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Using systems-biology workflows, this study identifies weighted co-expression network modules associated with breast cancer progression. Hypergeometric testing performs miRNA target enrichment, while negative correlation between miRNA expression and modules acts as an additional filter to select candidate miRNAs. The analysis highlights hsa-mir-23b, hsa-let-7b, and hsa-mir-30a and evaluates their roles in progression.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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were co-expression network modules identified for breast cancer progression?","Question",{"text":63,"@type":64},"The study uses weighted co-expression network analysis to group genes with similar expression patterns into modules linked to breast cancer progression.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What method was used to detect miRNA target enrichment, and how were candidates filtered?",{"text":68,"@type":64},"A hypergeometric test was applied for miRNA target enrichment, and negative correlation between miRNA expression and modules served as a second filter to improve candidate selection.",{"name":70,"@type":61,"acceptedAnswer":71},"Which miRNAs were found to be important, and what were they linked to?",{"text":72,"@type":64},"The study identifies hsa-mir-23b, hsa-let-7b, and hsa-mir-30a as important miRNAs and investigates their roles in breast cancer 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Aghaee-Bakhtiari*  \nMiRNA target enrichment analysis of  \nco-expression network modules reveals important miRNAs and their roles in breast cancer progression  \n[https://doi.org/10.1515/jib-2022-0036](https://doi.org/10.1515/jib-2022-0036)  \nReceived June 26, 2022; accepted March 21, 2023; published online December 25, 2024  \nAbstract: Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is oneof the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted coexpression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNAto find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules asthe second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30aare important miRNAs in breast cancer and also investigated their roles in breast cancer progression.  \nKeywords: systems biology; gene expression regulation; functional enrichment analysis; protein-protein interaction network; molecular complex detection  \nAbbreviations  \nBP biological process  \nCC cellular compartment  \nFDR false discovery rate GO gene ontology  \nGSVA gene set variation analysis  \nKEGG Kyoto encyclopedia of genes and genomes MCODE molecular complex detection  \nMF molecular function  \nPPI protein-protein interaction  \nTCGA the cancer genome atlas TOM topology overlap measure  \nWGCNA weighted co-expression network analysis  \n*Corresponding author: Seyed Hamid Aghaee-Bakhtiari, Bioinformatics Research Center,Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; and Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran, E-mail: [aghaeibh@mums.ac.ir](aghaeibh@mums.ac.ir). [https://orcid.org/0000-0002-6093-7837](https://orcid.org/0000-0002-6093-7837)[ ](https://orcid.org/0000-0002-6093-7837)Mohammad Javad Bazyari, Department of Medical Biotechnology and Nanotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran  \n Open Access. © 2024 the author(s), published by De Gruyter.  This work is licensed under the Creative Commons Attribution 4 .0 International License.  \n2 — M. J. Bazyari and S. H. Aghaee-Bakhtiari: miRNA target enrichment analysis reveals important miRNAs in breast cancer   \n1 Introduction  \nBreast cancer is the most common cancer in the world and the fifth cause of death due to cancer. Among the women population, it accounts for 25 % of cancer incidence and 16.6 % of cancer mortality [1]. There are several risk factors associated with the development and progression of breast cancer. Factors such as familial history of breast cancer, history of chest radiation therapy and hormone replacement therapy can increase the chance of cancer development. Besides, genetic risk factors also play an important role in breast cancer. Inherited mutationsin several genes such as CHEK2, CDH1, ATM, andTP53 are known tobe breast cancer risk factors. Mutations in BRCA1 and BRCA2 are the most common cause of hereditary breast cancer and the most notable genetic risk factors [2] . Because of its heterogeneous nature, breast cancer cells can change i","cbCaik6Q3RkNiIvE","https://ap.wps.com/l/cbCaik6Q3RkNiIvE","pdf",3012973,14,"English","# Abstract\n# Keywords\n# Abbreviations\n# Introduction\n## Breast cancer background and risk factors\n## MicroRNAs in gene regulation and cancer phenotypes\n## Need for systems-level network approaches","[{\"question\":\"How were co-expression network modules identified for breast cancer progression?\",\"answer\":\"The study uses weighted co-expression network analysis to group genes with similar expression patterns into modules linked to breast cancer progression.\"},{\"question\":\"What method was used to detect miRNA target enrichment, and how were candidates filtered?\",\"answer\":\"A hypergeometric test was applied for miRNA target enrichment, and negative correlation between miRNA expression and modules served as a second filter to improve candidate selection.\"},{\"question\":\"Which miRNAs were found to be important, and what were they linked to?\",\"answer\":\"The study identifies hsa-mir-23b, hsa-let-7b, and hsa-mir-30a as important miRNAs and investigates their roles in breast cancer progression.\"}]","MiRNA target enrichment analysis of co-expression network modules reveals important miRNAs and their roles in breast cancer progression | PDF",1789999984,35]