Volume 7 Supplement 1
Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression
© Li et al.; licensee BioMed Central Ltd. 2014
Published: 8 May 2014
Prostate 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.
In 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.
Different 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.
The 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/
KeywordsBiomarker Disease-associated Genes Integrative analysis Prostate cancer Transcription factor
Prostate cancer is the second leading cause of morbidity and mortality in men [1, 2]. In recent years, the incidence rate of prostate cancer has dramatically increased , and this is largely because of lack of diagnosis and treatment of the disease at the early stage . Thus, the successful clinical biomarkers for early diagnosis of the presence of prostate cancer become very urgent to reduce the death risk of the prostate cancer [5, 6].
In the post-genomics era, there is an explosion of biological data and information generated from high-throughput technologies which have rapidly provided an unprecedented multi-level omics data . Such transcriptomics, referred to as gene expression profiling can now comprehensively survey the entire human genomics. Moreover, enormous efforts have been made to identify biomarkers for various cancers by the analysis of different transcriptomics data [8–12]. As an example reported by our previous study, integrative transcriptomics data could be used to identify putative novel prostate cancer associated pathways, such as Endothelin-1/EDNRA trans-activation of EGFR pathway which would provide essential information for development of network biomarkers and individualized therapy strategy for prostate cancer [11–13]. Looking at the other relevant studies for cancer transcriptomics, a large scale expression study presented by Wang et al. identified a set of gene markers for prediction of metastasis for breast cancer  and followed by Chari et al. demonstrated an approach based on multiple concerted disruptions (MCD) analysis and identified genes and pathways in cancer . Furthermore, transcriptomics could be used to identify metabolic biomarkers through alterative metabolic pathways at different cancer phases . Concerning on the other levels of omics, proteomics in context of protein-protein interaction network could also be used to characterize and diagnose a pathological process . As clearly reported by Ideker and Sharan , the indicating genes as biomarkers in complex diseases tend to cluster together on well-connected proteins interaction sub-networks. In following years, Chuang et al. also showed that it could be useful to extract co-expressed functional sub-networks for metastasis of breast cancer through integrating transcriptomics data with protein-protein interaction to obtain higher classification accuracy . Later, Taylor et al. studied the altered protein interaction modularity to predict breast cancer progression by examining the biochemical structure of the interactome . Besides, there were similar studies for analysis of sub-networks and/or hub proteins which had been helpful for the understanding of the metastasis of cancer at the molecular level .
Focusing on prostate cancer, there were some reports on identifying disease-related gene modules, sub-networks or dysfunctional pathways focused on global characteristics of interactome together with gene expression data by different novel algorithms and methods development [21–23]. Nonetheless, there are still few studies on identification of prostate cancer biomarkers for early detection of the presence as well as disease progression . The relationships among the potential prostate cancer genes and associated functions as well as pathways are still poorly characterized, such as how they interacted and regulated with each other, also what they act within the network modules. These investigations are warranted for a comprehensive understanding of the molecular mechanisms underlying prostate cancer progression. Hence, it is a challenge to perform an integrative analysis of different data, which can be gene expression profiling, protein-protein interaction (PPI) data, pathway information, and clinical information, that can offer different perspectives on the biological problems in prostate cancer and further identification of potential biomarkers [24, 25].
In this study, we therefore aim to reveal candidate disease- associated genes and biomarkers for prostate cancer progression by integrative gene expression profiling and network analysis at a systematic level. We first reconstructed human prostate cancer protein-protein interaction network and used this network as a scaffold for further integrative analysed with gene expression data of prostate cancer. Here, analysis of gene expression profiling of prostate cancer was performed at different disease phases. Through modular analysis, the different modules associated with disease phases were then identified. Last but not least, we could identify significant genes through these modules which were supposed to be the gene expression signatures with highly relevant to specific phases of prostate cancer. Once the common genes identified in each of different modules were overlapped, expectedly these common genes were beneficial for uncovering of novel prostate cancer-related pathways and transcription factors which could be candidate biomarkers for prostate cancer progression. Our study hereby demonstrated a practical workflow for integrative analysis of prostate cancer at the systematic level. For the genome-wide studies, this will be a basic effort for future development and evolution in aspects of the translational biomedical informatics, which ultimately intend to improve patient outcomes and diagnostics with omics dataset through integrative systems biology .
