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Using proximity extension assay-based proteomics across 1463 plasma proteins from 456 pre-diagnostic patients, the study identifies 29 proteins linked to new cancer diagnoses and trains a model to stratify 160 cancer cases and 296 non-cancer cases (AUC 0.80), with preserved performance in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory, and infectious diseases, supporting triage decisions for faster, sensitive diagnostic work-ups.",{"@graph":69,"@context":123},[70,84,106],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/plasma-protein-profiling-predicts-cancer-in-inpatients-with-non-specific-symptoms-research/455521/",{"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/plasma-protein-profiling-predicts-cancer-in-inpatients-with-non-specific-symptoms-research/455521.png","ImageObject",300,407,{"name":92,"@type":93},"Eliana","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"为什么在出现非特异性症状的患者中更难进行癌症检测？","Question",{"text":113,"@type":114},"因为这些症状往往与非恶性疾病相互重叠，导致临床表现不易区分。文中指出早期诊断在该人群中特别具有挑战性。","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"研究如何从血浆蛋白数据中建立癌症风险分层模型？",{"text":118,"@type":114},"研究基于近邻延伸试验（proximity extension assay）对456名在确诊癌症前的患者血浆进行蛋白组检测，识别与新发癌症诊断相关的蛋白，并据此构建可分层的预测模型。",{"name":120,"@type":111,"acceptedAnswer":121},"模型能否区分癌症与其他疾病？",{"text":122,"@type":114},"可以。模型不仅用于区分癌症与非癌病例，还能区分癌症与自身免疫性、炎症性及感染性疾病。","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},455521,1790925582,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"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":41,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","Article [https://doi.org/10.1038/s41467-025-67688-3](https://doi.org/10.1038/s41467-025-67688-3)  \nPlasma protein profiling predicts cancer inpatients with non-specific symptoms  \nReceived: 8 March 2025  \nAccepted: 5 December 2025  \n\n| Published online: 29 December 2025 |\n| --- |\n| Check for updates |\n\nFredrika Wannberg 1,11, María Bueno Álvez 2,11, Alvida Qvick 3,11,  \nTamas Pongracz 1, Katherina Aguilera1, Emma Adolfsson 4, Louise Essehorn5, Max Gordon 6, Mathias Uhlén 2, Gisela Helenius3,7, Viktoria Hjalmar1,8, Mikael Åberg 9,11, Axel Rosell 1,10,11 & Charlotte Thålin 1,11   \nCancer detection is challenging, especially in patients with diffuse symptoms that overlap with non-malignant conditions. Here we show that plasma protein profiling can identify cancer among patients with non-specific symptoms. Using proximity extension assay-based proteomics of 1463 plasma proteins from 456 patients presenting with non-specific symptoms sampled prior to cancer diagnostic work-up and diagnosis, we identify 29 proteins associated with new cancer diagnoses. We develop a model able to stratify 160 cancer cases and 296 non-cancer cases with an area under the curve of 0.80, maintaining performance (0.82) in an independent replication cohort of 238 patients. The model also distinguishes cancer from autoimmune, inflammatory and infectious diseases. Designed as a triage tool, our model based on a blood test could help prioritize patients at higher cancer risk for rapid and highly sensitive diagnostic modalities such as positron emission tomography–computed tomography. These findings emphasize the potential of blood proteome profiling to support timely diagnosis and transform clinical medicine.  \nEarly detection of cancer is key to reduce cancer-related mortality and morbidity1. However, early diagnosis is challenging in patients presenting with non-specific symptoms2 as these patients are not entitled to oneof the organ-specific cancer diagnostic pathways. Cancer screening programs furthermore primarily target a limited number of cancer types, such as breast, cervical and colorectal cancer3,4. To avoid diagnostic delay in this patient group, several countries have therefore initiated fast-track diagnostic pathways for patients with non-specific symptoms suggestive of an underlying cancer2,5–7. Referral of all patients  \nwith non-specific symptoms to fast-track diagnostic pathways would, however, strain the healthcare system and expose patients to potentially harmful and unnecessary investigations. Minimally invasive and easily accessible blood-based biomarkers discriminating between patients at high risk of cancer and those without cancer could aid in selecting patients that would benefit the most from an accelerated and targeted cancer diagnostic work-up and possibly enable earlier diagnosis.  \nPan-cancer blood biomarkers have been the focus of extensive research8–20. While a handful of pan-cancer blood biomarkers have  \n1Department of Clinical Sciences, Danderyd Hospital, Division of Internal Medicine, Karolinska Institutet, Stockholm, Sweden. 2Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden. 3Clinical Research Center, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. 4Department of Obstetrics and Gynecology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden. 5Division of Internal Medicine, Danderyd Hospital, Stockholm, Sweden. 6Department of Clinical Sciences, Danderyd Hospital, Division of Orthopedics, Karolinska Institutet, Stockholm, Sweden. 7ATMP-center, Skåne University Hospital, Lund, Sweden. 8Division of Specialist Medical Care, Diagnostic center, Danderyd Hospital, Stockholm, Sweden. 9Department of Medical Sciences, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden. 10Center for Hematology and Regenerative Medicine (HERM), NEO, Department of Medicine, Huddinge, Karolinska Institutet, Stockholm, Sweden. 11These","cbCaisOwoAxqFSA5","https://ap.wps.com/l/cbCaisOwoAxqFSA5","pdf",3743552,"English","# Background\n## Clinical challenge of non-specific symptoms\n## Need for blood-based biomarkers and triage\n# Methods\n## Cohort design and pre-diagnostic sampling\n## Proximity extension assay proteomics\n# Results\n## Proteins associated with new cancer diagnoses\n## Predictive model performance and replication\n## Differentiation from autoimmune, inflammatory, infectious diseases\n# Clinical implications","[{\"question\":\"为什么在出现非特异性症状的患者中更难进行癌症检测？\",\"answer\":\"因为这些症状往往与非恶性疾病相互重叠，导致临床表现不易区分。文中指出早期诊断在该人群中特别具有挑战性。\"},{\"question\":\"研究如何从血浆蛋白数据中建立癌症风险分层模型？\",\"answer\":\"研究基于近邻延伸试验（proximity extension assay）对456名在确诊癌症前的患者血浆进行蛋白组检测，识别与新发癌症诊断相关的蛋白，并据此构建可分层的预测模型。\"},{\"question\":\"模型能否区分癌症与其他疾病？\",\"answer\":\"可以。模型不仅用于区分癌症与非癌病例，还能区分癌症与自身免疫性、炎症性及感染性疾病。\"}]","Plasma protein profiling predicts cancer inpatients with non-specific symptoms - Research | PDF",1790743313]