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Methods: Gene expression profiles were obtained from Gene Expression Omnibus, The Cancer Genome Atlas, and Genotype-Tissue Expression. Differential expression analysis, WGCNA, and machine learning (LASSO regression, random forest) were used to prioritize key genes. Results: hIL-24 was highlighted and experimentally validated to inhibit proliferation, migration, and invasion while promoting apoptosis in SiHa and HeLa cells. Conclusion: The multi-database strategy supports hIL-24 as a promising candidate for targeted cervical cancer therapy and mechanistic exploration.",{"@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/inhibitory-effect-of-human-interleukin-24-on-the-proliferation-migration-and-invasion-of-cervical-cancer-cells/332289/",{"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/inhibitory-effect-of-human-interleukin-24-on-the-proliferation-migration-and-invasion-of-cervical-cancer-cells/332289.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main objective of the study?","Question",{"text":112,"@type":113},"The study aimed to identify differentially expressed genes related to cervical cancer using large gene expression datasets, with the goal of uncovering new therapeutic targets.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were key genes selected in the research pipeline?",{"text":117,"@type":113},"Gene expression datasets were integrated, DEGs were identified, WGCNA was used to associate genes with clinical traits, and machine learning methods (LASSO regression and random forest) were applied to pinpoint key genes.",{"name":119,"@type":110,"acceptedAnswer":120},"What effects did hIL-24 show on cervical cancer cells?",{"text":121,"@type":113},"Experimental validation showed that hIL-24 inhibited proliferation, migration, and invasion and promoted apoptosis in SiHa and HeLa cervical cancer cells.","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},332289,1790189209,{"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":29,"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,"preview_page_count":4},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Inhibitory effect of human interleukin-24 on the proliferation, migration, and invasion of cervical cancer cells  \nMin Song 1,*, Hongtao Yuan2,*, Jie Zhang 1,*, Jing Wang 1, Jianhua Yu 1 and Wei Wang 1   \nJournal of International Medical Research 2024, Vol. 52(7) 1–18! The Author(s) 2024 Article reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/03000605241259655](DOI: 10.1177/03000605241259655)[ ](DOI: 10.1177/03000605241259655)[journals.sagepub.com/home/imr](journals.sagepub.com/home/imr)  \nAbstract  \nObjective: This study aimed to identify significantly differentially expressed genes (DEGs) related to cervical cancer by exploring extensive gene expression datasets to unveil new therapeutic targets.  \nMethods: Gene expression profiles were extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Genotype-Tissue Expression platforms. A differential expression analysis identified DEGs in cervical cancer cases. Weighted gene co-expression network analysis (WGCNA) was implemented to locate genes closely linked to the clinical traits of diseases. Machine learning algorithms, including LASSO regression and the random forest algorithm, were applied to pinpoint key genes.  \nResults: The investigation successfully isolated DEGs pertinent to cervical cancer. Interleukin-24 was recognized as a pivotal gene via WGCNA and machine learning techniques. Experimental validations demonstrated that human interleukin (hIL)-24 inhibited proliferation, migration, and invasion, while promoting apoptosis, in SiHa and HeLa cervical cancer cells, affirming its role as a therapeutic target.  \nConclusion: The multi-database analysis strategy employed herein emphasized hIL-24 as a principal gene in cervical cancer pathogenesis. The findings suggest hIL-24 as a promising candidate for targeted therapy, offering a potential avenue for innovative treatment modalities. This study enhances the understanding of molecular mechanisms of cervical cancer and aids in the pursuit of novel oncological therapies.  \n1 Qilu Medical University, Zibo, P. R. China 2Zhangdian People’s Hospital, Zibo, P. R. China  \n*These authors contributed equally to this work. Corresponding author:  \nWei Wang, Qilu Medical University, No. 1678, Renmin West Road, Zhoucun District, Zibo City, Shandong Province 255300, China.  \nEmail: [kaiwei529@163.com](kaiwei529@163.com)  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative  \nCommons Attribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ([https://us.sagepub.com/en-us/nam/open-access-at-sage](https://us.sagepub.com/en-us/nam/open-access-at-sage)).  \nKeywords  \nCervical cancer, differentially expressed gene, weighted gene co-expression network analysis, machine learning, human interleukin-24, gene expression data  \nDate received: 7 November 2023; accepted: 20 May 2024  \nIntroduction  \nCervical cancer continues to be a major health issue globally, with a disproportionately high burden in developing countries, where it leads to a substantial number of cases and deaths.1–3 Despite advancementsin the prevention and treatment of cervical cancer in recent years, such as the introduction of the national cervical screening program by the National Health Service in 1988, which has led to a reduction of over one-third of cases in England,4 the malignant biological behaviors of the disease, including rapid proliferation, invasion, and migration, remain major challenges for treatment.3,5,6 Currently, surgery, radiotherapy, and chemotherapy are the primary treatment modalities, but these approaches often come with significant s","cbCailCLYpHhSaDQ","https://ap.wps.com/l/cbCailCLYpHhSaDQ","pdf",1687233,18,"English","# Abstract\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"The study aimed to identify differentially expressed genes related to cervical cancer using large gene expression datasets, with the goal of uncovering new therapeutic targets.\"},{\"question\":\"How were key genes selected in the research pipeline?\",\"answer\":\"Gene expression datasets were integrated, DEGs were identified, WGCNA was used to associate genes with clinical traits, and machine learning methods (LASSO regression and random forest) were applied to pinpoint key genes.\"},{\"question\":\"What effects did hIL-24 show on cervical cancer cells?\",\"answer\":\"Experimental validation showed that hIL-24 inhibited proliferation, migration, and invasion and promoted apoptosis in SiHa and HeLa cervical cancer cells.\"}]","Inhibitory effect of human interleukin-24 on the proliferation, migration, and invasion of cervical cancer cells | PDF",1789997645,45]