Human prostate cancer protein interaction network reconstruction and annotation
For the second type of the dataset, it was the human protein-protein interactions data (Homo sapiens) which was downloaded from the BioGRID database . Concerning on annotation of the HPC-PPIN, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) system [35, 36]. At the beginning, functional annotation clustering tool of DAVID system was applied to group annotated genes within HPC-PPIN across three GO processes underlying molecular function, biological process, and cellular component. Among three GO processes, this tool was then used to identify the enriched GO terms. In order to annotate detailed functions in context of pathways underlying metabolism, cellular process, environmental information process and genetics information process, KEGG database was used (http://www.genome.jp/kegg/pathway.html).
Prostate cancer gene expression data collection and analysis
Gene expression profiles of prostate cancer used for integrative analysis#
No. Samples of prostate cancer stages
cDNAChinnaiyan Human 20K Hs6
Agilent-014850 4x44K G4112F
Agilent-014850 4x44K G4112F
Modular analysis for prostate cancer progression
In order to perform modular analysis for study of disease progression, three main steps were necessarily performed. At the first step, the analysed gene expression data previously derived from pairwise disease phase comparison of prostate cancer was integrated with the reconstructed HPC-PPIN. Hereafter core sub-networks analysis and overlapping analysis as second and the third steps were then performed, respectively. Regarding on the core sub-networks analysis, they were investigated for which were shown highly active scores and top ranks based on the greedy algorithm. In this investigation, the greedy algorithm was selected for searching the core sub-networks in a large network of interactions from any pairwise disease phases comparison, where refers to a connected sub-graph of the interactome that has high significance of differential expression values . To elaborate how the greedy algorithm used, originally the adjusted p-value derived from any pairwise disease phases comparison was converted to the readily form of z-score by using the inverse normal cumulative distribution (θ-1) for scoring and ranking . Afterwards the greedy algorithm by jActiveModules (jAM) plug-in as implemented in the Cytoscape [41, 42] was used to investigate and extract the significant core sub-networks under threshold of three iterations and top ten ranks. Through the end, the list of top ten ranks were merged together to gain a final core sub-network which represented for each of pairwise disease phases comparison and for each of gene expression profile. Notably, jAM was chosen as a basis for this investigation because it is a fashionable method, based on a survey of the current literature. There are several successful cases where jAM has been applied to extract the significant core sub-networks, for examples in fruit fly Drosophila , yeast S. cerevisiae , worm C. elegans  and human H. sapiens [19, 46].
To finalize the modular analysis, the overlapping analysis was carried out. The overlapping analysis at gene level was applied to show the number of enriched genes shared by all gene expression profiles (see Table 1) calculated based on core sub-networks analysis. For example, considering each of a final core sub-network retrieved from each of pairwise disease phase comparison analysing across all gene expression profiles, the overlapping percentage of genes was calculated between any two of the final core sub-networks derived from any two of the gene expression profiles. For the formula of the overlapping analysis, we defined the number of genes
After overlapping analysis, as a result the overlapping percentage across all gene expression profiles was obtained for each of pairwise disease phase comparison. Towards all possible pairwise disease phases comparison (i.e. early-middle phases, middle-late phases, and early-late phases), three different modules associated with disease progression were eventually identified. It is very possible that each of these three modules plays important roles in dynamic changes of molecular interactions at a specific phase of the disease progression. The identified unique genes in each module were regarded as signatures at a specific phase of prostate cancer. The identified common genes in all three modules were regarded as candidate disease-associated genes.
Identified candidate prostate cancer associated genes as putative module biomarker
To validate the identified prostate cancer associated genes as putative module biomarker, we used them as a module biomarker to discriminate between control and prostate cancer samples. Support vector machine (SVM) regression proposed by Cortes and Vapnik  was selected due to its attractive features and high performances [48–50] for applying to the expression values of the predicted prostate cancer associated genes from the module biomarker to distinguish prostate cancer from controls. The Receiver Operating Characteristic (ROC) curve and the area under curve (AUC) were used to evaluate the efficiency of classification [51–53]. Two R packages, namely kernlab  and ROCR , were applied to build the SVM classifier and produced the ROC curves.
Validation of candidate prostate cancer associated genes by statistical methods
In the above equation, N and M represents the number of genes in the expression profiles and the number of known cancer genes respectively, n and k are the number of the candidate prostate cancer associated genes that we identified, and the number of common entries between them, respectively. P represents the statistical significance of the enrichment. Random sampling was used to test the statistical significance and the same number of known cancer genes was randomly selected from Cancer Gene Census database , Genetic Association Database (GAD)  and AnimalTFDB  to assess the statistically significance of these known cancer genes included in the previous results. At first, the same number of genes as the candidate prostate cancer associated genes was randomly selected from the reconstructed HPC-PPIN. Subsequently, the number of known cancer genes included in the random samples was then counted. Afterwards, random sampling was repeated 106 times. Then, the p-value of the candidate prostate cancer associated genes was defined as the probability that one random sampling might contain a greater or equal number of known cancer genes than in our study samples.
