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Proteomics, pathway array and signaling network-based medicine in cancer.

ABSTRACT: Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures can be used for cancer diagnosis, prognosis, and personalized treatment. During the past several decades, the available technology to study signaling networks has significantly evolved to include such platforms as genomic microarray (expression array, SNP array, CGH array, etc.) and proteomic analysis, which globally assesses genetic, epigenetic, and proteomic alterations in cancer. In this review, we compared Pathway Array analysis with other proteomic approaches in analyzing protein network involved in cancer and its utility serving as cancer biomarkers in diagnosis, prognosis and therapeutic target identification. With the advent of bioinformatics, constructing high complexity signaling networks is possible. As the use of signaling network-based cancer diagnosis, prognosis and treatment is anticipated in the near future, medical and scientific communities should be prepared to apply these techniques to further enhance personalized medicine.

SUBMITTER: Zhang DY

PROVIDER: S-EPMC2780394 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Proteomics, pathway array and signaling network-based medicine in cancer.

Zhang David Y DY Ye Fei F Gao Ling L Liu Xiaoliang X Zhao Xin X Che Yufang Y Wang Hongxia H Wang Libo L Wu Josephine J Song Dong D Liu Wei W Xu Hong H Jiang Bo B Zhang Weijia W Wang Jinhua J Lee Peng P

Cell division 20091028

Cancer is a multifaceted disease that results from dysregulated normal cellular signaling networks caused by genetic, genomic and epigenetic alterations at cell or tissue levels. Uncovering the underlying protein signaling network changes, including cell cycle gene networks in cancer, aids in understanding the molecular mechanism of carcinogenesis and identifies the characteristic signaling network signatures unique for different cancers and specific cancer subtypes. The identified signatures ca ...[more]

PMID: 19863813

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Project description:RelevanceMolecular network changes are the hallmark of the pathogenesis of ovarian cancers (OCs). Network-based biomarkers benefit for the effective treatment of OC.PurposeThis study sought to identify key pathway-network alterations and network-based biomarkers for clarification of molecular mechanisms and treatment of OCs.MethodsIngenuity Pathway Analysis (IPA) platform was used to mine signaling pathway networks with 1198 human tissue mitochondrial differentially expressed proteins (mtDEPs) and compared those pathway network changes between OCs and controls. The mtDEPs in important cancer-related pathway systems were further validated with qRT-PCR and Western blot in OC cell models. Moreover, integrative analysis of mtDEPs and Cancer Genome Atlas (TCGA) data from 419 patients was used to identify hub molecules with molecular complex detection method. Hub molecule-based survival analysis and multiple multivariate regression analysis were used to identify survival-related hub molecules and hub molecule signature model.ResultsPathway network analysis revealed 25 statistically significant networks, 192 canonical pathways, and 5 significant molecular/cellular function models. A total of 52 canonical pathways were activated or inhibited in cancer pathogenesis, including antigen presentation, mitochondrial dysfunction, GP6 signaling, EIF2 signaling, and glutathione-mediated detoxification. Of them, mtDEPs (TPM1, CALR, GSTP1, LYN, AKAP12, and CPT2) in those canonical pathway and molecular/cellular models were validated in OC cell models at the mRNA and protein levels. Moreover, 102 hub molecules were identified, and they were regulated by post-translational modifications and functioned in multiple biological processes. Of them, 62 hub molecules were individually significantly related to OC survival risk. Furthermore, multivariate regression analysis of 102 hub molecules identified significant seven hub molecule signature models (HIST1H2BK, ALB, RRAS2, HIBCH, EIF3E, RPS20, and RPL23A) to assess OC survival risks.ConclusionThese findings provided the overall signaling pathway network profiling of human OCs; offered scientific data to discover pathway network-based cancer biomarkers for diagnosis, prognosis, and treatment of OCs; and clarify accurate molecular mechanisms and therapeutic targets. These findings benefit for the discovery of effective and reliable biomarkers based on pathway networks for OC predictive and personalized medicine.

Project description:Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ER? requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ER? on breast cancer.To investigate the regulatory network of ER? and discover novel modulators of ER? functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ER? binding. Network formed from targets genes with ER? binding was called ER? genomic regulatory network; while network formed from targets genes without ER? binding was called ER? non-genomic regulatory network. Considering the active or repressive function of ER?, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ER?, the ER?/modulator/target relationships were categorized into 27 classes.Using the gene expression data and ER? Chip-seq data from the MCF-7 cell line, the ER? genomic/non-genomic regulatory networks were built by merging ER?/ modulator/target triplets (TF, M, T), where TF refers to the ER?, M refers to the modulator, and T refers to the target. Comparing these two networks, ER? non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ER? genomic regulatory network, but 4% overlap for the non-genomic regulatory network.We proposed a novel approach to infer the ER?/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network.

Project description:AimEarly diagnosis of prostate cancer (PCa), which is a clinically heterogeneous-multifocal disease, is essential to improve the prognosis of patients. However, published PCa diagnostic markers share little overlap and are poorly validated using independent data. Therefore, we here developed an integrative proteomics and interaction network-based classifier by combining the differential protein expression with topological features of human protein interaction networks to enhance the ability of PCa diagnosis.Methods and resultsBy two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with MS using PCa and adjacent benign tissues of prostate, a total of 60 proteins with the differential expression in PCa tissues were identified as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and three hub proteins (PTEN, SFPQ and HDAC1) were chosen. After that, a PCa diagnostic classifier was constructed by support vector machine (SVM) modeling based on the microarray gene expression data of the genes which encode the hub proteins mentioned above. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.96?90.18%) and area under ROC curve (approximating 1.0). Furthermore, the clinical significance of PTEN, SFPQ and HDAC1 proteins in PCa was validated by both ELISA and immunohistochemistry analyses. More interestingly, PTEN protein was identified as an independent prognostic marker for biochemical recurrence-free survival in PCa patients according to the multivariate analysis by Cox Regression.ConclusionsOur data indicated that the integrative proteomics and interaction network-based classifier which combines the differential protein expression and topological features of human protein interaction network may be a powerful tool for the diagnosis of PCa. We also identified PTEN protein as a novel prognostic marker for biochemical recurrence-free survival in PCa patients.

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Proteomics, pathway array and signaling network-based medicine in cancer.

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Proteomics, pathway array and signaling network-based medicine in cancer.

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