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Integrative analysis of cancer pathway progression and coherence.

ABSTRACT: We analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The database provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, have not been analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into discrete X and Y scales and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distribution of mutation targets. The X scale corresponds to the downstream location in a pathway, whereas the Y scale corresponds to the stage of the tumor. The gene expression values of the early stage pathway members are significantly higher than of the rest of the pathway members in colorectal adenoma tissues. The colorectal cancer pathway shows some degree of coherence in the carcinoma samples. The correlated gene pairs responsible for the coherence of the colorectal cancer pathway in the carcinoma samples are supported, in part, by the literature and may suggest novel regulatory associations. Finally, there are more mutation targets in the nucleus as well as the late tumor stages of the KEGG colorectal cancer pathway.

SUBMITTER: Dalkic E

PROVIDER: S-EPMC3033602 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature

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Integrative analysis of cancer pathway progression and coherence.

Dalkic Ertugrul E Nash Daniel Elwin Walter DE Fassia Mohammad Kasim MK Chan Christina C

Proteomics. Clinical applications 20090309 4

We analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The database provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, have not been analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into discrete X and Y scales and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distr ...[more]

PMID: 21136972

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Project description:BackgroundProstate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.MethodsIn this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.ResultsDifferent phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.ConclusionsThe successful results on prostate cancer samples demonstrated that the integrative analysis is powerful and useful approach to detect candidate disease-associate genes and modules which can be used as the potential biomarkers for prostate cancer progression. The data, tools and supplementary files for this integrative analysis are deposited at http://www.ibio-cn.org/HPC-PPIN/.

Project description:Cervical Cancer is one of the leading causes of cancer-associated mortality in women. The present study aimed to identify key genes and pathways involved in cervical cancer (CC) progression, via a comprehensive bioinformatics analysis. The GSE63514 dataset from the Gene Expression Omnibus database was analyzed for hub genes and cancer progression was divided into four phases (phases I-IV). Pathway enrichment, protein-protein interaction (PPI) and pathway crosstalk analyses were performed, to identify key genes and pathways using a criterion nodal degree ?5. Gene pathway analysis was determined by mapping the key genes into the key pathways. Co-expression between key genes and their effect on overall survival (OS) time was assessed using The Cancer Genome Atlas database. A total of 3,446 differentially expressed genes with 107 hub genes were identified within the four phases. A total of 14 key genes with 11 key pathways were obtained, following extraction of ?5 degree nodes from the PPI and pathway crosstalk networks. Gene pathway analysis revealed that CDK1 and CCNB1 regulated the cell cycle and were activated in phase I. Notably, the following terms, 'pathways in cancer', 'focal adhesion' and the 'PI3K-Akt signaling pathway' ranked the highest in phases II-IV. Furthermore, FN1, ITGB1 and MMP9 may be associated with metastasis of tumor cells. STAT1 was indicated to predominantly function at the phase IV via cancer-associated signaling pathways, including 'pathways in cancer' and 'Toll-like receptor signaling pathway'. Survival analysis revealed that high ITGB1 and FN1 expression levels resulted in significantly worse OS. CDK1 and CCNB1 were revealed to regulate proliferation and differentiation through the cell cycle and viral tumorigenesis, while FN1 and ITGB1, which may be developed as novel prognostic factors, were co-expressed to induce metastasis via cancer-associated signaling pathways, including PI3K-Art signaling pathway, and focal adhesion in CC; however, the underlying molecular mechanisms require further research.

Dataset Information

Integrative analysis of cancer pathway progression and coherence.

Publications

Integrative analysis of cancer pathway progression and coherence.

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