[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126811-en":3,"doc-seo-126811-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},126811,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification","Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases with heterogeneous clinical presentation. A gel-based bottom-up proteomic workflow was applied to the acid-insoluble salivary fraction from AIH and PBC patients and healthy controls, combined with robust machine-learning statistics. Several proteins showed differential abundance, and hornerin decreased across both disease groups. Machine learning supported the discriminative capacity of the salivary proteome for AIH versus PBC occurrence, while topology-based enrichment emphasized immune system involvement, liver fibrosis pathways, and antimicrobial functions.","Article  \nCombined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification  \nGiulia Guadalupi 1, Cristina Contini 1, Federica Iavarone 2,3, Massimo Castagnola 4, Irene Messana 5, Gavino Faa 6, Simona Onali 7, Luchino Chessa 7, Rui Vitorino 8,9, Francisco Amado 10, Giacomo Diaz 11, Barbara Manconi 1, *, Tiziana Cabras 1,† and Alessandra Olianas 1,†  \nCitation: Guadalupi, G.; Contini, C.; Iavarone, F.; Castagnola, M.; Messana, I.; Faa, G.; Onali, S.; Chessa, L.; Vitorino, R.; Amado, F.; et al. Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification. Int. J. Mol. Sci. 2023, 24, 12207. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijms241512207  \nAcademic Editor: Chulso Moon  \nReceived: 15 June 2023  \nRevised: 26 July 2023  \nAccepted: 28 July 2023  \nPublished: 30 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Dipartimento di Scienze della Vita e dell'Ambiente, Universit di Cagliari, 09124 Cagliari, Italy; [giulia.guadalupi@unica.it](giulia.guadalupi@unica.it) (G.G.); [cristina.contini93@unica.it](cristina.contini93@unica.it) (C.C.); [tcabras@unica.it](tcabras@unica.it) (T.C.); [olianas@unica.it](olianas@unica.it) (A.O.)  \n2 Fondazione Policlinico Universitario IRCCS “A. Gemelli”, 00168 Rome, Italy; [federica.iavarone@unicatt.it](federica.iavarone@unicatt.it)  \n3 Dipartimento di Scienze Biotecnologiche di Base, Cliniche Intensivologiche e Perioperatorie, Universit Cattolica del Sacro Cuore, 00168 Rome, Italy  \n4 Laboratorio di Proteomica, Centro Europeo di Ricerca sul Cervello, IRCCS Fondazione Santa Lucia,  \n00168 Rome, Italy; [maxcastagnola@outlook.it](maxcastagnola@outlook.it)  \n5 Istituto di Scienze e Tecnologie Chimiche “Giulio Natta”, Consiglio Nazionale delle Ricerche,  \n00168 Rome, Italy; [imessana53@gmail.com](imessana53@gmail.com)  \n6 Division of Pathology, Department of Medical Sciences and Public Health, University Hospital, 09124 Cagliari, Italy; [gavinofaa@gmail.com](gavinofaa@gmail.com)  \n7 Liver Unit, University Hospital of Cagliari, 09124 Cagliari, Italy; simona.onali@yahoo.it (S.O.); [luchinochessa@unica.it](luchinochessa@unica.it) (L.C.)  \n8 iBiMED, Department of Medical Science, University of Aveiro, 3810-193 Aveiro, Portugal; [rvitorino@ua.pt](rvitorino@ua.pt)  \n[9](9 UnIC@RISE)[ UnIC@RISE](9 UnIC@RISE), [Department of Surgery and Physiology](Department of Surgery and Physiology), [Faculty of Medicine of the University of Porto](Faculty of Medicine of the University of Porto), 4200-319 Porto, Portugal  \n10 LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal; [famado@ua.pt](famado@ua.pt)  \n11 Dipartimento di Scienze Biomediche, Universit di Cagliari, 09124 Cagliari, Italy; [gdiaz@unica.it](gdiaz@unica.it)  \n* [Correspondence: bmanconi@unica.it](Correspondence: bmanconi@unica.it)[ ](Correspondence: bmanconi@unica.it)† These authors contributed equally to this work.  \nAbstract: Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases that target the liver and have a wide spectrum of presentation. A global overview of quantitative variations on the salivary proteome in presence of these two pathologies is investigated in this study. The acid-insoluble salivary fraction of AIH and PBC patients, and healthy controls (HCs), was analyzed using a gel-based bottom-up proteomic approach combined with a robust machine learning statistical analysis of the dataset. The abundance of Arginase, Junction plakoglobin, Desm","cbCaiqPCQ9HnlXal","https://ap.wps.com/l/cbCaiqPCQ9HnlXal","pdf",7707634,1,23,"English","en",105,"# Introduction\n## Study rationale and disease background\n# Methods\n## Salivary proteome profiling approach\n## Machine learning analysis\n# Results\n## Differentially abundant proteins\n## Classification performance\n# Functional enrichment and biological interpretation\n## Immune, fibrosis, and antimicrobial pathways","[{\"question\":\"What samples and experimental approach were used to study AIH and PBC?\",\"answer\":\"The study analyzed the acid-insoluble salivary fraction from AIH and PBC patients and healthy controls. It used a gel-based bottom-up proteomic workflow combined with robust machine-learning statistics.\"},{\"question\":\"Which markers helped discriminate disease groups?\",\"answer\":\"Differentially abundant proteins were identified between AIH/PBC and healthy controls. Hornerin decreased in both AIH and PBC compared with healthy controls and achieved an area under the ROC curve of 0.939.\"},{\"question\":\"What biological processes were highlighted by functional enrichment?\",\"answer\":\"Topology-based functional enrichment of potential salivary biomarkers emphasized immune system-related terms. It also indicated strong involvement in liver fibrosis processes and antimicrobial activity.\"}]","Combined Salivary Proteome Profiling and Machine Learning Analysis Provides Insight into Molecular Signature for Autoimmune Liver Diseases Classification | PDF",1785934927,58,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"combined-salivary-proteome-profiling-and-machine-learning-analysis-provides-insight-into-molecular-signature-for-autoimmune-liver-diseases-classication","",{"@graph":36,"@context":85},[37,54,68],{"@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/combined-salivary-proteome-profiling-and-machine-learning-analysis-provides-insight-into-molecular-signature-for-autoimmune-liver-diseases-classication/126811/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What samples and experimental approach were used to study AIH and PBC?","Question",{"text":75,"@type":76},"The study analyzed the acid-insoluble salivary fraction from AIH and PBC patients and healthy controls. It used a gel-based bottom-up proteomic workflow combined with robust machine-learning statistics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which markers helped discriminate disease groups?",{"text":80,"@type":76},"Differentially abundant proteins were identified between AIH/PBC and healthy controls. Hornerin decreased in both AIH and PBC compared with healthy controls and achieved an area under the ROC curve of 0.939.",{"name":82,"@type":73,"acceptedAnswer":83},"What biological processes were highlighted by functional enrichment?",{"text":84,"@type":76},"Topology-based functional enrichment of potential salivary biomarkers emphasized immune system-related terms. 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