Functional and pathway enrichment analysis
The GeneGo, which is a commercial integrated knowledge database , was used for analysis of functional and pathway enrichment. The statistical significance value was calculated using hypergeometric distribution and false discovery rate (FDR) method (p value < 0.05).
Results and discussion
Reconstructed HPC-PPIN and its functional annotation
Number of genes, proteins and interactions between pairwise disease phases comparison#
Number of proteins in the reconstructed HPC-PPIN
Number of genes within analyzed modules
Number of interactions within analyzed modules
bNumer of candidate disease-associated genes
Modules involved in prostate cancer progression
To further elaborate functions of unique gene expression signatures, literature search using PubMed was performed. Our finding clearly showed that unique gene expression signatures play important roles in progression of prostate cancer at a specific phase. For examples, SMAD3 and TGFB2 were reported as androgen- independent prostate cancer-specific genes  which were found in a specific expression of early-late phase. In addition, there were more unique gene expression signatures in early-late phase, for instances PTEN, BRAF, DDX5, NCOA4, WHSC1, CCND2, CDH11, ERCC5, FANCD2, LIFR, MAF, RAF1, and TOP1. Examples of unique gene expression signatures in middle-late phase, we found TP53 and RB1 which were reported as tumour suppressor genes. Growing evidences were also shown in transcription factor, such as STAT3 which was identified only in early-middle phase.
Candidate disease-associated genes in prostate cancer progression and their statistical significance
Summary of statistical significance of candidate disease-associated genes in prostate cancer progression#
Known reported genes
Genes in the HPC-PPIN
Candidate disease-associated genes
6 × 10-4,
<3 × 10-4
309 (prostate cancer only
7 × 10-3, 7 × 10-3
AnimalTFDB (Transcription factors)
9 × 10-3, 7 × 10-3
To further assess statistical significance of 94 candidate disease-associated genes, the Cancer Gene Census database , GAD , and AnimalTFDB  were also used. 23 out of 94 genes underlying 488 genes which have been reported to be related to cancer in Cancer Gene Census database , we further investigated whether these genes could be randomly obtained. Statistical significance was checked using hypergeometric distribution and 106 times random simulation. The results showed that two significant p-values of 6 × 10-4 and < 3 × 10-4 were obtained, respectively. These indicate that the candidate cancer genes are enriched among known cancer-related genes and cannot be obtained randomly. For GAD , 22 out of 94 genes underlying 309 genes reported to be related to prostate cancer. The statistical significance was similarly checked. Two significant p-values of 7 × 10-3 from a hypergeometric distribution and7 × 10-3 from random simulation were obtained. Once using the AnimalTFDB , 18 out of 94 genes underlying 1,457 genes, which have been reported to be related to transcription factors. Statistical significance was similarly checked. As a result, two significant p-values of 9 × 10-3 and 7 × 10-3 were obtained with a hypergeometric distribution and random simulation, respectively.
Validation of candidate disease-associated genes regarded as potential module biomarker
Transcription factor Sp1 as a novel biomarker for prostate cancer
Towards candidate disease-associated genes as the potential module biomarker, interestingly, we found 18 key transcription factors which had a major fraction involved in Transcriptional regulation. Accordingly, it is possible that these key transcription factors probably regulate a large number of genes and are called potential candidates to be biomarkers for prostate cancer. This is based on the concept that transcription factors are the drivers of the potential regulation of genes in prostate cancer, and thus are relevant for use as biomarkers .
In order to identify potential candidates to be biomarkers for prostate cancer, we initially mapped 94 candidate disease-associated genes to GeneGo which invoked an appropriate algorithm to build networks relevant to active data, such as our gene list in a straightforward manner depending on the task. Later, we chose Transcription regulation workflow from GeneGo which generated sub-networks centred on transcription factors. Sub-networks were then ranked by p-values and interpreted in terms of gene ontology. Afterwards, a few of sub-networks containing receptors with direct ligands from our datasets and their closet transcription factors that directly targeted the objects with these datasets were generated. To the end, we could identify potential transcription factors regarded as candidates to be biomarkers for prostate cancer which had a Transcription regulation relationship with the regulated candidate disease-associated genes.
