Pan-cancer analysis of TCGA data reveals notable signaling pathways
| Autor: | Xia Jiang, Curt M. Horvath, Richard E. Neapolitan |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Lung adenocarcinoma
Male Oncology Cancer Research SPIA Breast cancer 0302 clinical medicine Surgical oncology Neoplasms Ovarian carcinoma Databases Genetic Protein Interaction Mapping Cluster Analysis Protein Interaction Maps Colon adenocarcinoma 0303 health sciences Uterine corpus endometriod carcinoma Rectum adenocarcinoma Signal transduction pathway Genomics 3. Good health 030220 oncology & carcinogenesis Adenocarcinoma Female Signal transduction Research Article Signal Transduction Kidney renal papillary cell carcinoma medicine.medical_specialty Cell signaling Pan-cancer 03 medical and health sciences Internal medicine Lung squamous cell carcinoma medicine Carcinoma Genetics Low grade glioma Humans 030304 developmental biology business.industry Computational Biology TCGA medicine.disease Cancer research Gene expression data Glioblastoma business |
| Zdroj: | BMC Cancer |
| ISSN: | 1471-2407 |
| DOI: | 10.1186/s12885-015-1484-6 |
| Popis: | Background A 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. Methods We 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. Results In 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. Conclusions We 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. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1484-6) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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