Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression

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/

[1]  Jiajia Chen,et al.  Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer , 2013, BMC Systems Biology.

[2]  John Quackenbush,et al.  Open source software for the analysis of microarray data. , 2003, BioTechniques.

[3]  David Warde-Farley,et al.  Dynamic modularity in protein interaction networks predicts breast cancer outcome , 2009, Nature Biotechnology.

[4]  R. Tibshirani,et al.  Gene expression profiling identifies clinically relevant subtypes of prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  B. Conley,et al.  Detection of Prostate Cancer and Predicting Progression , 2004, Clinical Cancer Research.

[6]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[7]  Robert E. W. Hancock,et al.  The Pseudomonas aeruginosa Autoinducer 3O-C12 Homoserine Lactone Provokes Hyperinflammatory Responses from Cystic Fibrosis Airway Epithelial Cells , 2011, PloS one.

[8]  M. Becich,et al.  Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process , 2007, BMC Cancer.

[9]  Qiang Huang,et al.  Corbi: a new R package for biological network alignment and querying , 2013, BMC Systems Biology.

[10]  Lee T. Sam,et al.  Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. , 2011, Genome research.

[11]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[12]  Zhenyu Jia,et al.  In silico estimates of tissue components in surgical samples based on expression profiling data. , 2010, Cancer research.

[13]  Sarah J. Sweeney,et al.  Genetic and systems level analysis of Drosophila sticky/citron kinase and dFmr1 mutants reveals common regulation of genetic networks , 2008, BMC Systems Biology.

[14]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[15]  B. Palsson,et al.  The model organism as a system: integrating 'omics' data sets , 2006, Nature Reviews Molecular Cell Biology.

[16]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

[17]  Xing-Ming Zhao,et al.  Identifying disease genes and module biomarkers by differential interactions , 2012, J. Am. Medical Informatics Assoc..

[18]  M. Becich,et al.  Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  John T. Wei,et al.  Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. , 2005, Cancer cell.

[20]  S. Dhanasekaran,et al.  Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.

[21]  Bairong Shen,et al.  Post genome-wide association studies functional characterization of prostate cancer risk loci , 2013, BMC Genomics.

[22]  Huiqing Yuan,et al.  Involvement of transcription factor Sp1 in quercetin-mediated inhibitory effect on the androgen receptor in human prostate cancer cells. , 2005, Carcinogenesis.

[23]  Vladimir B. Bajic,et al.  In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer , 2011, BMC Systems Biology.

[24]  Jun-jun Liu,et al.  In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers. , 2011, Methods in molecular biology.

[25]  John T. Wei,et al.  Integrative molecular concept modeling of prostate cancer progression , 2007, Nature Genetics.

[26]  Yu-Chun Lin,et al.  ' s response to reviews Title : Identifying Significant Genetic Regulatory Networks in the Prostate Cancer from Microarray Data Based on Transcription Factor Analysis and Conditional Independency , 2009 .

[27]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[28]  B. Woda,et al.  Sp1, a New Biomarker That Identifies a Subset of Aggressive Pancreatic Ductal Adenocarcinoma , 2008, Cancer Epidemiology Biomarkers & Prevention.

[29]  Hyun-Seok Jin,et al.  Type 2 diabetes genetic association database manually curated for the study design and odds ratio , 2010, BMC Medical Informatics Decis. Mak..

[30]  Reinhard Schneider,et al.  Using graph theory to analyze biological networks , 2011, BioData Mining.

[31]  I. Cree,et al.  Identification of markers of prostate cancer progression using candidate gene expression , 2011, British Journal of Cancer.

[32]  S. Goodison,et al.  Targeting Sp1 transcription factors in prostate cancer therapy. , 2011, Medicinal chemistry (Shariqah (United Arab Emirates)).

[33]  Igor Jurisica,et al.  Integrative computational biology for cancer research , 2011, Human Genetics.

[34]  Younghoon Kim,et al.  Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification , 2009, Bioinform..

[35]  Hong Zhao,et al.  PGDB: a curated and integrated database of genes related to the prostate , 2003, Nucleic Acids Res..

[36]  G. R. Stuart,et al.  Construction and application of a protein and genetic interaction network (yeast interactome) , 2009, Nucleic acids research.

[37]  Ying Wang,et al.  Molecular Signature of Cancer at Gene Level or Pathway Level? Case Studies of Colorectal Cancer and Prostate Cancer Microarray Data , 2013, Comput. Math. Methods Medicine.

[38]  Gordon K. Smyth,et al.  Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..

[39]  O. Kallioniemi.  Functional genomics and transcriptomics of prostate cancer: promises and limitations , 2005, BJU international.

[40]  F. Collins,et al.  The Human Genome Project: Lessons from Large-Scale Biology , 2003, Science.

