Meta-analysis of Gene Expression Data Identifies Causal Genes for Prostate Cancer

Wang, Xiangyang; Hao, Jianwei; Zhou, Ran; Zhang, Xiangsheng; Yan, Tian-Zhong; Ding, Degang; Lei, Shan

doi:10.7314/apjcp.2013.14.1.457

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Meta-analysis based approaches in pancreatic cancer have led to identification of novel targets and candidate biomarkers [ 12 ]. In prostate cancers, markers causal to the carcinogenic process were identified using a similar approach [ 13 ]. In addition, markers highly associated with prognosis and treatment outcome prediction in breast cancer [ 14 ] [ 15 ] were also identified through similar meta-analysis based approaches.…”

Section: Introductionmentioning

confidence: 99%

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer

et al. 2016

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The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (ECT2, ANO1, TP63, FADD, EXT1, NCBP2) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed ANO1 had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. UBE2V2, PLAC8, FADD and TTK showed high sensitivity (1.00) in Group I while UBE2V2 and CRYM were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that ANO1 and FADD, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (p<0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance.

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Section: Introductionmentioning

confidence: 99%

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer

et al. 2016

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“…Gene expression microarray technology, which can observe the expression of thousands of genes within a single experiment, has been widely used in the study of cancer. Gene expression profiles have shown that numerous molecular pathways are activated persistently in metastatic processes, and multiple genes are significantly altered during tumor progression (3). Thus, identification of the functions of these pathways and the genes involved in them, may provide important information for use in the development of diagnostic tools and therapies for cancer.…”

Section: Introductionmentioning

confidence: 99%

Aquaporin 3 promotes prostate cancer cell motility and invasion via extracellular signal-regulated kinase 1/2-mediated matrix metalloproteinase-3 secretion

Chen

Wang

et al. 2014

Molecular Medicine Reports

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Aquaporins (AQPs) are known to be important in cancer progression. The present study used a cDNA microarray to demonstrate that AQP3, a member of the AQP family, was overexpressed in prostate cancer cells. In order to determine the role of AQP3 in prostate cancer, AQP3 expression was silenced by transfection with small interfering RNA, following which wound healing and invasion assays were conducted in DU‑145 and PC‑3 cells. Notably, the results showed that silencing of AQP3 suppressed prostate cancer cell motility and invasion. The possible mechanisms underlying the AQP3‑enhanced motility and invasion of prostate cancer cells was also investigated. Knockdown of AQP3 expression was shown to reduce extracellular signal‑regulated kinase 1/2 (ERK1/2) activation in DU‑145 and PC‑3 cells. Blocking the ERK pathway inhibited AQP3‑mediated motility and invasion of these cells. Finally, the results demonstrated that AQP3 upregulated matrix metalloproteinase‑3 (MMP‑3) expression and secretion in prostate cancer cells via activation of the ERK pathway. In conclusion, the present study suggests that AQP3 induces ERK1/2 activation, thereby increasing MMP‑3 expression and secretion, which in turn promotes prostate cancer cell motility and invasion. Thus, AQP3 may be considered to be a potential therapeutic target for prostate cancer.

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“…The gene regulates telomerase protein component 1 (TLP1) expression and telomerase activity, thus enabling certain prostate cells to resist apoptosis. Multiple works have proved that PARM1 is an important causal gene of prostate cancer [38-40]. It contributes to the immortalization of prostatic cancer cells, which enhances the survival advantage against the neighboring native cells and promotes metastasis.…”

Section: Discussionmentioning

confidence: 99%

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

Dong

Liu

2014

Theor Biol Med Model

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BackgroundProstate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis – the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development.ResultsUsing the maximum relevance minimum redundancy (mRMR) method on microarray data from normal tissues, primary tumors and metastatic tumors, we identifed four genes that can optimally classify samples of different prostate cancer phases. Moreover, we constructed a molecular interaction network with existing bioinformatic resources and co-identifed eight genes on the shortest-paths among the mRMR-identified genes, which are potential co-acting factors of prostate cancer. Functional analyses show that molecular functions involved in cell communication, hormone-receptor mediated signaling, and transcription regulation play important roles in the development of prostate cancer.ConclusionWe conclude that the surrogate genes we have selected compose an effective classifier of prostate cancer phases, which corresponds to a minimum characterization of cancer phenotypes on the molecular level. Along with their molecular interaction partners, it is fairly to assume that these genes may have important roles in prostate cancer development; particularly, the un-reported genes may bring new insights for the understanding of the molecular mechanisms. Thus our results may serve as a candidate gene set for further functional studies.

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Meta-analysis of Gene Expression Data Identifies Causal Genes for Prostate Cancer

Cited by 11 publications

References 35 publications

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer

Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer

Aquaporin 3 promotes prostate cancer cell motility and invasion via extracellular signal-regulated kinase 1/2-mediated matrix metalloproteinase-3 secretion

Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis

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