Clinical proteomics最新文献

{"title":"Integrative Proteomic and Phosphoproteomic Analysis Reveals Altered Vesicle Transport in Systemic Lupus Erythematosus.","authors":"Lingling Zhou, Yixi Li, Mengyao Wu, Donge Tang, Wei Zhang, Yong Dai","doi":"10.1186/s12014-026-09599-z","DOIUrl":"10.1186/s12014-026-09599-z","url":null,"abstract":"","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13361604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma proteomics of extracellular vesicles identifies key proteins correlating to postoperative atrial fibrillation after coronary artery bypass surgery.","authors":"Huan-Xin Chen, Qin Yang, Guo-Wei He","doi":"10.1186/s12014-026-09606-3","DOIUrl":"10.1186/s12014-026-09606-3","url":null,"abstract":"<p><p>BACKGROUND: New-onset postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG) is a major complication and the precise mechanism remains unclear. EVs have become a special intercellular information transmission system secreted from different cells. The present study aimed to identify differentially expressed proteins (DEPs) of the plasma exosomes (Extracellular Vesicles, EVs) from patients with POAF after CABG and explored the pathogenesis of POAF. METHODS: Label-free quantitative LC–MS/MS proteomics analyses were performed to find changes in protein levels proteomic alterations in plasma EVs of patients with POAF (n = 6) after CABG and sinus rhythm (SR, n = 6). The DEPs screened by bioinformatics analysis were validated by parallel reaction monitoring (PRM) quantitative analysis from a new set of plasma samples (POAF n = 9; SR n = 9). RESULTS: Exosomal proteomics identified 51 significant DEPs including 29 up-regulated and 22 down-regulated proteins in POAF group compared to the SR control. Bioinformatics analysis showed that the DEPs were significantly enriched in cysteine and methionine metabolism, Toll-like receptor signaling pathway, AGE-RAGE signaling pathway in diabetic complications and Diabetic cardiomyopathy. The PRM reconfirmed that serum amyloid P-component and Collagen alpha-2(I) chain were significantly down-regulated in POAF group. CONCLUSIONS: This study provides a new insight into the exploration of the pathogenesis of POAF. Abnormally expressed SAP and Collagen alpha-2(I) chain correlate to POAF and might be further explored for their role in the occurrence of POAF. These two proteins in the plasma EVs may be developed as biomarkers for future diagnosis and treatment of POAF in the clinical setting. </p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13274030/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147764578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated serum proteomics and metabolomics reveal an INR-associated multi-omics signature in hepatocellular carcinoma.","authors":"Yuying Cai, Lingna Lyu, Jiayi Ma, Xiaoru Wang, Wenxia Ma, Jiming Yin, Shanshan Wang","doi":"10.1186/s12014-026-09603-6","DOIUrl":"10.1186/s12014-026-09603-6","url":null,"abstract":"<p><p>BACKGROUND: Coagulation dysfunction is a common complication of hepatocellular carcinoma (HCC) and is reflected clinically by the international normalized ratio (INR), yet its serum molecular correlates remain poorly defined. We aimed to identify an INR-associated serum multi-omics signature in HCC and characterize the underlying molecular networks. METHODS: We performed serum proteomics and untargeted metabolomics on 52 HCC patients and 30 healthy controls. HCC patients were stratified by INR levels. Differential molecules were integrated using pathway analysis. LASSO and logistic regression were used to derive an INR-associated multi-omics signature. RESULTS: Proteomics and metabolomics identified 1,810 DEPs and 246 DEMs between HCC and controls. Integrated analysis mapped these changes to 101 shared pathways, highlighting cholesterol and central carbon metabolism. LASSO selected an eight-molecule signature comprising four proteins (CAMK2B, TGFB1, IDH1, LDHA) and four metabolites (DL-tryptophan, ornithine, D-glutamine, lysine) that discriminated INR-defined coagulation abnormality (AUC 0.859). WGCNA indicated remodeling of the coagulation-complement axis. In the TCGA-LIHC cohort, core genes were dysregulated, stratified overall survival, and showed partial concordance with INR status in an INR-annotated subset (AUC 0.668). CONCLUSIONS: Integrated serum proteomics and metabolomics revealed an INR-associated multi-omics signature and linked metabolic and immune–coagulation networks in HCC. These