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Ovarian cancer remains highly prevalent among women and shows extensive genomic and clinical heterogeneity. Building on prior network-bioinformatics work that identified nine dysregulated hub genes, the study performs integrated analyses across hub genes using GEPIA2, KM plotter, and cBioPortal to assess expression patterns, prognostic significance, mutation frequency, and patient survival effects. ELAVL2 is prioritized, followed by high-throughput screening and docking after small-molecule competitive inhibition, with molecular dynamics indicating relative stability for the resulting ELAV-like protein 2-ZINC03830554 complex and identifying five candidate compounds.",{"@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/unravelling-driver-genes-as-potential-therapeutic-targets-in-ovarian-cancer-via-integrated-bioinformatics-approach/342715/",{"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/unravelling-driver-genes-as-potential-therapeutic-targets-in-ovarian-cancer-via-integrated-bioinformatics-approach/342715.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the goal of this study on ovarian cancer?","Question",{"text":112,"@type":113},"The study aimed to identify driver genes and evaluate them as potential therapeutic targets using an integrated bioinformatics approach, extending prior hub-gene findings toward candidate treatments.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which bioinformatics tools were used to analyze the genes and their clinical relevance?",{"text":117,"@type":113},"GEPIA2 was used to assess critical gene expression patterns, KM plotter to determine statistical prognostic significance, and cBioPortal to examine mutation frequency and survival impacts.",{"name":119,"@type":110,"acceptedAnswer":120},"How were therapeutic candidates derived after prioritizing ELAVL2?",{"text":121,"@type":113},"The workflow involved competitive inhibition of ELAVL2 using a small-molecular drug complex, followed by high-throughput screening and docking studies to identify five compounds, with molecular dynamics supporting relative stability for the ELAV-like protein 2-ZINC03830554 complex.","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},342715,1790196202,{"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":14,"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":145},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Beg et al. Journal of Ovarian Research (2024) 17:86 Journal of Ovarian Research  \n[https://doi.org/10.1186/s13048-024-01402-7](https://doi.org/10.1186/s13048-024-01402-7)  \nRESEARCH Open Access  \nUnravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach  \nAnam Beg 1, Rafat Parveen 1*, Hassan Fouad2, M. E. Yahia3 and Azza S. Hassanein4  \nAbstract  \nTarget-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2 . In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.  \nKeywords Ovarian cancer, Hub genes, Prognostic relevance, RNA-binding proteins, Docking, MD simulation therapeutic targets  \n*Correspondence:  \nRafat Parveen  \n[rparveen@jmi.ac.in](rparveen@jmi.ac.in)  \n1Department of Computer Science, Jamia Millia Islamia,  \nNew Delhi 110025, India  \n2Applied Medical Science Department, CC, King Saud University, Riyadh 11433, Saudi Arabia  \n3Abu Dhabi Polytechnic, Institute of Applied Technology, Abu Dhabi 111499, United Arab Emirates  \n4Biomedical Engineering Department, Faculty of Engineering, Helwan University, Cairo, Egypt  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.  \nBeg et al. Journal of Ovarian Research","cbCailVZjXir7vf6","https://ap.wps.com/l/cbCailVZjXir7vf6","pdf",2508796,17,"English","# Introduction\n# Materials and Methods\n## GEPIA2 analysis\n## KM plotter analysis\n## cBioPortal mutation and survival analysis\n## Competitive inhibition, screening and docking\n## Molecular dynamics simulation\n# Results\n## Hub gene network findings\n## Statistically significant genes and prognostic signals\n## Mutation patterns and survival associations\n## Candidate compounds and docking outcomes\n## Stability of ELAV2-ZINC03830554 complex\n# Discussion\n# Conclusions","[{\"question\":\"What was the goal of this study on ovarian cancer?\",\"answer\":\"The study aimed to identify driver genes and evaluate them as potential therapeutic targets using an integrated bioinformatics approach, extending prior hub-gene findings toward candidate treatments.\"},{\"question\":\"Which bioinformatics tools were used to analyze the genes and their clinical relevance?\",\"answer\":\"GEPIA2 was used to assess critical gene expression patterns, KM plotter to determine statistical prognostic significance, and cBioPortal to examine mutation frequency and survival impacts.\"},{\"question\":\"How were therapeutic candidates derived after prioritizing ELAVL2?\",\"answer\":\"The workflow involved competitive inhibition of ELAVL2 using a small-molecular drug complex, followed by high-throughput screening and docking studies to identify five compounds, with molecular dynamics supporting relative stability for the ELAV-like protein 2-ZINC03830554 complex.\"}]","Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach | PDF",1790047838,43]