[41]  G. Fraizer,et al.  Androgen up-regulates vascular endothelial growth factor expression in prostate cancer cells via an Sp1 binding site , 2013, Molecular Cancer.

[42]  Raj Chari,et al.  An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer , 2010, BMC Systems Biology.

[43]  Hui Liu,et al.  AnimalTFDB: a comprehensive animal transcription factor database , 2011, Nucleic Acids Res..

[44]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[45]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2002, Nucleic Acids Res..

[46]  S. Willard,et al.  Regulators of gene expression as biomarkers for prostate cancer. , 2012, American journal of cancer research.

[47]  Hongyu Zhao,et al.  COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method , 2011, Bioinform..

[48]  Goran Nenadic,et al.  Mining protein function from text using term-based support vector machines , 2005, BMC Bioinformatics.

[49]  R. Medeiros,et al.  EGFR signaling pathway and related-miRNAs in age-related diseases: the example of miR-221 and miR-222 , 2012, Front. Gene..

[50]  Moorthy P Ponnusamy,et al.  Targeting the EGFR signaling pathway in cancer therapy , 2012, Expert opinion on therapeutic targets.

[51]  H. Kim,et al.  Application of Support Vector Machine for Prediction of Medication Adherence in Heart Failure Patients , 2010, Healthcare informatics research.

[52]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[53]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[54]  R. Sharan,et al.  Protein networks in disease. , 2008, Genome research.

[55]  T. Nikolskaya,et al.  Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms , 2006, Xenobiotica; the fate of foreign compounds in biological systems.

[56]  Gajendra P. S. Raghava,et al.  CCDB: a curated database of genes involved in cervix cancer , 2010, Nucleic Acids Res..

[57]  Kern Rei Chng,et al.  Sequencing the transcriptional network of androgen receptor in prostate cancer. , 2013, Cancer letters.

[58]  Yin Li,et al.  Identifying novel glioma associated pathways based on systems biology level meta-analysis , 2013, BMC Systems Biology.

[59]  M. Verma,et al.  Biomarkers in Prostate Cancer Epidemiology , 2011, Cancers.

[60]  Kurt Hornik,et al.  kernlab - An S4 Package for Kernel Methods in R , 2004 .

[61]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[62]  Ying Wang,et al.  Identifying novel prostate cancer associated pathways based on integrative microarray data analysis , 2011, Comput. Biol. Chem..

[63]  Y. Benjamini,et al.  Controlling the false discovery rate in behavior genetics research , 2001, Behavioural Brain Research.

[64]  Benno Schwikowski,et al.  Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.

[65]  Yan Zhang,et al.  Research and applications: An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer , 2013, J. Am. Medical Informatics Assoc..

[66]  Zhenyu Jia,et al.  Diagnosis of prostate cancer using differentially expressed genes in stroma. , 2010, Cancer research.

[67]  Jing Zhu,et al.  Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network , 2007, Bioinform..

[68]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[69]  F. Jiang,et al.  Analysis of the specific pathways and networks of prostate cancer for gene expression profiles in the Chinese population , 2012, Medical Oncology.

[70]  M. Kohli,et al.  Biomarker-Based Targeting of the Androgen-Androgen Receptor Axis in Advanced Prostate Cancer , 2012, Advances in urology.

[71]  Jiajia Chen,et al.  Computational analysis of microRNA function in heart development. , 2010, Acta biochimica et biophysica Sinica.

[72]  Elizabeth Shoop,et al.  Data exploration tools for the Gene Ontology database , 2004, Bioinform..

[73]  Shi-Hua Zhang,et al.  Detecting disease associated modules and prioritizing active genes based on high throughput data , 2010, BMC Bioinformatics.

[74]  Vladimir B. Bajic,et al.  DDPC: Dragon Database of Genes associated with Prostate Cancer , 2010, Nucleic Acids Res..

[75]  Santosh K. Mishra,et al.  De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures , 2007, Bioinform..

[76]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[77]  Windy A. Boyd,et al.  Decline of nucleotide excision repair capacity in aging Caenorhabditis elegans , 2007, Genome Biology.

[78]  Y. Fradet.  Biomarkers in prostate cancer diagnosis and prognosis: beyond prostate-specific antigen , 2009, Current opinion in urology.

[79]  J. Vishwanatha,et al.  Oncogenic activation in prostate cancer progression and metastasis: Molecular insights and future challenges , 2012, Journal of carcinogenesis.

[80]  N. Campbell.  Genetic association database , 2004, Nature Reviews Genetics.

[81]  M. Loda,et al.  Epithelial-Restricted Gene Profile of Primary Cultures from Human Prostate Tumors: A Molecular Approach to Predict Clinical Behavior of Prostate Cancer , 2006, Molecular Cancer Research.

[83]  J. Welsh,et al.  Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.

[84]  A. Jemal,et al.  The global burden of cancer: priorities for prevention , 2009, Carcinogenesis.