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Integrative genomic analysis for the discovery of biomarkers in prostate cancer.

ABSTRACT: Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

SUBMITTER: Hicks C

PROVIDER: S-EPMC4085106 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Integrative genomic analysis for the discovery of biomarkers in prostate cancer.

Hicks Chindo C Koganti Tejaswi T Giri Shankar S Tekere Memory M Ramani Ritika R Sitthi-Amorn Jitsuda J Vijayakumar Srinivasan S

Biomarker insights 20140629

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal associ ...[more]

PMID: 25057237

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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:BackgroundWith the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.Methodology/principal findingsIn the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.Conclusion/significanceThe upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the 'omics' era.

Project description:BackgroundDuctal adenocarcinoma of the prostate is an aggressive subtype, with high rates of biochemical recurrence and overall poor prognosis. It is frequently found coincident with conventional acinar adenocarcinoma. The genomic features driving evolution to its ductal histology and the biology associated with its poor prognosis remain unknown.ObjectiveTo characterize genomic features distinguishing ductal adenocarcinoma from coincident acinar adenocarcinoma foci from the same patient.Design, setting, and participantsTen patients with coincident acinar and ductal prostate cancer underwent prostatectomy. Laser microdissection was used to separately isolate acinar and ductal foci. DNA and RNA were extracted, and used for integrative genomic and transcriptomic analyses.Outcome measurements and statistical analysisSingle nucleotide mutations, small indels, copy number estimates, and expression profiles were identified. Phylogenetic relationships between coincident foci were determined, and characteristics distinguishing ductal from acinar foci were identified.Results and limitationsExome sequencing, copy number estimates, and fusion genes demonstrated coincident ductal and acinar adenocarcinoma diverged from a common progenitor, yet they harbored distinct alterations unique to each focus. AR expression and activity were similar in both histologies. Nine of 10 cases had mutually exclusive CTNNB1 hotspot mutations or phosphatase and tensin homolog (PTEN) alterations in the ductal component, and these were absent in the acinar foci. These alterations were associated with changes in expression in WNT- and PI3K-pathway genes.ConclusionsCoincident ductal and acinar histologies typically are clonally related and thus arise from the same cell of origin. Ductal foci are enriched for cases with either a CTNNB1 hotspot mutation or a PTEN alteration, and are associated with WNT- or PI3K-pathway activation. These alterations are mutually exclusive and may represent distinct subtypes.Patient summaryThe aggressive subtype ductal adenocarcinoma is closely related to conventional acinar prostate cancer. Ductal foci contain additional alterations, however, leading to frequent activation of two targetable pathways.

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Integrative genomic analysis for the discovery of biomarkers in prostate cancer.

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Integrative genomic analysis for the discovery of biomarkers in prostate cancer.

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