In summary, we proposed an integrative analysis based on the gene expression profiles and the reconstructed protein-protein interaction network for prostate cancer, in contrast to the conventional methods of examining differential genes expression or proteins expression. In particular, this study was more intensive analysis on modular analysis for investigating the progression of different disease phases of prostate cancer. The achieved significant modules resulted in the identification of the candidate disease-associated genes which were consequently regarded as potential module biomarker. It can be effectively used as the promising feature to distinguish between control and disease samples. Regarding on functional analysis of candidate disease-associated genes, interestingly a major fraction of genes was enriched in the nucleus and different functions were encoded for transcription factors. Concerning on pathway enrichment analysis, Transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were clearly shown to be the pathway targets for prostate cancer progression. Transcription factor AR plays an important role in Transcription relationship and acts as a hub for regulating a lot of genes in the Transcription AR nuclear signaling. EGFR signalling regulates cell proliferation, cell differentiation, cell cycle, and cell migration and therefore it has been a potential interested target for effective cancer therapies. Last but not least, we successfully found an interesting transcription factor Sp1 which could be regarded as a potential novel biomarker for prostate cancer. For a future work, we will further study the experimental validation of potential disease genes and pathways during prostate cancer progression.
We would like to thank Dr. Subir Kumar Nandy for assisting in human prostate cancer protein interaction network reconstruction. BS and YL were supported by National Natural Science Foundation of China (NSFC) (grant no. 31170795 and no. 91230117). WV was supported by National Natural Science Foundation of China (NSFC) (grant no. 31200989).
The publication costs for this article were funded by National Natural Science Foundation of China (NSFC) (grant no. 31170795 and no. 91230117).
This article has been published as part of BMC Medical Genomics Volume 7 Supplement 1, 2014: Selected articles from the 3rd Translational Bioinformatics Conference (TBC/ISCB-Asia 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/7/S1.
- Dasgupta S, Srinidhi S, Vishwanatha JK: Oncogenic activation in prostate cancer progression and metastasis: Molecular insights and future challenges. J Carcinog. 2012, 11: 4-10.4103/1477-3163.93001.PubMedPubMed CentralView ArticleGoogle Scholar
- Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics. CA Cancer J Clin. 2011, 61 (2): 69-90. 10.3322/caac.20107.PubMedView ArticleGoogle Scholar
- Tricoli JV, Schoenfeldt M, Conley BA: Detection of prostate cancer and predicting progression: current and future diagnostic markers. Clin Cancer Res. 2004, 10 (12 Pt 1): 3943-3953.PubMedView ArticleGoogle Scholar
- Thun MJ, DeLancey JO, Center MM, Jemal A, Ward EM: The global burden of cancer: priorities for prevention. Carcinogenesis. 31 (1): 100-110.
- Lim JE, Hong KW, Jin HS, Kim YS, Park HK, Oh B: Type 2 diabetes genetic association database manually curated for the study design and odds ratio. BMC Med Inform Decis Mak. 2010, 10: 76-10.1186/1472-6947-10-76.PubMedPubMed CentralView ArticleGoogle Scholar
- Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown PO, Brooks JD, Pollack JR: Gene expression profiling identifies clinically relevant subtypes of prostate cancer. Proc Natl Acad Sci USA. 2004, 101 (3): 811-816. 10.1073/pnas.0304146101.PubMedPubMed CentralView ArticleGoogle Scholar
- Collins FS, Morgan M, Patrinos A: The Human Genome Project: lessons from large-scale biology. Science. 2003, 300 (5617): 286-290. 10.1126/science.1084564.PubMedView ArticleGoogle Scholar
- Kallioniemi O: Functional genomics and transcriptomics of prostate cancer: promises and limitations. BJU Int. 2005, 96 (Suppl 2): 10-15.PubMedView ArticleGoogle Scholar
- Jiang J, Cui W, Vongsangnak W, Hu G, Shen B: Post genome-wide association studies functional characterization of prostate cancer risk loci. BMC Genomics. 2013, 14 (Suppl 8): S9-10.1186/1471-2164-14-S8-S9.PubMedPubMed CentralView ArticleGoogle Scholar
