CAJ | 학술논문

目的乳腺癌类型和分级多样性导致其预后差别显著，探寻乳腺癌不同分级情况下的基因表达差异及调控关系能够为乳腺癌致病机制的发现提供重要依据。方法对不同分级下的乳腺癌基因表达数据利用快速独立成分分析( FastICA)方法提取特征基因，并结合人类蛋白质相互作用( PPI)数据选取目标基因。在此基础上，结合转录因子对靶基因调控的先验信息，利用网络成分分析( NCA)方法对与乳腺癌发病有密切关系的转录因子及其靶基因构建转录调控网络。结果筛选出的基因经过数据库验证与乳腺癌相关的占48.15%，构建的调控网络发现了多个转录因子及靶基因在不同分级情况下的活性变化趋势。结论 FastICA算法结合PPI数据提取目标基因的方法较为有效，通过NCA算法构建的转录调控网络为研究乳腺癌发生发展机制提供了新的方法。
목적유선암류형화분급다양성도치기예후차별현저，탐심유선암불동분급정황하적기인표체차이급조공관계능구위유선암치병궤제적발현제공중요의거。방법대불동분급하적유선암기인표체수거이용쾌속독립성분분석( FastICA)방법제취특정기인，병결합인류단백질상호작용( PPI)수거선취목표기인。재차기출상，결합전록인자대파기인조공적선험신식，이용망락성분분석( NCA)방법대여유선암발병유밀절관계적전록인자급기파기인구건전록조공망락。결과사선출적기인경과수거고험증여유선암상관적점48.15%，구건적조공망락발현료다개전록인자급파기인재불동분급정황하적활성변화추세。결론 FastICA산법결합PPI수거제취목표기인적방법교위유효，통과NCA산법구건적전록조공망락위연구유선암발생발전궤제제공료신적방법。
Objective The diversities of breast cancer types and grading levels lead to distinct difference for breast cancer prognosis. Studying the gene difference expression and regulatory relationship among genes under different grading levels of breast cancer could provide an important basis for finding breast cancer pathogenesis. MethodsUsing fast independent component analysis ( FastICA ) method to extract feature genes of gene expression data of breast cancer, and then selected the aimed genes by combining with human protein-protein interaction data ( PPI) . On this basis, introducing prior information which described regulatory relationships about how transcription factors regulated their target genes, we continued to analize transcription factors and their target genes, which were closely associated with the incidence of breast cancer, by using network components analysis method ( NCA) , and then constructed a transcriptional regulatory network. Results Selected aimed gene which was closely associated with breast cancer is about 48.15%, that had been validated by breast cancer database. And from the built regulatory network, found out the activity change trend of multiple transcription factors and their target genes under different grading levels. Conclusion FastICA algorithm combined with PPI data for extracting aimed gene is a relatively ef-fective method. Simultaneously, constructing transcription regulatory network with NCA method provides a novel way for studying progression mechanism of breast cancer.