Pan-cancer analysis of TCGA data reveals notable signaling pathways

BMC Cancer

Research Article

Xia Jiang¹  Curt M. Horvath²  Richard Neapolitan³ 

[1] Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA;Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA;Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Il, USA;

关键词: Pan-cancer;    Breast cancer;    Colon adenocarcinoma;    Glioblastoma;    Kidney renal papillary cell carcinoma;    Low grade glioma;    Lung adenocarcinoma;    Lung squamous cell carcinoma;    Ovarian carcinoma;    Rectum adenocarcinoma;    Uterine corpus endometriod carcinoma;    Signal transduction pathway;    Gene expression data;    TCGA;    SPIA;   

DOI  :  10.1186/s12885-015-1484-6

 received in 2014-12-29, accepted in 2015-06-09,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundA signal transduction pathway (STP) is a network of intercellular information flow initiated when extracellular signaling molecules bind to cell-surface receptors. Many aberrant STPs have been associated with various cancers. To develop optimal treatments for cancer patients, it is important to discover which STPs are implicated in a cancer or cancer-subtype. The Cancer Genome Atlas (TCGA) makes available gene expression level data on cases and controls in ten different types of cancer including breast cancer, colon adenocarcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, lung adenocarcinoma, lung squamous cell carcinoma, ovarian carcinoma, rectum adenocarcinoma, and uterine corpus endometriod carcinoma. Signaling Pathway Impact Analysis (SPIA) is a software package that analyzes gene expression data to identify whether a pathway is relevant in a given condition.MethodsWe present the results of a study that uses SPIA to investigate all 157 signaling pathways in the KEGG PATHWAY database. We analyzed each of the ten cancer types mentioned above separately, and we perform a pan-cancer analysis by grouping the data for all the cancer types.ResultsIn each analysis several pathways were found to be markedly more significant than all the other pathways. We call them notable. Research has already established a connection between many of these pathways and the corresponding cancer type. However, some of our discovered pathways appear to be new findings. Altogether there were 37 notable findings in the separate analyses, 26 of them occurred in 7 pathways. These 7 pathways included the 4 notable pathways discovered in the pan-cancer analysis. So, our results suggest that these 7 pathways account for much of the mechanisms of cancer. Furthermore, by looking at the overlap among pathways, we identified possible regions on the pathways where the aberrant activity is occurring.ConclusionsWe obtained 37 notable findings concerning 18 pathways. Some of them appear to be new discoveries. Furthermore, we identified regions on pathways where the aberrant activity might be occurring. We conclude that our results will prove to be valuable to cancer researchers because they provide many opportunities for laboratory and clinical follow-up studies.

【 授权许可】

CC BY   
© Neapolitan et al. 2015