- Hu Y, Li J, Yan W, Chen J, Li Y, Hu G, Shen B: Identifying novel glioma associated pathways based on systems biology level meta-analysis. BMC systems biology. 2013, 7 (Suppl 2): S9-10.1186/1752-0509-7-S2-S9.PubMedPubMed CentralView ArticleGoogle Scholar
- Tang Y, Yan W, Chen J, Luo C, Kaipia A, Shen B: Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer. BMC systems biology. 2013, 7 (Suppl 2): S6-10.1186/1752-0509-7-S2-S6.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Y, Chen J, Li Q, Wang H, Liu G, Jing Q, Shen B: Identifying novel prostate cancer associated pathways based on integrative microarray data analysis. Comput Biol Chem. 2011, 35 (3): 151-158. 10.1016/j.compbiolchem.2011.04.003.PubMedView ArticleGoogle Scholar
- Chen J, Wang Y, Shen B, Zhang D: Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data. Comput Math Methods Med. 2013, 2013: 909525-PubMedPubMed CentralGoogle Scholar
- Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, Foekens JA: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005, 365 (9460): 671-679. 10.1016/S0140-6736(05)17947-1.PubMedView ArticleGoogle Scholar
- Chari R, Coe BP, Vucic EA, Lockwood WW, Lam WL: An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer. BMC Syst Biol. 4: 67-
- Nam H, Chung BC, Kim Y, Lee K, Lee D: Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics. 2009, 25 (23): 3151-3157. 10.1093/bioinformatics/btp558.PubMedView ArticleGoogle Scholar
- Fradet Y: Biomarkers in prostate cancer diagnosis and prognosis: beyond prostate-specific antigen. Curr Opin Urol. 2009, 19 (3): 243-246. 10.1097/MOU.0b013e32832a08b5.PubMedView ArticleGoogle Scholar
- Ideker T, Sharan R: Protein networks in disease. Genome Res. 2008, 18 (4): 644-652. 10.1101/gr.071852.107.PubMedPubMed CentralView ArticleGoogle Scholar
- Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007, 3: 140-PubMedPubMed CentralView ArticleGoogle Scholar
- Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009, 27 (2): 199-204. 10.1038/nbt.1522.PubMedView ArticleGoogle Scholar
- Guo Z, Wang L, Li Y, Gong X, Yao C, Ma W, Wang D, Zhu J, Zhang M, Yang D, Rao S, Wang J: Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. Bioinformatics. 2007, 23 (16): 2121-2128. 10.1093/bioinformatics/btm294.PubMedView ArticleGoogle Scholar
- Ma H, Schadt EE, Kaplan LM, Zhao H: COSINE: COndition-SpecIfic sub-NEtwork identification using a global optimization method. Bioinformatics. 2011, 27 (9): 1290-1298. 10.1093/bioinformatics/btr136.PubMedPubMed CentralView ArticleGoogle Scholar
- Qiu YQ, Zhang S, Zhang XS, Chen L: Detecting disease associated modules and prioritizing active genes based on high throughput data. BMC Bioinformatics. 2010, 11: 26-10.1186/1471-2105-11-26.PubMedPubMed CentralView ArticleGoogle Scholar
- Fortney K, Jurisica I: Integrative computational biology for cancer research. Hum Genet. 130 (4): 465-481.
- Joyce AR, Palsson BO: The model organism as a system: integrating 'omics' data sets. Nat Rev Mol Cell Biol. 2006, 7 (3): 198-210. 10.1038/nrm1857.PubMedView ArticleGoogle Scholar
- Brahmachari SK: Introducing the medical bioinformatics in Journal of Translational Medicine. J Transl Med. 10: 202-
- Maqungo M, Kaur M, Kwofie SK, Radovanovic A, Schaefer U, Schmeier S, Oppon E, Christoffels A, Bajic VB: DDPC: Dragon Database of Genes associated with Prostate Cancer. Nucleic Acids Res. 39 (Database): D980-985.
- Ekins S, Bugrim A, Brovold L, Kirillov E, Nikolsky Y, Rakhmatulin E, Sorokina S, Ryabov A, Serebryiskaya T, Melnikov A, Metz J, Nikolskaya T: Algorithms for network analysis in systems-ADME/Tox using the MetaCore and MetaDrug platforms. Xenobiotica. 2006, 36 (10-11): 877-901. 10.1080/00498250600861660.PubMedView ArticleGoogle Scholar
- Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA: Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33 (Database): D514-517.PubMedPubMed CentralGoogle Scholar
- Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004, 32 (Database): D277-280.PubMedPubMed CentralView ArticleGoogle Scholar
- Li LC, Zhao H, Shiina H, Kane CJ, Dahiya R: PGDB: a curated and integrated database of genes related to the prostate. Nucleic Acids Res. 2003, 31 (1): 291-293. 10.1093/nar/gkg008.PubMedPubMed CentralView ArticleGoogle Scholar
- Agarwal SM, Raghav D, Singh H, Raghava GP: CCDB: a curated database of genes involved in cervix cancer. Nucleic Acids Res. 39 (Database): D975-979.
