Identification of miR-30d as a novel prognostic maker of prostate cancer

Kobayashi, N; Uemura, Hiroji; Nagahama, Kiyotaka; Okihara, Koji; Furuya, Mitsuko; Ino, Yoko; Ito, Yusuke; Hirano, Hisashi; Inayama, Yoshiaki; Aoki, Ichiro; Nagashima, Yoji; Kubota, Yoshinobu; Ishiguro, Hitoshi

doi:10.18632/oncotarget.696

Cited by 83 publications

(75 citation statements)

References 37 publications

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“…Kobayashi et al (28) observed significantly higher expression levels of miR-30d in three PC cell lines compared with those in two normal prostate cell lines using miRNA microarray and quantitative polymerase chain reaction analysis (28). Furthermore, the authors suggested that miR-30d mediated its effects in PC by downregulating suppressor of cytokine signaling 1.…”

Section: Discussionmentioning

confidence: 99%

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

Guo

Zhu

et al. 2017

Oncol Lett

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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 |log 2 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.

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Section: Discussionmentioning

confidence: 99%

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

Guo

Zhu

et al. 2017

Oncol Lett

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“…The regulation of Livin expression may involve miR-198 in prostate cancer cell lines (Ye et al, 2013). Kobayashi et al (2012) report significantly higher expression of miR-30d in three prostate cell lines (PC3, DU145, and LNCaP) compared with two normal prostate cell lines (RWPE-1 and PrSc) using miRNA microarrays and qPCR. Using reporter gene assay, they identify miR-30d as a downregulator of SOCS1 expression by directly binding to 3¢-UTR of SOCS1.…”

mentioning

confidence: 98%

Screening Biomarkers of Prostate Cancer by Integrating microRNA and mRNA Microarrays

Feng

Chi-bing

Diao

et al. 2013

Genetic Testing and Molecular Biomarkers

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Objective: In this study, we screened microRNA (miRNA) target genes of prostate cancer by integrating miRNA and mRNA expression profiles after target prediction and performed function enrichment analysis for selected candidate genes. Methods: The miRNA expression profile (GSE36802) and mRNA expression profile (GSE36801) were downloaded from the Gene Expression Omnibus database. We processed data and identified the differentially expressed miRNAs and mRNAs with R packages. Verified targets of miRNAs were identified through miRecods and miRTarBase. Then, software of Search Tool for the Retrieval of Interacting Genes was used to construct the interaction network of target genes. Finally, we performed function enrichment analysis for genes in the interaction network with the Functional Classification Tool. Results: A total of 22 upregulated and 8 downregulated miRNAs were detected in this study, of which, hsa-mir-31 was the most overexpressed miRNA in prostate cancer. Both ITGA5 and RDX, two target genes of hsa-mir-31, were found to be differentially expressed from mRNA profiles by overexpressing hsa-mir-31. The cell adhesion molecule was found to be the most significant pathway enriched by ITGA5 and RDX. Conclusion: Overexpression of hsa-mir-31 can be a significant marker to distinguish cancer tissues from benign tissues. The targets such as ITGA5 and RDX regulated by hsamir-31 are candidate genes of prostate cancer, which provide new treatment strategies for its gene therapy.

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“…Several studies have reported that numerous miRNAs may be independent biochemical recurrence prediction markers (35)(36)(37)(38). The present study aimed to investigate the potential of miR-17-92 cluster derived from surgery at the initial visit in predicting the possibility of PCa progression or metastasis, and the results obtained demonstrated that the expression status of the miR-17-92 cluster was a good prognostic marker for time to progression to biochemical recurrence.…”

Section: Discussionmentioning

confidence: 80%

Combinations of elevated tissue miRNA‑17‑92 cluster expression and serum prostate‑specific antigen as potential diagnostic biomarkers for prostate cancer

Feng

Qian

et al. 2017

Oncol Lett

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Abstract. The aim of the present study was to investigate the effectiveness of the miR-17-92 cluster as a disease progression marker in prostate cancer (PCa). Reverse transcription-quantitative polymerase chain reaction analysis was used to detect the microRNA (miR)-17-92 cluster expression levels in tissues from patients with PCa or benign prostatic hyperplasia (BPH), in addition to in PCa and BPH cell lines. Spearman correlation was used for comparison and estimation of correlations between miRNA expression levels and clinicopathological characteristics such as the Gleason score and prostate-specific antigen (PSA). Receiver operating curve (ROC) analysis was performed for evaluation of specificity and sensitivity of miR-17-92 cluster expression levels for discriminating patients with PCa from patients with BPH. Kaplan-Meier analysis was plotted to investigate the predictive potential of miR-17-92 cluster for PCa biochemical recurrence. Expression of the majority of miRNAs in the miR-17-92 cluster was identified to be significantly increased in PCa tissues and cell lines. Bivariate correlation analysis indicated that the high expression of unregulated miRNAs was positively correlated with Gleason grade, but had no significant association with PSA. ROC curves demonstrated that high expression of miR-17-92 cluster predicted a higher diagnostic accuracy compared with PSA. Improved discriminating quotients were observed when combinations of unregulated miRNAs with PSA were used. Survival analysis confirmed a high combined miRNA score of miR-17-92 cluster was associated with shorter biochemical recurrence interval. miR-17-92 cluster could be a potential diagnostic and prognostic biomarker for PCa, and the combination of the miR-17-92 cluster and serum PSA may enhance the accuracy for diagnosis of PCa.

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Identification of miR-30d as a novel prognostic maker of prostate cancer

Cited by 83 publications

References 37 publications

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

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

Screening Biomarkers of Prostate Cancer by Integrating microRNA and mRNA Microarrays

Combinations of elevated tissue miRNA‑17‑92 cluster expression and serum prostate‑specific antigen as potential diagnostic biomarkers for prostate cancer

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