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This study uses in-silico tools to examine complement protein expression changes across breast cancer conditions, focusing on C3, C7, CFB, and CFD. Data extracted from multiple bioinformatics resources were filtered by statistical criteria. Twenty-one complement genes showed associations with survival conditions, including stage-related expression differences and co-expression networks. Changes in complement proteins correlate with prognosis and status, supporting CFD and CFB as reliable prognostic values.",{"@graph":69,"@context":126},[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/complement-protein-expression-changes-in-various-conditions-of-breast-cancer-in-silico-analyses-experimental-research/328771/",{"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/complement-protein-expression-changes-in-various-conditions-of-breast-cancer-in-silico-analyses-experimental-research/328771.png","ImageObject",300,407,{"name":92,"@type":93},"Theodore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-05","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"Which complement proteins and factors were重点 investigated in the study?","Question",{"text":112,"@type":113},"The study focused on complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), assessing their expression changes across breast cancer conditions.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What bioinformatics tools were used to extract and evaluate data?",{"text":117,"@type":113},"Intent data were extracted using Kaplan–Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr, followed by selection using a P-value threshold greater than 0.05.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings regarding survival and gene expression?",{"text":121,"@type":113},"The study clarified that 21 complement genes correlated with survival conditions, identified down- or upregulation patterns, and detected expression differences across four breast cancer stages, with co-expression genes also extracted and networked.",{"name":123,"@type":110,"acceptedAnswer":124},"How do the authors justify CFD and CFB as prognostic values?",{"text":125,"@type":113},"Given the vital role of CFD and CFB in the alternative pathway across different breast cancer stages, the observed complement expression changes support CFD and CFB as reliable prognostic indicators for diagnosis.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},328771,1790788638,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":147,"read_time":148,"preview_page_count":4},7971461740886,"https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","’Experimental Research   \nComplement protein expression changes in various conditions of breast cancer: in-silico analyses—experimental research  \nMohammad Reza Zabihia , Bahar Farhadi, MDb , Mohammad Akhoondianc,*  \nIntroduction: Breast cancer is the most prevalent cancer diagnosed in females worldwide. The known biomarkers are insufficient to understand the actual prognosis of breast cancer, and identifying new biomarkers is desirable and valuable data to improve the patient’s survival. Many inflammatory biomarkers, such as the complement system, can be regarded as prognostic values and as potent inflammatory mediators; complement proteins have a critical role in tumorigenesis. In the current study, the authors aim to investigate complement protein expression changes, particularly complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), in various conditions of breast cancer using in-silico tools.  \nMethods: The intent data were extracted using webtools, including; Kaplan–Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr. To select valid data, a P greater than 0 .05 was considered.  \nResults: The current study clarified that 21 complement genes correlated to survival conditions. Also, down or upregulation of extracted genes and breast cancer statuses were identified. Additionally, expression level difference of complement genes in various breast cancer four stages was detected. Ultimately, co-expression genes with complement genes were extracted and networked. Conclusion: Changes in the expression of complement proteins can strongly correlate to breast cancer’s prognosis, status, and survival. Furthermore, considering the vital role of CFD and CFB complement proteins in the alternative pathway in different stages of breast cancer, CFD and CFB can be regarded as reliable prognostic values for diagnosis.  \nKeywords: bioinformatic, breast cancer, complement, prognosis  \nIntroduction  \nBreast cancer is the most prevalent cancer diagnosed in females worldwide and accounts for 15% of cancer-related deaths in women[1,2] . Various biomarkers characterize breast cancer prognosis; however, the known biomarkers are insufficient to understand the actual prognosis; hence, identifying new biomarkers is desirable and valuable data to improve the patient’s survival[3] .  \nBased on the evidence, many inflammatory biomarkers, such as the complement system, can be regarded as prognostic values[4] . In addition, as potent inflammatory mediators, complement proteins have a critical role in tumorigenesis[5] . Also, the increase of complement regulatory proteins and activation  \naDepartment of Immunology, School of Medicine, Tehran University of Medical Sciences, bSchool of Medicine, Islamic Azad University, Mashhad Branch, Mashhad and cDepartment of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran  \nSponsorships or competing interests that maybe relevant to content are disclosed atthe end of this article.  \n*Corresponding authors. Address: Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. Tel.: +98 933 167 4201; fax: +98 021 [425 563. E-mail: m.akhoondian1994@gmail.com](425 563. E-mail: m.akhoondian1994@gmail.com) (M. Akhoondian).  \nCopyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.  \nAnnals of Medicine & Surgery (2024) 86:5152–5161  \nReceived 30 April 2024; Accepted 15 May 2024  \nPublished online 17 July 2024  \n[http://dx.doi.org/10.1097/MS9.0000000000002216](http://dx.doi.org/10.1097/MS9.0000000000002216)  \nHIGHLIGHTS  \n• The current study clarified that 21 complement genes corr","cbCaicXdIhKKH4SG","https://ap.wps.com/l/cbCaicXdIhKKH4SG","pdf",815226,"English","# Introduction\n# Methods\n# Results\n# Conclusion\n# Highlights\n# Keywords","[{\"question\":\"Which complement proteins and factors were重点 investigated in the study?\",\"answer\":\"The study focused on complement 3 (C3), complement 7 (C7), complement factor B (CFB), and complement factor D (CFD), assessing their expression changes across breast cancer conditions.\"},{\"question\":\"What bioinformatics tools were used to extract and evaluate data?\",\"answer\":\"Intent data were extracted using Kaplan–Meier plotter, BcGenExMiner, UALCAN, cbioportal, GeneMania, and Enrichr, followed by selection using a P-value threshold greater than 0.05.\"},{\"question\":\"What were the main findings regarding survival and gene expression?\",\"answer\":\"The study clarified that 21 complement genes correlated with survival conditions, identified down- or upregulation patterns, and detected expression differences across four breast cancer stages, with co-expression genes also extracted and networked.\"},{\"question\":\"How do the authors justify CFD and CFB as prognostic values?\",\"answer\":\"Given the vital role of CFD and CFB in the alternative pathway across different breast cancer stages, the observed complement expression changes support CFD and CFB as reliable prognostic indicators for diagnosis.\"}]","Complement protein expression changes in various conditions of breast cancer - in-silico analyses - experimental research | PDF",1789980930,25]