- Shoop E, Casaes P, Onsongo G, Lesnett L, Petursdottir EO, Donkor EK, Tkach D, Cosimini M: Data exploration tools for the Gene Ontology database. Bioinformatics. 2004, 20 (18): 3442-3454. 10.1093/bioinformatics/bth425.PubMedView ArticleGoogle Scholar
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (Database): D535-539.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.PubMedView ArticleGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37 (1): 1-13. 10.1093/nar/gkn923.PubMedPubMed CentralView ArticleGoogle Scholar
- Smyth GK, Michaud J, Scott HS: Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics. 2005, 21 (9): 2067-2075. 10.1093/bioinformatics/bti270.PubMedView ArticleGoogle Scholar
- Dudoit S, Gentleman RC, Quackenbush J: Open source software for the analysis of microarray data. Biotechniques. 2003, 45-51. Suppl
- Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001, 125 (1-2): 279-284. 10.1016/S0166-4328(01)00297-2.PubMedView ArticleGoogle Scholar
- Pavlopoulos GA, Secrier M, Moschopoulos CN, Soldatos TG, Kossida S, Aerts J, Schneider R, Bagos PG: Using graph theory to analyze biological networks. BioData Min. 4: 10-
- Ideker T, Ozier O, Schwikowski B, Siegel AF: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002, 18 (Suppl 1): S233-240. 10.1093/bioinformatics/18.suppl_1.S233.PubMedView ArticleGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13 (11): 2498-2504. 10.1101/gr.1239303.PubMedPubMed CentralView ArticleGoogle Scholar
- Bauer CR, Epstein AM, Sweeney SJ, Zarnescu DC, Bosco G: Genetic and systems level analysis of Drosophila sticky/citron kinase and dFmr1 mutants reveals common regulation of genetic networks. BMC systems biology. 2008, 2: 101-10.1186/1752-0509-2-101.PubMedPubMed CentralView ArticleGoogle Scholar
- Stuart GR, Copeland WC, Strand MK: Construction and application of a protein and genetic interaction network (yeast interactome). Nucleic Acids Res. 2009, 37 (7): e54-10.1093/nar/gkp140.PubMedPubMed CentralView ArticleGoogle Scholar
- Meyer JN, Boyd WA, Azzam GA, Haugen AC, Freedman JH, Van Houten B: Decline of nucleotide excision repair capacity in aging Caenorhabditis elegans. Genome Biol. 2007, 8 (5): R70-10.1186/gb-2007-8-5-r70.PubMedPubMed CentralView ArticleGoogle Scholar
- Mayer ML, Sheridan JA, Blohmke CJ, Turvey SE, Hancock RE: The Pseudomonas aeruginosa autoinducer 3O-C12 homoserine lactone provokes hyperinflammatory responses from cystic fibrosis airway epithelial cells. PLoS One. 2011, 6 (1): e16246-10.1371/journal.pone.0016246.PubMedPubMed CentralView ArticleGoogle Scholar
- Cortes C VV: Support-vector networks. Mach Learn. 1995, 20: 273-297.Google Scholar
- Ng KL, Mishra SK: De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics. 2007, 23 (11): 1321-1330. 10.1093/bioinformatics/btm026.PubMedView ArticleGoogle Scholar
- Rice SB, Nenadic G, Stapley BJ: Mining protein function from text using term-based support vector machines. BMC Bioinformatics. 2005, 6 (Suppl 1): S22-10.1186/1471-2105-6-S1-S22.PubMedPubMed CentralView ArticleGoogle Scholar
- Son YJ, Kim HG, Kim EH, Choi S, Lee SK: Application of support vector machine for prediction of medication adherence in heart failure patients. Healthc Inform Res. 2010, 16 (4): 253-259. 10.4258/hir.2010.16.4.253.PubMedPubMed CentralView ArticleGoogle Scholar
- Wen Z, Liu ZP, Liu Z, Zhang Y, Chen L: An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer. J Am Med Inform Assoc. 2012Google Scholar
- Liu X, Liu ZP, Zhao XM, Chen L: Identifying disease genes and module biomarkers by differential interactions. J Am Med Inform Assoc. 2012, 19 (2): 241-248. 10.1136/amiajnl-2011-000658.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu JJ, Xiang Y: In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers. Methods Mol Biol. 2011, 754: 21-43. 10.1007/978-1-61779-154-3_2.PubMedView ArticleGoogle Scholar
- Karatzoglou BJ: kernlab - an S4 package for kernel methods in R. J Stat Softw. 2004Google Scholar
- Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics. 2005, 21 (20): 3940-3941. 10.1093/bioinformatics/bti623.PubMedView ArticleGoogle Scholar
- Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR: A census of human cancer genes. Nat Rev Cancer. 2004, 4 (3): 177-183. 10.1038/nrc1299.PubMedPubMed CentralView ArticleGoogle Scholar
- Becker KG, Barnes KC, Bright TJ, Wang SA: The genetic association database. Nat Genet. 2004, 36 (5): 431-432. 10.1038/ng0504-431.PubMedView ArticleGoogle Scholar
- Zhang HM, Chen H, Liu W, Liu H, Gong J, Wang H, Guo AY: AnimalTFDB: a comprehensive animal transcription factor database. Nucleic Acids Res. 2012, 40 (Database): D144-149.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu G, Ding M, Chen J, Huang J, Wang H, Jing Q, Shen B: Computational analysis of microRNA function in heart development. Acta Biochim Biophys Sin (Shanghai). 42 (9): 662-670.
