Dataset Information

Comparison of miRNA and gene expression profiles between metastatic and primary prostate cancer.

ABSTRACT: The present study aimed to identify the regulatory mechanisms associated with the metastasis of prostate cancer (PC). The microRNA (miRNA/miR) microarray dataset GSE21036 and gene transcript dataset GSE21034 were downloaded from the Gene Expression Omnibus database. Following pre-processing, differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) between samples from patients with primary prostate cancer (PPC) and metastatic prostate cancer (MPC) with |log2 fold change (FC)| >1 and a false discovery rate <0.05 were selected using the Linear Models for Microarray and RNA-seq Data 4 package of R. Next, a DEM-DEG regulatory network was constructed by downloading miRNA-DEG pairs from the miRNA.org database. Finally, functional annotation of each DEM-DEG module was performed using the Database for Annotation, Visualization and Integrated Discovery based on the Gene Ontology database. The upregulated miRNAs, including miR-144, miR-494 and miR-181a, exhibited a higher degree of connections compared with other nodes, including in the DEM-DEG regulatory network, and regulated a number of downregulated DEGs. According to the functional annotation of the DEM-DEG modules, miR-144 and its targeted DEGs enriched the highest number of biological process terms (36 terms), followed by miR-494 (24 terms), miR-30d (18 terms), miR-181a (15 terms), hsa-miR-196a (8 terms), miR-708 (7 terms) and miR-486-5p (2 terms). Therefore, these miRNAs may serve roles in the metastasis of PC cells via downregulation of their corresponding target DEGs.

SUBMITTER: Guo K

PROVIDER: S-EPMC5661486 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Comparison of miRNA and gene expression profiles between metastatic and primary prostate cancer.

Guo Kaimin K Liang Zuowen Z Li Fubiao F Wang Hongliang H

Oncology letters 20170915 5

The present study aimed to identify the regulatory mechanisms associated with the metastasis of prostate cancer (PC). The microRNA (miRNA/miR) microarray dataset GSE21036 and gene transcript dataset GSE21034 were downloaded from the Gene Expression Omnibus database. Following pre-processing, differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) between samples from patients with primary prostate cancer (PPC) and metastatic prostate cancer (MPC) with |log<sub>2</sub> fo ...[more]

PMID: 29113250

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Project description:BackgroundClinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa.ObjectiveTo identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa).Design, setting, and participantsTo develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers.Outcome measurements and statistical analysisThe outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures.Results and limitationsA close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures.ConclusionsProfiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors.Patient summaryOur stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.

Project description:Recent studies have shown the contribution of miRNAs to cancer pathogenesis. Prostate cancer is the most commonly diagnosed cancer in men. Unlike other major types of cancer, no single gene has been identified as being mutated in the majority of prostate tumors. This implies that the expression profiling of genes, including the non-coding miRNAs, may substantially vary across individual cases of this cancer. The within-class variability makes it possible to reconstruct or infer disease-specific miRNA-mRNA correlation and regulatory modular networks using high-dimensional microarray data of prostate tumor samples. Furthermore, since miRNAs and tumor suppressor genes are usually tissue specific, miRNA-mRNA modules could potentially differ between primary prostate cancer (PPC) and metastatic prostate cancer (MPC). We herein performed an in silico analysis to explore the miRNA-mRNA correlation network modules in the two tumor subtypes. Our analysis identified 5 miRNA-mRNA module pairs (MPs) for PPC and MPC, respectively. Each MP includes one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs (genes) in each module varies from 2 to 8 or from 6 to 622. The modules discovered for PPC are more informative than those for MPC in terms of the implicated biological insights. In particular, one negative-connection module in PPC fits well with the popularly recognized miRNA-mediated post-transcriptional regulation theory. That is, the 3'UTR sequences of the involved mRNAs (?620) are enriched with the target site motifs of the 7 modular miRNAs, has-miR-106b, -191, -19b, -92a, -92b, -93, and -141. About 330 GO terms and KEGG pathways, including TGF-beta signaling pathway that maintains tissue homeostasis and plays a crucial role in the suppression of the proliferation of cancer cells, are over-represented (adj.p<0.05) in the modular gene list. These computationally identified modules provide remarkable biological evidence for the interference of miRNAs in the development of prostate cancers and warrant additional follow-up in independent laboratory studies.

Dataset Information

Comparison of miRNA and gene expression profiles between metastatic and primary prostate cancer.

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Comparison of miRNA and gene expression profiles between metastatic and primary prostate cancer.

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