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Construction of Prognostic Microrna Signature for Human Invasive Breast Cancer by Integrated Analysis

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Construction of Prognostic Microrna Signature for Human Invasive Breast Cancer by Integrated Analysis Journal name: OncoTargets and Therapy Article Designation: Original Research Year: 2019 Volume: 12 OncoTargets and Therapy Dovepress Running head verso: Shi et al Running head recto: Shi et al open access to scientific and medical research DOI: 189265 Open Access Full Text Article ORIGINAL RESEARCH Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis This article was published in the following Dove Medical Press journal: OncoTargets and Therapy Wei Shi* Background: Despite the advances in early detection and treatment methods, breast cancer Fang Dong* still has a high mortality rate, even in those patients predicted to have a good prognosis. The Yujia Jiang purpose of this study is to identify a microRNA signature that could better predict prognosis in Linlin Lu breast cancer and add new insights to the current classification criteria. Changwen Wang Materials and methods: We downloaded microRNA sequencing data along with correspond- Jie Tan ing clinicopathological data from The Cancer Genome Atlas (TCGA). Of 1,098 breast cancer patients identified, 253 patients with fully characterized microRNA profiles were selected for Wen Yang analysis. A three-microRNA signature was generated in the training set. Subsequently, the per- Hui Guo formance of the signature was confirmed in a validation set. After construction of the signature, Jie Ming* we conducted additional experiments, including flow cytometry and the Cell Counting Kit-8 Tao Huang* assay, to illustrate the correlation of this microRNA signature with breast cancer cell cycle, Department of Breast and Thyroid apoptosis, and proliferation. Surgery, Union Hospital, Tongji Results: Three microRNAs (hsa-mir-31, hsa-mir-16-2, and hsa-mir-484) were identified to Medical College, Huazhong University of Science and Technology, Wuhan be significantly and independently correlated with patient prognosis, and performed with good 430022, China stability. Our results suggest that higher expression of hsa-mir-484 indicated worse prognosis, *These authors contributed equally while higher expression of hsa-mir-31 and hsa-mir-16-2 indicated better prognosis. Moreover, to this work additional experiments confirmed that this microRNA signature was related to breast cancer cell cycle and proliferation. Conclusion: Our results indicate a three-microRNA signature that can accurately predict the prognosis of breast cancer, especially in basal-like and hormone receptor-positive breast cancer subtypes. We recommend more aggressive therapy and more frequent follow-up for high-risk groups. Keywords: microRNA, breast cancer, TCGA, prognosis Introduction Breast cancer is one of the most common malignancies among women, and despite the discovery of early detection methods and effective treatment therapies, it is still 1 Correspondence: Tao Huang; Jie Ming the second leading cause of cancer-related death in females. Breast cancer is a group Department of Breast and Thyroid of molecularly distinct neoplasms classified into four main subgroups based on Surgery, Union Hospital, Tongji Medical 2 College, Huazhong University of Science their expression of estrogen receptor (ER), progesterone receptor (PR), and human and Technology, Wuhan 430022, China epidermal growth factor receptor 2 (Her2). These subgroups require different treat- Tel +86 138 0711 2766; ment therapies and experience different clinical outcomes. However, even within the +86 139 9551 9049 Fax +86 027 8535 1622; subgroups, there are different subsets of genetic and epigenetic abnormalities leading +86 027 8535 1622 to different patient prognoses;3 thus, more research is needed to understand the mecha- Email [email protected]; [email protected] nisms related to the prognosis within different breast cancer subgroups. submit your manuscript | www.dovepress.com OncoTargets and Therapy 2019:12 1979–2010 1979 Dovepress © 2019 Shi et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you http://dx.doi.org/10.2147/OTT.S189265 hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Shi et al Dovepress MicroRNAs are a class of endogenously expressed small, as reads per million (RPM) microRNA mapped data. Any single-stranded, non-coding RNAs. Over the past decade, microRNA expression level reads where microRNAs equaled the aberrant expression of microRNAs has been increasingly 0 RPM in .40% observations were excluded. After trans- reported