Dataset Information

The Distinct Gene Regulatory Network of Myoglobin in Prostate and Breast Cancer.

ABSTRACT: Myoglobin (MB) is not only strongly expressed in myocytes, but also at much lower levels in different cancer entities. 40% of breast tumors are MB-positive, with the globin being co-expressed with markers of tumor hypoxia in a proportion of cases. In breast cancer, MB expression is associated with a positive hormone receptor status and patient prognosis. In prostate cancer, another hormone-dependent cancer type, 53% of tumors were recently shown to express MB. Especially in more aggressive prostate cancer specimen MB expression also correlates with increased patient survival rates. Both findings might be due to tumor-suppressing properties of MB in cancer cells. In contrast to muscle, MB transcription in breast and prostate cancer mainly depends on a novel, alternative promoter site. We show here that its associated transcripts can be upregulated by hypoxia and downregulated by estrogens and androgens in MCF7 breast and LNCaP prostate cancer cells, respectively. Bioinformatic data mining of epigenetic histone marks and experimental verification reveal a hitherto uncharacterized transcriptional network that drives the regulation of the MB gene in cancer cells. We identified candidate hormone-receptor binding elements that may interact with the cancer-associated MB promoter to decrease its activity in breast and prostate cancer cells. Additionally, a regulatory element, 250 kb downstream of the promoter, acts as a hypoxia-inducible site within the transcriptional machinery. Understanding the distinct regulation of MB in tumors will improve unraveling the respiratory protein's function in the cancer context and may provide new starting points for developing therapeutic strategies.

SUBMITTER: Bicker A

PROVIDER: S-EPMC4641586 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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The Distinct Gene Regulatory Network of Myoglobin in Prostate and Breast Cancer.

Bicker Anne A Brahmer Alexandra M AM Meller Sebastian S Kristiansen Glen G Gorr Thomas A TA Hankeln Thomas T

PloS one 20151111 11

Myoglobin (MB) is not only strongly expressed in myocytes, but also at much lower levels in different cancer entities. 40% of breast tumors are MB-positive, with the globin being co-expressed with markers of tumor hypoxia in a proportion of cases. In breast cancer, MB expression is associated with a positive hormone receptor status and patient prognosis. In prostate cancer, another hormone-dependent cancer type, 53% of tumors were recently shown to express MB. Especially in more aggressive prost ...[more]

PMID: 26559958

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Project description:BackgroundMining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes.ResultsHere, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations.ConclusionIn conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

Project description:BackgroundTranslocase of inner mitochondrial membrane 17A (TIMM17A) is overexpressed in breast cancer (BRCA), and upregulation can increase the aggressiveness of BRCA cells. This study examined the influence of the TIMM17A gene network on BRCA outcome.MethodsExpression levels of TIMM17A were compared between normal and tumor tissues from the OncomineTM database, and the association with patient survival was analyzed using Kaplan-Meier Plotter. Clinical factors influencing TIMM17A expression were studied by UALCAN. cBioPotal was then used to identify genes interacting with TIMM17A, and network relationships were assessed using the R clusterProfiler package. The association between TIMM17A mutation and mRNA expression in BRCA was examined using the LinkFinder application in LinkedOmics, and coexpressed genes were assessed for functional enrichment using the LinkInterpreter application. Furthermore, TIMM17A expression correlation with cell cycle phase distribution was performed by flow cytometry. Finally, the target networks of kinases, microRNAs (miRNAs), and transcription factors were identified using GeneMANIA. The expression and correlation of potential miRNAs and targets were further validated in BRCA cell lines by qRT-PCR.ResultsExpression of TIMM17A was significantly elevated in BRCA compared with normal tissue (p < 0.05), and overexpression was associated with both poor overall survival (OS) and shorter distant metastasis-free survival (DMFS) (p < 0.05). Expression of TIMM17A was not associated with age, sex, BRCA subclass, clinical stage, or patient ethnicity. The coexpressed TIMM17A network was enriched in genes targeted by cell cycle regulators such as CDK1, miR-331, and E2F family transcription factors (FDR < 0.001). Furthermore, flow cytometry revealed a strong association between higher TIMM17A expression and faster cell cycle progression in these BRCA cell lines. In addition, expression of TIMM17A protein was correlated with CDK1 protein expression in BRCA cell lines as measured by western blotting.ConclusionElevated TIMM17A expression accelerates the progression of BRCA, thereby reducing OS and DMFS. The TIMM17A-associated networks identified here provide clues to the molecular pathogenesis of BRCA and potential targets for BRCA treatment.

Dataset Information

The Distinct Gene Regulatory Network of Myoglobin in Prostate and Breast Cancer.

Publications

The Distinct Gene Regulatory Network of Myoglobin in Prostate and Breast Cancer.

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