的候选驾驶员基因。鉴定的调节剂的最终列表是与卵巢癌相关的生物过程中众所周知的，例如CCL17，甲酸甲酰胺，CCL16，CCL22，APOB，KDF1，CCL11，HNF1B，LRG1，MED1等，这有助于促进发现生物标志物，分子诊断和药物发现。

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