[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127625-en":3,"doc-seo-127625-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127625,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers - research","Adrenocortical carcinoma is a rare, aggressive endocrine cancer with an incomplete understanding of pathogenesis and limited therapeutic options. This study integrates machine learning with multi-omics and system-biology tools to discover prognostic biomarkers. Gene expression and DNA methylation datasets were analyzed using DIABLO for multi-omics signature discovery and latent-component integration, while network regulators were inferred via Clarivate CBDD network propagation and hidden-node methods. A random-forest model and Kaplan-Meier validation assessed discriminative and survival associations, producing a high-risk stratification signature with clinical relevance.","TYPE Original Research PUBLISHED 06 November 2023 DOI 10.3389/fmolb.2023.1258902  \nOPEN ACCESS  \nEDITED BY  \nSilvia Bottini,  \nUniversité Côte d’Azur, France  \nREVIEWED BY  \nYize Li,  \nWashington University in St. Louis, United States  \nTamás Micsik,  \nSemmelweis University, Hungary  \n*CORRESPONDENCE  \nRoberto Martin-Hernandez,  \n [roberto.martin@clarivate.com](roberto.martin@clarivate.com)  \n†These authors have contributed equally to this work  \nRECEIVED 14 July 2023  \nACCEPTED 06 October 2023  \nPUBLISHED 06 November 2023  \nCITATION  \nMartin-Hernandez R, Espeso-Gil S, Domingo C, Latorre P, Hervas S, Hernandez Mora JR and Kotelnikova E (2023), Machine learning combining multi-omics data and network algorithmsidentifies adrenocortical carcinoma prognostic biomarkers.  \nFront. Mol. Biosci. 10:1258902 .  \ndoi: 10.3389/fmolb.2023.1258902  \nCOPYRIGHT  \n© 2023 Martin-Hernandez, Espeso-Gil, Domingo, Latorre, Hervas, Hernandez Mora and Kotelnikova. This is an openaccess 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.  \nMachine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers  \nRoberto Martin-Hernandez*, Sergio Espeso-Gil†, Clara Domingo†, Pablo Latorre, Sergi Hervas, Jose Ramon Hernandez Mora and Ekaterina Kotelnikova  \nDiscovery and Translational Sciences (DTS), Clarivate Analytics, Barcelona, Spain  \nBackground: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.  \nMethods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort) . A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™) . The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.  \nResults: A multi-omics signature including genes, micro RNA ’s and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (O","cbCaiimcBKIro0id","https://ap.wps.com/l/cbCaiimcBKIro0id","pdf",2443417,1,14,"English","en",105,"# Background\n# Methods\n## Data integration and signature discovery\n## Network propagation and regulator identification\n## Model training and prognostic evaluation\n# Results\n# Conclusion\n# Keywords\n# Introduction","[{\"question\":\"What problem does the study address in adrenocortical carcinoma?\",\"answer\":\"It targets the challenge of incomplete knowledge of ACC pathogenesis and the need for robust prognostic biomarkers to support timely diagnosis and patient stratification for more beneficial treatments.\"},{\"question\":\"How were the multi-omics data used to discover biomarkers?\",\"answer\":\"The study downloaded ACC gene expression and DNA methylation datasets and used the DIABLO method to integrate multi-omics signals and identify a highly correlated signature discriminating sample groups.\"},{\"question\":\"How was prognostic value validated?\",\"answer\":\"A random forest with 10-fold cross validation assessed discriminative power, and an external GEO dataset (GSE49280) was evaluated using Kaplan-Meier analysis. An optimal risk signature was then derived using stepwise AIC.\"}]","Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers - research | PDF",1785940352,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-combining-multi-omics-data-and-network-algorithms-identifies-adrenocortical-carcinoma-prognostic-biomarkers-research","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-combining-multi-omics-data-and-network-algorithms-identifies-adrenocortical-carcinoma-prognostic-biomarkers-research/127625/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in adrenocortical carcinoma?","Question",{"text":76,"@type":77},"It targets the challenge of incomplete knowledge of ACC pathogenesis and the need for robust prognostic biomarkers to support timely diagnosis and patient stratification for more beneficial treatments.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the multi-omics data used to discover biomarkers?",{"text":81,"@type":77},"The study downloaded ACC gene expression and DNA methylation datasets and used the DIABLO method to integrate multi-omics signals and identify a highly correlated signature discriminating sample groups.",{"name":83,"@type":74,"acceptedAnswer":84},"How was prognostic value validated?",{"text":85,"@type":77},"A random forest with 10-fold cross validation assessed discriminative power, and an external GEO dataset (GSE49280) was evaluated using Kaplan-Meier analysis. 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