- Li TQ, Feng CQ, Zou YG, Shi R, Liang S, Mao XM: Literature-mining and bioinformatic analysis of androgen-independent prostate cancer-specific genes. Zhonghua Nan Ke Xue. 2009, 15 (12): 1102-1107.PubMedGoogle Scholar
- Seshacharyulu P, Ponnusamy MP, Haridas D, Jain M, Ganti AK, Batra SK: Targeting the EGFR signaling pathway in cancer therapy. Expert Opin Ther Targets. 16 (1): 15-31.
- Teixeira AL, Gomes M, Medeiros R: EGFR signaling pathway and related- miRNAs in age-related diseases: the example of miR-221 and miR-222. Front Genet. 3: 286-
- Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer. Nature. 2001, 412 (6849): 822-826. 10.1038/35090585.PubMedView ArticleGoogle Scholar
- Larkin SE, Holmes S, Cree IA, Walker T, Basketter V, Bickers B, Harris S, Garbis SD, Townsend PA, Aukim-Hastie C: Identification of markers of prostate cancer progression using candidate gene expression. Br J Cancer. 2012, 106 (1): 157-165. 10.1038/bjc.2011.490.PubMedPubMed CentralView ArticleGoogle Scholar
- Kaur M, MacPherson CR, Schmeier S, Narasimhan K, Choolani M, Bajic VB: In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer. BMC systems biology. 2011, 5: 144-10.1186/1752-0509-5-144.PubMedPubMed CentralView ArticleGoogle Scholar
- Chng KR, Cheung E: Sequencing the transcriptional network of androgen receptor in prostate cancer. Cancer Lett. 2012Google Scholar
- Kohli M, Qin R, Jimenez R, Dehm SM: Biomarker-based targeting of the androgen-androgen receptor axis in advanced prostate cancer. Adv Urol. 2012, 2012: 781459-PubMedPubMed CentralView ArticleGoogle Scholar
- Verma MP, P , Verma M: Biomarkers in Prostate Cancer Epidemiology. Cancers. 2011, 3: 3773-3798. 10.3390/cancers3043773.PubMedPubMed CentralView ArticleGoogle Scholar
- Willard SS, Koochekpour S: Regulators of gene expression as biomarkers for prostate cancer. Am J Cancer Res. 2012, 2 (6): 620-657.PubMedPubMed CentralGoogle Scholar
- Yeh HY, Cheng SW, Lin YC, Yeh CY, Lin SF, Soo VW: Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency. BMC Med Genomics. 2009, 2: 70-10.1186/1755-8794-2-70.PubMedPubMed CentralView ArticleGoogle Scholar
- Sankpal UT, Goodison S, Abdelrahim M, Basha R: Targeting Sp1 transcription factors in prostate cancer therapy. Med Chem. 7 (5): 518-525.
