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      Clinical Value of miR-101-3p and Biological Analysis of its Prospective Targets in Breast Cancer: A Study Based on The Cancer Genome Atlas (TCGA) and Bioinformatics

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          Abstract

          Background

          MiR-101-3p can promote apoptosis and inhibit proliferation, invasion, and metastasis in breast cancer (BC) cells. However, its mechanisms in BC are not fully understood. Therefore, a comprehensive analysis of the target genes, pathways, and networks of miR-101-3p in BC is necessary.

          Material/Methods

          The miR-101 profiles for 781 patients with BC from The Cancer Genome Atlas (TCGA) were analyzed. Gene expression profiling of GSE31397 with miR-101-3p transfected MCF-7 cells and scramble control cells was downloaded from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified. The potential genes targeted by miR-101-3p were also predicted. Gene Ontology (GO) and pathway and network analyses were constructed for the DEGs and predicted genes.

          Results

          In the TCGA data, a low level of miR-101-2 expression might represent a diagnostic (AUC: 0.63) marker, and the miR-101-1 was a prognostic (HR=1.79) marker. MiR-101-1 was linked to the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), and miR-101-2 was associated with the tumor (T), lymph node (N), and metastasis (M) stages of BC. Moreover, 427 genes were selected from the 921 DEGs in GEO and the 7924 potential target genes from the prediction databases. These genes were related to transcription, metabolism, biosynthesis, and proliferation. The results were also significantly enriched in the VEGF, mTOR, focal adhesion, Wnt, and chemokine signaling pathways.

          Conclusions

          MiR-101-1 and miR-101-2 may be prospective biomarkers for the prognosis and diagnosis of BC, respectively, and are associated with diverse clinical parameters. The target genes of miR-101-3p regulate the development and progression of BC. These results provide insight into the pathogenic mechanism and potential therapies for BC.

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          Most cited references36

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          Cancer statistics in China, 2015.

          With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.
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            DAVID: Database for Annotation, Visualization, and Integrated Discovery

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              A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments.

              Wei Pan (2002)
              A common task in analyzing microarray data is to determine which genes are differentially expressed across two kinds of tissue samples or samples obtained under two experimental conditions. Recently several statistical methods have been proposed to accomplish this goal when there are replicated samples under each condition. However, it may not be clear how these methods compare with each other. Our main goal here is to compare three methods, the t-test, a regression modeling approach (Thomas et al., Genome Res., 11, 1227-1236, 2001) and a mixture model approach (Pan et al., http://www.biostat.umn.edu/cgi-bin/rrs?print+2001,2001a,b) with particular attention to their different modeling assumptions. It is pointed out that all the three methods are based on using the two-sample t-statistic or its minor variation, but they differ in how to associate a statistical significance level to the corresponding statistic, leading to possibly large difference in the resulting significance levels and the numbers of genes detected. In particular, we give an explicit formula for the test statistic used in the regression approach. Using the leukemia data of Golub et al. (Science, 285, 531-537, 1999), we illustrate these points. We also briefly compare the results with those of several other methods, including the empirical Bayesian method of Efron et al. (J. Am. Stat. Assoc., to appear, 2001) and the Significance Analysis of Microarray (SAM) method of Tusher et al. (PROC: Natl Acad. Sci. USA, 98, 5116-5121, 2001).
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                Author and article information

                Journal
                Med Sci Monit
                Med. Sci. Monit
                Medical Science Monitor
                Medical Science Monitor : International Medical Journal of Experimental and Clinical Research
                International Scientific Literature, Inc.
                1234-1010
                1643-3750
                2017
                18 April 2017
                : 23
                : 1857-1871
                Affiliations
                [1 ]Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
                [2 ]Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
                [3 ]Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
                Author notes
                Corresponding Authors: Hua-wei Zhu, e-mail: 526202641@ 123456qq.com , Gang Chen, e-mail: chen_gang_triones@ 123456163.com
                [A]

                Study Design

                [B]

                Data Collection

                [C]

                Statistical Analysis

                [D]

                Data Interpretation

                [E]

                Manuscript Preparation

                [F]

                Literature Search

                [G]

                Funds Collection

                [*]

                These authors contributed equally to this work

                Article
                900030
                10.12659/MSM.900030
                5404822
                28416776
                2a059833-00ff-4f5c-afbf-f40efd338a3d
                © Med Sci Monit, 2017

                This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International ( CC BY-NC-ND 4.0)

                History
                : 12 June 2016
                : 12 September 2016
                Categories
                Lab/In Vitro Research

                breast neoplasms,gene expression profiling,gene targeting,information systems,micrornas

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