findings provide mechanistic insight and a candidate serum panel for future external validation. </p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13273987/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147721819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.","authors":"Qingyu Zhou, Yahui Shen","doi":"10.1186/s12014-026-09583-7","DOIUrl":"10.1186/s12014-026-09583-7","url":null,"abstract":"<p><strong>Background: </strong>Peptidomics is an emerging tool for biomarker discovery. By capturing end products and active peptides generated during protein breakdown, it helps reveal short peptides linked to disease. This narrative review centers on chronic obstructive pulmonary disease (COPD) and synthesizes recent advances in respiratory peptidomics across patient-accessible matrices and laboratory workflows toward treatable-trait translation.</p><p><strong>Main body: </strong>We conducted a structured literature search of PubMed, Embase, and Web of Science (January 2000-September 2025, with emphasis on the most recent five years), prioritizing mass-spectrometry-based discovery and targeted verification in human biospecimens. We distinguish clinical endogenous peptidomics, immunopeptidomics, and degradomics as complementary approaches in respiratory disease. Six representative peptide classes were compared across pre-analytical handling, enrichment strategies, and MS identification, building an evidence map for COPD, asthma, lung cancer, and pulmonary fibrosis. Using this map, we discuss matrix-technology fit and recurrent biological signals-airway inflammation, extracellular matrix turnover, and host-pathogen interaction-that show promise for disease subtyping and early diagnosis. For translation, we outline a stepwise pathway: (i) harmonized sampling and internal-standard-driven quality control; (ii) transparent modeling with calibration and decision-curve analysis; and (iii) multicenter external validation. We further consider integration with proteomics and breathomics, emerging peptide-drug leads, and open sharing of data and code to improve reproducibility and transferability.</p><p><strong>Conclusions: </strong>Peptidomics is poised to contribute clinically actionable biomarker panels in respiratory disease, with near-term opportunities in COPD phenotyping and exacerbation risk assessment using sputum and blood. Broad adoption will depend on standardized pre-analytics, feasible targeted assays in routine laboratories, robust external validation, and transparent model calibration.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13343862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational proteomics to enhance personalized treatment of COVID-19 and Long COVID.","authors":"Debasis Sahu, Logan R Van Nynatten, David Tweddell, Mark Daley, Douglas D Fraser","doi":"10.1186/s12014-026-09601-8","DOIUrl":"10.1186/s12014-026-09601-8","url":null,"abstract":"<p><p>The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to significant global health burden, including both acute infections and persistent post-acute sequelae, also known as Long COVID (LC) in survivors. While clinical management has reduced case-fatality rates, a substantial proportion of patients develop LC, a heterogeneous syndrome with long-term symptoms. This complex continuum requires therapeutic strategies for both the acute and chronic phases. Plasma proteomics has emerged as a powerful tool in precision medicine, offering insights into systemic molecular changes and disease trajectories. Using targeted and untargeted proteomic analyses, researchers can identify disease-relevant pathways, perform cellular deconvolution to assess tissue-specific contributions, and pinpoint therapeutic targets for both acute infection and persistent symptoms. Combined with bioinformatics and machine learning, these proteomic insights support biomarker discovery and drug repurposing strategies.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma exosome proteomics in different glucose statuses: a cross-sectional study on type 2 diabetes pathogenesis.","authors":"Yuxiang Liu, Haijian Guo, Mingma Li, Jinshui Xu, Yu Liu, Kaicheng Sun, Evan Yi-Wen Yu, Zilin Sun, Bei Wang","doi":"10.1186/s12014-026-09600-9","DOIUrl":"10.1186/s12014-026-09600-9","url":null,"abstract":"<p><strong>Background: </strong>The pathogenesis of type 2 diabetes (T2DM) remained to be fully understood. Meanwhile, exosome have shown its potential to further advance diabetes research as a rich source of biomarkers. This study aims to explore the proteomic profiles of circulating plasma exosomes in individuals with varying glucose statuses and offer potentially new perspective on the pathogenesis of T2DM.