- Eisermann K, Broderick CJ, Bazarov A, Moazam MM, Fraizer GC: Androgen up-regulates vascular endothelial growth factor expression in prostate cancer cells via an Sp1 binding site. Mol Cancer. 2013, 12: 7-10.1186/1476-4598-12-7.PubMedPubMed CentralView ArticleGoogle Scholar
- Yuan H, Gong A, Young CY: Involvement of transcription factor Sp1 in quercetin-mediated inhibitory effect on the androgen receptor in human prostate cancer cells. Carcinogenesis. 2005, 26 (4): 793-801. 10.1093/carcin/bgi021.PubMedView ArticleGoogle Scholar
- Jiang NY, Woda BA, Banner BF, Whalen GF, Dresser KA, Lu D: Sp1, a new biomarker that identifies a subset of aggressive pancreatic ductal adenocarcinoma. Cancer Epidemiol Biomarkers Prev. 2008, 17 (7): 1648-1652. 10.1158/1055-9965.EPI-07-2791.PubMedView ArticleGoogle Scholar
- Agarwal SM, Raghav D, Singh H, Raghava GP: CCDB: a curated database of genes involved in cervix cancer. Nucleic Acids Res. 2011, 39 (Database): D975-979. 10.1093/nar/gkq1024.PubMedPubMed CentralView ArticleGoogle Scholar
- Varambally S, Yu J, Laxman B, Rhodes DR, Mehra R, Tomlins SA, Shah RB, Chandran U, Monzon FA, Becich MJ, Wei JT, Pienta KJ, Ghosh D, Rubin MA, Chinnaiyan AM: Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression. Cancer Cell. 2005, 8 (5): 393-406. 10.1016/j.ccr.2005.10.001.PubMedView ArticleGoogle Scholar
- Tomlins SA, Mehra R, Rhodes DR, Cao X, Wang L, Dhanasekaran SM, Kalyana-Sundaram S, Wei JT, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM: Integrative molecular concept modeling of prostate cancer progression. Nat Genet. 2007, 39 (1): 41-51. 10.1038/ng1935.PubMedView ArticleGoogle Scholar
- Kim JH, Dhanasekaran SM, Prensner JR, Cao X, Robinson D, Kalyana- Sundaram S, Huang C, Shankar S, Jing X, Iyer M, Hu M, Sam L, Grasso C, Maher CA, Palanisamy N, Mehra R, Kominsky HD, Siddiqui J, Yu J, Qin ZS, Chinnaiyan AM: Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer. Genome Res. 2011, 21 (7): 1028-1041. 10.1101/gr.119347.110.PubMedPubMed CentralView ArticleGoogle Scholar
- Nanni S, Priolo C, Grasselli A, D'Eletto M, Merola R, Moretti F, Gallucci M, De Carli P, Sentinelli S, Cianciulli AM, Mottolese M, Carlini P, Arcelli D, Helmer-Citterich M, Gaetano C, Loda M, Pontecorvi A, Bacchetti S, Sacchi A, Farsetti A: Epithelial-restricted gene profile of primary cultures from human prostate tumors: a molecular approach to predict clinical behavior of prostate cancer. Mol Cancer Res. 2006, 4 (2): 79-92. 10.1158/1541-7786.MCR-05-0098.PubMedView ArticleGoogle Scholar
- Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J, Moskaluk CA, Frierson HF, Hampton GM: Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res. 2001, 61 (16): 5974-5978.PubMedGoogle Scholar
- Wang Y, Xia XQ, Jia Z, Sawyers A, Yao H, Wang-Rodriquez J, Mercola D, McClelland M: In silico estimates of tissue components in surgical samples based on expression profiling data. Cancer Res. 2010, 70 (16): 6448-6455. 10.1158/0008-5472.CAN-10-0021.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia Z, Wang Y, Sawyers A, Yao H, Rahmatpanah F, Xia XQ, Xu Q, Pio R, Turan T, Koziol JA, Goodison S, Carpenter P, Wang-Rodriguez J, Simoneau A, Meyskens F, Sutton M, Lernhardt W, Beach T, Monforte J, McClelland M, Mercola D: Diagnosis of prostate cancer using differentially expressed genes in stroma. Cancer Res. 2011, 71 (7): 2476-2487. 10.1158/0008-5472.CAN-10-2585.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen JH, He HC, Jiang FN, Militar J, Ran PY, Qin GQ, Cai C, Chen XB, Zhao J, Mo ZY, Chen YR, Zhu JG, Liu X, Zhong WD: Analysis of the specific pathways and networks of prostate cancer for gene expression profiles in the Chinese population. Med Oncol. 2012, 29 (3): 1972-1984. 10.1007/s12032-011-0088-5.PubMedView ArticleGoogle Scholar
- Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, Liang W, Michalopoulos G, Becich M, Monzon FA: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process. BMC Cancer. 2007, 7: 64-10.1186/1471-2407-7-64.PubMedPubMed CentralView ArticleGoogle Scholar
- Yu YP, Landsittel D, Jing L, Nelson J, Ren B, Liu L, McDonald C, Thomas R, Dhir R, Finkelstein S, Michalopoulos G, Becich M, Luo JH: Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. J Clin Oncol. 2004, 22 (14): 2790-2799. 10.1200/JCO.2004.05.158.PubMedView ArticleGoogle Scholar
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