in human cancers and has often been associated with formation into binary variables according to the median diagnosis,4 prognosis, and response to clinical therapies.5 expression level, univariate Cox models were generated for They are involved in the post-transcriptional regulation of preliminary screening of microRNAs that were significantly gene expression via base pairing with target mRNAs (usually correlated with overall survival (OS). A cut-off P-value in the 3′ untranslated region), causing degradation and trans- of ,0.05 was used to filter out significant parameters. lation repression of mRNAs.6 MicroRNAs are now widely Clinical characteristics that were previously reported to regarded as the most powerful regulators of gene expression be associated with prognosis, including age at diagnosis, in complex cellular processes including cancer cell prolif- N stage, T stage, metastasis, ER, PR, and Her2, were also eration, metastasis, migration, and apoptosis.7 Of particular similarly evaluated in the univariate Cox models. We then importance is the association with cancer cell proliferation generated general multivariate stepwise Cox regression and metastasis, as these are two hallmarks of malignancy models to determine which of the significant microRNA and the leading causes of cancer-related death.5 In addition, identified by univariate proportional hazards regression many studies have shed light on tumor-targeting therapies was an independent predictor of prognosis. OS time was using microRNAs as novel diagnostic and therapeutic tools.8,9 calculated from the date of the initial pathological diagnosis The Cancer Genome Atlas (TCGA) project provides to the date of death. researchers with a set of comprehensive tools that can be The permutation test was used to evaluate the perfor- used to analyze clinical and genetic signatures of a variety mance and randomness of the final multivariate model. of cancers including breast carcinoma. In this study, we Using the combination of patient OS time and vital status retrieved breast carcinoma data from TCGA to construct a as a label, each patient was assigned a label and risk score three-microRNA signature that can be used to predict the under the microRNA scoring system. A random system was prognosis of breast cancer, and we verified the signature constructed by assigning labels while the risk score was kept using both statistical and experimental methods. consistent within each individual. The random system was tested for significance in predicting survival. If the model Materials and methods performed well, the random system was not a predictor of TCGA breast invasive carcinoma data set prognosis, and the area under the curve (AUC) of the receiver The clinical information and expression levels from 1,158 operating characteristics (ROC) curve would approach 0.5. microRNAs of 1,098 patients with breast as the primary cancer We generated 1,000 random systems. A cut-off P-value site were downloaded from TCGA (https://cancergenome. of ,0.05 was used to indicate a significant association nih.gov/) on May 4, 2017. Patients were screened by the between AUCs of the random system and the label system. following criteria for inclusion: 1) the patients were female; We would conclude that the label system had no effect on 2) the patients had no preoperative treatment; 3) the patients’ outcome unless the calculated P-value was smaller than sample types were primary tumor; 4) the patients had fully 0.05. A validation set containing 100 patients was used to characterized microRNA profiles; and 5) the percentage of test the prognostic value of the microRNA signature. These necrosis in samples was ,40% on both the top and bottom analyses were performed using R software (version 3.3.2, slides. Patients who were alive but missing the date of last https://www.r-project.org/). contact were excluded. A total of 253 breast invasive car- cinoma patients were identified for further study according Bioinformatics analysis to the selection criteria. The total set was randomly sepa- Targetscan7.1 (http://www.targetscan.org/vert_71/), DIANA- rated into a training set (153 patients) and a validation set microT,10 miRWalk,11 miRanda (http://www.microrna. (100 patients). org/microrna/home.do), PicTar (http://www.pictar.org/), and miRDB12 were used to identify the target genes of three Construction and validation of the microRNAs. To increase accuracy, only target genes pre- integrated microRNA signature dicted by a minimum of three programs were retained for fur- The microRNA signature was constructed in the training set. ther analysis.
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