论文库首页  论文库
 
论文编号:
论文题目: Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method
英文论文题目: Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method
第一作者: Shen, SH; Gui, TT; Ma, CC
英文第一作者: Shen, SH; Gui, TT; Ma, CC
联系作者: Ma, CC (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, Shanghai, Peoples R China.
英文联系作者: Ma, CC (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, Shanghai, Peoples R China.
外单位作者单位:
英文外单位作者单位:
发表年度: 2017
卷: 8
期: 25
页码: 41432-41439
摘要: The high mortality rate of pancreatic cancer makes it one of the most studied diseases among all cancer types. Many researches have been conducted to understand the mechanism underlying its emergence and pathogenesis of this disease. Here, by using minimum-redundancy-maximum-relevance (mRMR) method, we studied a set of transcriptome data of pancreatic cancer. As we gradually added features to achieve the most accurate classification results of Jackknife, a gene set of 9 genes was identified. They were NHS, SCML2, LAMC2, S100P, COL17A1, AMIGO2, PTPRR, KPNA7 and KCNN4. Through STRING 2.0 protein-protein interactions (PPIs) analysis, 40 proteins were identified in the shortest paths between genes in the gene set, 30 of them passed the permutation test, which indicated they were hubs in the background network. Those genes in the protein- protein interaction network were enriched to 37 functional modules, such as: negative regulation of transcription from RNA polymerase II promoter, negative regulation of ERK1 and ERK2 cascade and BMP signaling pathway. Our study indicated new mechanism of pancreatic cancer, suggesting potential therapeutic targets for further study.
英文摘要: The high mortality rate of pancreatic cancer makes it one of the most studied diseases among all cancer types. Many researches have been conducted to understand the mechanism underlying its emergence and pathogenesis of this disease. Here, by using minimum-redundancy-maximum-relevance (mRMR) method, we studied a set of transcriptome data of pancreatic cancer. As we gradually added features to achieve the most accurate classification results of Jackknife, a gene set of 9 genes was identified. They were NHS, SCML2, LAMC2, S100P, COL17A1, AMIGO2, PTPRR, KPNA7 and KCNN4. Through STRING 2.0 protein-protein interactions (PPIs) analysis, 40 proteins were identified in the shortest paths between genes in the gene set, 30 of them passed the permutation test, which indicated they were hubs in the background network. Those genes in the protein- protein interaction network were enriched to 37 functional modules, such as: negative regulation of transcription from RNA polymerase II promoter, negative regulation of ERK1 and ERK2 cascade and BMP signaling pathway. Our study indicated new mechanism of pancreatic cancer, suggesting potential therapeutic targets for further study.
刊物名称: ONCOTARGET
英文刊物名称: ONCOTARGET
论文全文:
英文论文全文:
全文链接:
其它备注:
英文其它备注:
学科: Oncology; Cell Biology
英文学科: Oncology; Cell Biology
影响因子: 5.168
第一作者所在部门:
英文第一作者所在部门:
论文出处:
英文论文出处:
论文类别: Article
英文论文类别: Article
参与作者:
英文参与作者:
 
2014 中国科学院上海生命科学研究院 版权所有