Literature DB >> 31938209

Bioinformatic analysis of differential expression and core GENEs in breast cancer.

Hongchang Dong1, Shuai Zhang1, Yu Wei2, Chunyan Liu2, Na Wang1, Pan Zhang1, Jingling Zhu1, Jin Huang1.   

Abstract

Breast cancer (BRCA) is one of the most common malignancies in women. The gene expression profile of GSE103512 from the GEO database was downloaded in order to find key genes involved in the occurrence and development of BRCA. 75 samples, including 65 cancer and 10 normal samples, were included in this analysis. Differentially expressed genes (DEGs) between BRCA patients and health people were chosen using R tool. We next performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) was utilized to visualize protein-protein interaction (PPI) of these DEGs. The related genes and medicines specific to hub genes were predicted by CBioportal. We screened a total of 357 DEGs including 77 up-regulated and 280 down-regulated. A series of BRCA related GO terms and pathways were identified by analysis of these DEGs. Insulin-like growth factor 1 (IGF1); epidermal growth factor receptor (EGFR); v-jun avian sarcoma virus 17 oncogene homolog (JUN) and Estrogen Receptor 1 (ESR1) of the DEGs were screened by construction of the PPI network and the degree of connectivity. IGF1 and ESR1 were finally selected as potential hub genes and treatment targets of BRCA. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as IGF1, might regulate the development of gastric cancer. These DEGs could be used as new biomarkers for diagnosis and to guide the combination medicine of BRCA. IJCEP
Copyright © 2018.

Entities:  

Keywords:  Breast cancer; bioinformatics analysis; biomarker; differential expression genes; therapeutic

Year:  2018        PMID: 31938209      PMCID: PMC6958129     

Source DB:  PubMed          Journal:  Int J Clin Exp Pathol        ISSN: 1936-2625


  27 in total

1.  Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

Authors:  M Ashburner; C A Ball; J A Blake; D Botstein; H Butler; J M Cherry; A P Davis; K Dolinski; S S Dwight; J T Eppig; M A Harris; D P Hill; L Issel-Tarver; A Kasarskis; S Lewis; J C Matese; J E Richardson; M Ringwald; G M Rubin; G Sherlock
Journal:  Nat Genet       Date:  2000-05       Impact factor: 38.330

2.  Breast cancer subtypes and previously established genetic risk factors: a bayesian approach.

Authors:  Katie M O'Brien; Stephen R Cole; Lawrence S Engel; Jeannette T Bensen; Charles Poole; Amy H Herring; Robert C Millikan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-10-31       Impact factor: 4.254

3.  Survival analysis.

Authors:  Robert Flynn
Journal:  J Clin Nurs       Date:  2012-08-04       Impact factor: 3.036

4.  Breast cancer statistics, 2015: Convergence of incidence rates between black and white women.

Authors:  Carol E DeSantis; Stacey A Fedewa; Ann Goding Sauer; Joan L Kramer; Robert A Smith; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2015-10-29       Impact factor: 508.702

5.  KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model.

Authors:  Junli Du; Zhifa Yuan; Ziwei Ma; Jiuzhou Song; Xiaoli Xie; Yulin Chen
Journal:  Mol Biosyst       Date:  2014-07-29

6.  Molecular markers of early Parkinson's disease based on gene expression in blood.

Authors:  Clemens R Scherzer; Aron C Eklund; Lee J Morse; Zhixiang Liao; Joseph J Locascio; Daniel Fefer; Michael A Schwarzschild; Michael G Schlossmacher; Michael A Hauser; Jeffery M Vance; Lewis R Sudarsky; David G Standaert; John H Growdon; Roderick V Jensen; Steven R Gullans
Journal:  Proc Natl Acad Sci U S A       Date:  2007-01-10       Impact factor: 11.205

7.  Insulin priming effect on estradiol-induced breast cancer metabolism and growth.

Authors:  Peninah M Wairagu; Ai N H Phan; Min-Kyu Kim; Jeongwoo Han; Hyun-Won Kim; Jong-Whan Choi; Ki Woo Kim; Seung-Kuy Cha; Kwang Hwa Park; Yangsik Jeong
Journal:  Cancer Biol Ther       Date:  2015       Impact factor: 4.742

8.  Contextual Hub Analysis Tool (CHAT): A Cytoscape app for identifying contextually relevant hubs in biological networks.

Authors:  Tanja Muetze; Ivan H Goenawan; Heather L Wiencko; Manuel Bernal-Llinares; Kenneth Bryan; David J Lynn
Journal:  F1000Res       Date:  2016-07-19

9.  Female Breast Cancer Mortality Clusters in Shandong Province, China: A Spatial Analysis.

Authors:  Jie Chu; Chengchao Zhou; Xiaolei Guo; Jiandong Sun; Fuzhong Xue; Jiyu Zhang; Zilong Lu; Zhentao Fu; Aiqiang Xu
Journal:  Sci Rep       Date:  2017-03-07       Impact factor: 4.379

10.  The association of dietary pattern and breast cancer in Jiangsu, China: A population-based case-control study.

Authors:  Shurong Lu; Yun Qian; Xingyu Huang; Hao Yu; Jie Yang; Renqiang Han; Jian Su; Wencong Du; Jinyi Zhou; Meihua Dong; Xiaojin Yu; Fränzel J B van Duijnhoven; Ellen Kampman; Ming Wu
Journal:  PLoS One       Date:  2017-09-12       Impact factor: 3.240

View more
  2 in total

1.  Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies.

Authors:  Md Shahin Alam; Adiba Sultana; Md Selim Reza; Md Amanullah; Syed Rashel Kabir; Md Nurul Haque Mollah
Journal:  PLoS One       Date:  2022-05-26       Impact factor: 3.752

2.  An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer.

Authors:  Sepideh Dashti; Mohammad Taheri; Soudeh Ghafouri-Fard
Journal:  Sci Rep       Date:  2020-10-30       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.