Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer.
Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer.
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DOI：
10.3390/cancers12123833
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发表时间：
2020-12-18
期刊：
影响因子：
5.2
通讯作者：
Sasaki H
中科院分区：
文献类型：
--
作者：
Tanabe S;Quader S;Ono R;Cabral H;Aoyagi K;Hirose A;Yokozaki H;Sasaki H
癌症有几个表型亚型，对药物的反应性或迁移或复发的能力不同。在不同的癌症表型中，分子网络是动态变化的。为了揭示上皮-间充质转化(EMT)中的网络通路，我们对间充质干细胞和弥漫型胃癌(GC)以及肠型GC中的基因表达进行了研究。基因表达谱显示，肠型和弥漫型胃癌的分子通路网络发生改变。人工智能(AI)识别肠型和弥漫型GC分子网络图的差异。上皮-间充质转化(EMT)在肿瘤干细胞(CSC)特性的获得和耐药中起着重要作用，而CSC和耐药是癌症恶性的主要标志。虽然以前的研究结果表明，在癌症进展过程中有几个信号通路被激活，但在EMT和CSCs中信号通路的确切机制还不完全清楚。本研究以肠型和弥漫型胃癌为研究对象，对公开的RNAseq数据进行基因表达分析，以了解不同亚型胃癌的分子通路调控。采用独创性路径分析(IPA)进行网络路径分析。在生物门户肿瘤基因组学中，肠型和弥漫型GC数据中共有2815个探针组ID显著不同。我们的分析发现了10个基因，包括雄性致死3同源(果蝇)假基因1(MSL3P1)、CDC28蛋白激酶调节亚基1B(CKS1B)、DEAD-BOX螺旋酶27(DDX27)、高尔基到内质网运输蛋白4(GET4)、染色体分离1样(CSE1L)、线粒体外膜转位酶34(TOMM34)、YTHDF1(YTHDF1)、核糖核酸输出1(RAE1)、PAR-6家族细胞极性调节因子β(PARD6B)和MRG结构域结合蛋白(MRGBP)，这些基因在肠型和弥漫型GC中的表达存在差异。在通过网络通路分析生成的生物标记物筛选网络中，共有463个与三个分子(MYC、NTRK1、UBE2M)有直接关系。生物信息学已对肠型和弥漫型GC的网络和特征进行了研究和描述。我们的结果揭示了肠型和弥漫型胃癌中的信号通路网络，为阐明CSCs的耐药机制提供了新的线索。
Cancer has several phenotypic subtypes where the responsiveness towards drugs or capacity of migration or recurrence are different. The molecular networks are dynamically altered in various phenotypes of cancer. To reveal the network pathways in epithelial-mesenchymal transition (EMT), we have profiled gene expression in mesenchymal stem cells and diffuse-type gastric cancer (GC), as well as intestinal-type GC. Gene expression signatures revealed that the molecular pathway networks were altered in intestinal- and diffuse-type GC. The artificial intelligence (AI) recognized the differences in molecular network pictures of intestinal- and diffuse-type GC. Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC) and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric cancer. Network pathway analysis was performed by Ingenuity Pathway Analysis (IPA). A total of 2815 probe set IDs were significantly different between intestinal- and diffuse-type GC data in cBioPortal Cancer Genomics. Our analysis uncovered 10 genes including male-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP), which have differences in gene expression between intestinal- and diffuse-type GC. A total of 463 direct relationships with three molecules (MYC, NTRK1, UBE2M) were found in the biomarker-filtered network generated by network pathway analysis. The networks and features in intestinal- and diffuse-type GC have been investigated and profiled in bioinformatics. Our results revealed the signaling pathway networks in intestinal- and diffuse-type GC, bringing new light for the elucidation of drug resistance mechanisms in CSCs.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
实现癌症基因组数据的共同愿景。
DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Grossman RL;Heath AP;Ferretti V;Varmus HE;Lowy DR;Kibbe WA;Staudt LM
通讯作者：
Staudt LM
在 Ampd1 基因中插入先敲除盒会破坏邻近基因的表达，从而导致新生儿死亡。
影响因子：
4.6
作者：
Pan Y;Zhang L;Liu Q;Li Y;Guo H;Peng Y;Peng H;Tang B;Hu Z;Zhao J;Xia K;Li JD
通讯作者：
Li JD
Sec23a 介导 miR-200c 增强寡转移至多转移进展。
影响因子：
11.1
作者：
Sun Z;Zhou S;Tang J;Ye T;Li J;Liu D;Zhou J;Wang J;Rosie Xing H
通讯作者：
Rosie Xing H
影响因子：
5.6
作者：
Choi PW;Ng SW
通讯作者：
Ng SW