School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.
Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan.
Int J Mol Sci. 2017 Nov 6;18(11):2345. doi: 10.3390/ijms18112345.
The clinical characteristics of clear cell carcinoma (CCC) and endometrioid carcinoma EC) are concomitant with endometriosis (ES), which leads to the postulation of malignant transformation of ES to endometriosis-associated ovarian carcinoma (EAOC). Different deregulated functional areas were proposed accounting for the pathogenesis of EAOC transformation, and there is still a lack of a data-driven analysis with the accumulated experimental data in publicly-available databases to incorporate the deregulated functions involved in the malignant transformation of EOAC. We used the microarray gene expression datasets of ES, CCC and EC downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) database. Then, we investigated the pathogenesis of EAOC by a data-driven, function-based analytic model with the quantified molecular functions defined by 1454 Gene Ontology (GO) term gene sets. This model converts the gene expression profiles to the functionome consisting of 1454 quantified GO functions, and then, the key functions involving the malignant transformation of EOAC can be extracted by a series of filters. Our results demonstrate that the deregulated oxidoreductase activity, metabolism, hormone activity, inflammatory response, innate immune response and cell-cell signaling play the key roles in the malignant transformation of EAOC. These results provide the evidence supporting the specific molecular pathways involved in the malignant transformation of EAOC.
透明细胞癌 (CCC) 和子宫内膜样癌 (EC) 的临床特征与子宫内膜异位症 (ES) 同时存在,这导致了 ES 向与子宫内膜异位症相关的卵巢癌 (EAOC) 的恶性转化的假设。提出了不同的失调功能区来解释 EAOC 转化的发病机制,但仍缺乏对累积的实验数据进行数据驱动分析,以纳入与 EOAC 恶性转化相关的失调功能。我们使用从国家生物技术信息中心基因表达综合数据库 (NCBI GEO) 下载的 ES、CCC 和 EC 的基因表达谱微阵列数据集。然后,我们通过数据驱动的、基于功能的分析模型,利用 1454 个基因本体论 (GO) 术语基因集定义的量化分子功能,研究了 EAOC 的发病机制。该模型将基因表达谱转换为包含 1454 个量化 GO 功能的功能组,然后可以通过一系列过滤器提取涉及 EOAC 恶性转化的关键功能。我们的结果表明,失调的氧化还原酶活性、代谢、激素活性、炎症反应、先天免疫反应和细胞-细胞信号转导在 EAOC 的恶性转化中起着关键作用。这些结果为支持涉及 EAOC 恶性转化的特定分子途径提供了证据。