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MicroRNAs regulate diverse disease pathways, yet clinical diagnosis is hindered by high heterogeneity. Using in silico analyses, the study identifies differentially expressed genes, hub genes from interaction networks, and interacting miRNAs, then validates hub gene expression in HeLa and HeLaDP cells by real-time PCR. Results prioritize key miRNAs and hub genes for personalized oncology.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/transcriptomics-driven-identification-of-hub-gene-mirna-interactions-for-biomarker-and-therapeutic-target-discovery-in-gynecological-cancers/353530/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/transcriptomics-driven-identification-of-hub-gene-mirna-interactions-for-biomarker-and-therapeutic-target-discovery-in-gynecological-cancers/353530.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":8},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this study?","Question",{"text":112,"@type":113},"To identify hub gene–miRNA interactions using transcriptomics data and computational analyses, enabling discovery of candidate biomarkers and therapeutic targets for gynecological cancers, especially cervical cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were hub genes and interacting miRNAs determined?",{"text":117,"@type":113},"The workflow used in silico identification of differentially expressed genes, hub genes derived from protein–protein interaction networks and cytohubba, and then predicted miRNAs that interact with the hub genes.",{"name":119,"@type":110,"acceptedAnswer":120},"How was the computational prediction validated?",{"text":121,"@type":113},"Differential expression of hub genes was validated in HeLa and HeLaDP cells using real-time PCR (qRT-PCR), with statistical support reported in the results.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},353530,1790167225,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":8,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","TYPE Original Research PUBLISHED 05 January 2026 DOI 10.3389/fonc.2025.1719597  \nOPEN ACCESS  \nEDITED BY  \nShuyuan Wang,  \nHarbin Medical University, China  \nREVIEWED BY  \nHengrui Liu,  \nUniversity of Cambridge, United Kingdom Arafat Rahman Oany,  \nTexas A and M University, United States  \n*CORRESPONDENCE  \nHanling Huang  \n [zhangandy@taihehospital.com](zhangandy@taihehospital.com)[ ](zhangandy@taihehospital.com)Ke Huang  \n[476309250@qq.com](476309250@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 29 October 2025  \nREVISED 30 November 2025  \nACCEPTED 10 December 2025  \nPUBLISHED 05 January 2026  \nCITATION  \nZhu Y, Chen S, Cao M, Liu W, Huang H, Huang K and Shi L (2026) Transcriptomics driven identification of hub gene miRNA interactions for biomarker and therapeutic target discovery in gynecological cancers. Front. Oncol. 15:1719597 .  \ndoi: 10.3389/fonc.2025.1719597  \nCOPYRIGHT  \n© 2026 Zhu, Chen, Cao, Liu, Huang, Huang and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTranscriptomics driven identification of hub gene miRNA interactions for biomarker and therapeutic target discovery in gynecological cancers  \nYuanjun Zhu 1†, Sisi Chen 1†, Mei Cao 1, Wangbo Liu 2, Hanling Huang 3*, Ke Huang 1* and Lingling Shi 4  \n1 Department of Obstetrics and Gynecology, Taihe Hospital, Hubei University of Medicine, Shiyan, China, 2 Department of Emergency, Taihe Hospital, Hubei University of Medicine,  \nShiyan, China, 3 Department of Physical Examination Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China, 4 Department of Ultrasound Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, China  \nIntroduction: MicroRNAs (miRNAs) are small, single-stranded noncoding RNAs that play critical roles in disease development, including gynecological cancers like vulvar and cervical cancer. Their high heterogeneity makes achieving an accurate diagnosis difficult in modern clinical practice.  \nMethods: In this study, we used in silico analyses to identify hub genes, miRNAs, and their interactions, enabling the discovery of potential biomarkers that may improve the diagnosis and treatment of cervical cancers following validation by quantitative gene expression analysis.  \nResults: The statistical analysis of GEOR2 yielded 16,344 differentially expressed genes (DEGs), and through robust regression analysis, 229 common DEGs were retrieved. Among them, 94 and 135 genes were downregulated and upregulated, respectively. We retrieved ten hub genes via a protein–protein interaction network and cytohubba, namely CDK1, AURKA, BUB1B, CCNB1, TOP2A, KIF11, BUB1, CCNB2, CDCA8, and BIRC5 . Following extensive in silico analysis, 30 miRNAs that interact with hub genes were identified and among these miRNAs, hsa-miR-653-5p, hsa-miR-495-3p, hsa-miR-381-3p, hsa-miR-1266-5p, and hsa-miR-589-3p were the top five interactive miRNAs that targeted the most hub genes and were involved in key functions leading to colorectal cancer, cervical cancer, glioma, and TGF-beta signaling. We further validated the differential expression of hub genes in HeLa and HeLaDP cells using real-time PCR (P \u003C 0 . 01) .  \nDiscussion: The identified miRNAs exhibit strong regulatory interactions with these hub genes, while serine/threonine protein kinases emerged asthe most significantly associated group. Together, these findings highlight promising biomarker candidates and potential therapeutic targets for gynecological cancers.  \nKEYWORDS  \nCervical cancer, RNA sequence data, regression analysis, hub genes, miRNA, noncoding RNAs, potential biomarker, q","cbCaihyhPC1m9Y6F","https://ap.wps.com/l/cbCaihyhPC1m9Y6F","pdf",4075458,16,"English","# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To identify hub gene–miRNA interactions using transcriptomics data and computational analyses, enabling discovery of candidate biomarkers and therapeutic targets for gynecological cancers, especially cervical cancer.\"},{\"question\":\"How were hub genes and interacting miRNAs determined?\",\"answer\":\"The workflow used in silico identification of differentially expressed genes, hub genes derived from protein–protein interaction networks and cytohubba, and then predicted miRNAs that interact with the hub genes.\"},{\"question\":\"How was the computational prediction validated?\",\"answer\":\"Differential expression of hub genes was validated in HeLa and HeLaDP cells using real-time PCR (qRT-PCR), with statistical support reported in the results.\"}]","Transcriptomics driven identification of hub gene miRNA interactions for biomarker and therapeutic target discovery in gynecological cancers | PDF",1790105940]