[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-331620-105":59,"doc-detail-331620-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","comprehensive-bioinformatics-and-machine-learning-analyses-for-breast-cancer-staging-using-tcga-dataset-case-study","Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset - Case Study","","Breast cancer remains a major global health threat characterized by diverse molecular subtypes. This study applies an integrated computational framework that fuses machine learning with bioinformatics to perform breast cancer staging, classification, and diagnostic discovery using The Cancer Genome Atlas (TCGA). Differentially expressed genes are linked to signaling pathways, protein–protein interactions, and regulatory networks to reveal putative therapeutic targets and biomarkers, including specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs such as hsa-let-7d-5p.",{"@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/comprehensive-bioinformatics-and-machine-learning-analyses-for-breast-cancer-staging-using-tcga-dataset-case-study/331620/",{"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/comprehensive-bioinformatics-and-machine-learning-analyses-for-breast-cancer-staging-using-tcga-dataset-case-study/331620.png","ImageObject",300,407,{"name":92,"@type":93},"Clementine","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data source and overall approach does the study use for breast cancer staging?","Question",{"text":112,"@type":113},"The study leverages TCGA gene expression data and combines machine learning with bioinformatics analyses to categorize breast cancer by stage and subtype.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are genes and molecular mechanisms analyzed in the workflow?",{"text":117,"@type":113},"Differentially expressed genes are analyzed through signaling pathways, protein–protein interactions, and regulatory networks to uncover potential therapeutic targets and biomarkers.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning methods are reported and what diagnostic accuracies are achieved?",{"text":121,"@type":113},"Random forest reaches 97.19% diagnostic accuracy for staging, while XGBoost attains 95.23%.","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},331620,1790923552,{"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":34,"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},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","Briefings in Bioinformatics, 2025, 26(1), bbae628  \n[https://doi.org/10.1093/bib/bbae628](https://doi.org/10.1093/bib/bbae628)  \nCase Study  \nComprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset  \nSaurav Chandra Das1 , 2 , Wahia Tasnim3 , Humayan Kabir Rana3 , Uzzal Kumar Acharjee 1 , *, Md. Manowarul Islam 1 , Rabea Khatun3  \n1 Department of Computer Science and Engineering, Jagannath University, Dhaka-1100, Bangladesh  \n2 Department of Internet of Things and Robotics Engineering, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh, Kaliakair, Gazipur-1750, Bangladesh  \n3 Department of Computer Science and Engineering, Green University of Bangladesh, Narayanganj-1461, Dhaka, Bangladesh  \n*[Corresponding author. E-mail:](Corresponding author. E-mail: uzzal@cse.jnu.ac.bd)[ uzzal@cse.jnu.ac.bd](Corresponding author. E-mail: uzzal@cse.jnu.ac.bd)  \nAbstract  \nBreast cancer is an alarming global health concern, including a vast and varied set of illnesses with different molecular characteristics. The fusion of sophisticated computational methodologies with extensive biological datasets has emerged as an effective strategy for unravelling complex patterns in cancer oncology. This research delves into breast cancer staging, classification, and diagnosis by leveraging the comprehensive dataset provided by the The Cancer Genome Atlas (TCGA) . By integrating advanced machine learning algorithms with bioinformatics analysis, it introduces a cutting-edge methodology for identifying complex molecular signatures associated with different subtypes and stages of breast cancer. This study utilizes TCGA gene expression data to detect and categorize breast cancer through the application of machine learning and systems biology techniques. Researchers identified differentially expressed genes in breast cancer and analyzed them using signaling pathways, protein–protein interactions, and regulatory networks to uncover potential therapeutic targets. The study also highlights the roles of specific proteins (MYH2, MYL1, MYL2, MYH7) and microRNAs (such as hsa-let-7d-5p) that are the potential biomarkers in cancer progression founded on several analyses. In terms of diagnostic accuracy for cancer staging, the random forest method achieved 97.19%, while the XGBoost algorithm attained 95.23% . Bioinformatics and machine learning meet in this study to find potential biomarkers that influence the progression of breast cancer. The combination of sophisticated analytical methods and extensive genomic datasets presents a promising path for expanding our understanding and enhancing clinical outcomes in identifying and categorizing this intricate illness.  \nKeywords: breast cancer; TCGA; cancer staging; ontology; machine learning; transcription factors  \nIntroduction  \nBreast cancer is the second most common cancer among women, following skin cancer, and is the second leading cause of cancerrelated mortality, after lung cancer [1] . Globally, breast cancer has surpassed lung cancer as the most frequently diagnosed cancer in women. In 2020, an estimated 2 261 419 women worldwide were diagnosed with breast cancer [2] . According to the American Society for Clinical Oncology, it is projected that in 2023, 297 790 women in the USA will be diagnosed with invasive breast cancer, while 55 720 will be diagnosed with noninvasive (in situ) breast cancer. Since the mid-2000s, the incidence of invasive breast cancer in women has increased by ∼0 . 5% annually, likely driven by factors such as declining fertility rates, delayed age of first childbirth, and rising obesity rates. Additionally, invasive breast cancer is expected to affect ∼2800 men in the USA in 2023 [3] . Early detection of breast cancer is critical for selecting appropriate treatments and reducing the risk of metastasis [4] . Breast cancer is a heterogeneous and evolving disease, marked by various somatic mutations and changes in gene and protein e","cbCaiqXOH1TecRLF","https://ap.wps.com/l/cbCaiqXOH1TecRLF","pdf",2537367,17,"English","# Abstract\n# Introduction\n## Breast cancer epidemiology and subtypes\n## Tumor markers and cancer staging\n## Staging relevance to treatment and survival","[{\"question\":\"What data source and overall approach does the study use for breast cancer staging?\",\"answer\":\"The study leverages TCGA gene expression data and combines machine learning with bioinformatics analyses to categorize breast cancer by stage and subtype.\"},{\"question\":\"How are genes and molecular mechanisms analyzed in the workflow?\",\"answer\":\"Differentially expressed genes are analyzed through signaling pathways, protein–protein interactions, and regulatory networks to uncover potential therapeutic targets and biomarkers.\"},{\"question\":\"Which machine learning methods are reported and what diagnostic accuracies are achieved?\",\"answer\":\"Random forest reaches 97.19% diagnostic accuracy for staging, while XGBoost attains 95.23%.\"}]","Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset - Case Study | PDF",1789994352,43]