CAJ | 학술논문

目的：整合高通量miRNA/mRNA/蛋白质表达数据，寻找胰腺导管腺癌（pancreatic ductal ade-nocarcinoma, PDAC）分子标志物（Biomarker）。方法本文提出利用PDAC样本的mRNA、miRNA与蛋白质表达谱数据，通过“转录因子（TF）调控miRNA”与“miRNA靶向调控基因”的关系构建在PDAC发病过程中的核心调控网络，并据此预测定义选候PDAC相关mRNA与miRNA，同时利用差异表达蛋白进一步定义调控网络。结果发现6个miR let-7家族miRNA成员（hsa-let-7i、hsa-let-7d、hsa-let-7b、hsa-let-7g、hsa-let-7c、hsa-let-7e）与5个在基因与蛋白层面同时差异表达的基因（ALDH1A1、TGFB1、ANX-A2、FN1、THBS1）形成的一个核心调控网络，其网络中涉及的5个基因全都为已知与PDAC发生发展明确相关的分子。利用5个基因对3套大规模的PDAC表达数据进行分型分析，结果发现其分类准确性分别达到了93%（91%～94%）、90%（89%～92%）与88%（87%～89%）。结论通过miRNA/mRNA/蛋白三种数据整合分析得到的5候选标记基因具有良好的分型能力，可作为新的PDAC分型Biomarker。
목적：정합고통량miRNA/mRNA/단백질표체수거，심조이선도관선암（pancreatic ductal ade-nocarcinoma, PDAC）분자표지물（Biomarker）。방법본문제출이용PDAC양본적mRNA、miRNA여단백질표체보수거，통과“전록인자（TF）조공miRNA”여“miRNA파향조공기인”적관계구건재PDAC발병과정중적핵심조공망락，병거차예측정의선후PDAC상관mRNA여miRNA，동시이용차이표체단백진일보정의조공망락。결과발현6개miR let-7가족miRNA성원（hsa-let-7i、hsa-let-7d、hsa-let-7b、hsa-let-7g、hsa-let-7c、hsa-let-7e）여5개재기인여단백층면동시차이표체적기인（ALDH1A1、TGFB1、ANX-A2、FN1、THBS1）형성적일개핵심조공망락，기망락중섭급적5개기인전도위이지여PDAC발생발전명학상관적분자。이용5개기인대3투대규모적PDAC표체수거진행분형분석，결과발현기분류준학성분별체도료93%（91%～94%）、90%（89%～92%）여88%（87%～89%）。결론통과miRNA/mRNA/단백삼충수거정합분석득도적5후선표기기인구유량호적분형능력，가작위신적PDAC분형Biomarker。
Objective Using mRNA/miRNA/protein expression profiles to identify the molecular biomarker for pancreatic ductal adenocarcinoma(PDAC). Method We used miRNA , mRNA and protein ex-pression data of PDAC to construct the molecular regulatory network by the interaction of"transcription factor (TF) target miRNA" and"miRNA target gene", and based on this network to infer some candidate genes and miRNAs which were related to the molecular mechanism of PDAC. Result We found a key network related to 6 miR let-7 family members (hsa-let-7i、hsa-let-7d、hsa-let-7b、hsa-let-7g、hsa-let-7c、hsa-let-7e) and 5 differently expressed genes(ALDH1A1、TGFB1、ANXA2、FN1、THBS1), all genes and miRNA were PDAC related molecular in this key network. Three large-scale expression profile of PDAC was used to test the 5 genes. The results shown that the prediction accuracy were 93%(91%~94%)、90%(89%~92%)、88%(87%~89%) in three expression datasets, respectively. Conclusion Five genes identified by our new methods with multidimensional data minning show a great classified power for PDAC, and should be new biomarkers for PDAC.