</p><p><strong>Methods: </strong>Participants with different glucose status were recruited according to the diagnostic criteria of the American diabetes association. Plasma exosomes were collected and went through data independent acquisition mass spectrometry quantitative proteomics analysis. Differential proteins identified through pairwise group comparisons underwent further analysis like protein-protein interaction (PPI) and gene ontology (GO) to reveal their functions and interactions.</p><p><strong>Results: </strong>A total of 75 participants (25 euglycemia; 25 prediabetes; 25 diabetes) were included in this study. Principal coordinates analysis showed that the proteomic patterns of exosomes in groups with prediabetes and diabetes exhibited certain similarities, contrasting with those in euglycemic individuals. From pairwise differential protein comparison, 32 proteins were selected for PPI and functional analysis, of which 7 were deemed significant within the network. GO annotations highlighted a close link between immunity and T2DM. Local STRING clustering, Reactome and KEGG pathway analysis all indicated great significance of complement and coagulation cascades.</p><p><strong>Conclusion: </strong>The proteomic patterns of exosomes in groups with different glucose levels exhibited that even before overt diabetes manifests, the circulating exosome cargo signals immune and coagulatory activation distinct from normal physiology.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13317248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147493597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative enrichment of amyloid precursors refines mass spectrometry-based amyloidosis diagnosis.","authors":"Sylvaine Di Tommaso, Bertrand Chauveau, Cyril Dourthe, Jean-William Dupuy, Frédéric Saltel, Brigitte Le Bail, Anne-Aurélie Raymond","doi":"10.1186/s12014-026-09598-0","DOIUrl":"10.1186/s12014-026-09598-0","url":null,"abstract":"<p><strong>Introduction: </strong>Amyloidosis typing is crucial to determine the best therapeutic strategy for patients. Since conventional histological techniques often fail, the identification of amyloid precursors by mass spectrometry has become the new standard. However, without quantification, selecting the amyloid precursor from proteins that may be ubiquitous under non-pathological conditions can be equivocal. Therefore, we quantified protein enrichment in amyloid deposits to improve amyloidosis typing.</p><p><strong>Methods: </strong>Protein enrichment was measured by extracted ion chromatogram-based label-free quantification by comparing a microdissected amyloid area to a non-amyloid area. We assessed the discrimination ability of candidate precursors with this approach compared to the two practiced identification methods.</p><p><strong>Results: </strong>As a proof of concept, we selected 9 cases including the most common amyloidosis subtypes, 6 typed by immunohistochemistry and 3 inconclusive by immunohistochemistry. Proteins associated with amyloid deposits were identified in all samples, confirming the pathology. Where the routine clinical mass spectrometric identification techniques allowed unambiguous conclusions for 3 of 9 cases, quantification of the enrichment ratio in amyloid deposits allowed unambiguous precursor selection in all cases.</p><p><strong>Conclusion: </strong>Quantification of precursor enrichment in amyloid deposits is a promising optimization for amyloidosis typing, which should be studied in larger cohorts. Incorporated into routine clinical processes, it could improve patient care in difficult diagnostic situations.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147472805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics of human duodenum in pre-diabetes and type 2 diabetes reveals potential novel therapeutic targets for aetiology and therapeutics.","authors":"Beatriz D G Alves, Paula Monteiro, Pedro C Martins, José Braga, Rune Matthiesen, José Flávio G Videira, Inês G Mollet","doi":"10.1186/s12014-026-09595-3","DOIUrl":"10.1186/s12014-026-09595-3","url":null,"abstract":"&lt;p&gt;&lt;p&gt;BACKGROUND: Remission from pre-diabetes and type 2 diabetes (T2D) is frequently observed immediately after a duodenal/jejunal bypass. This demonstrates the reversibility of T2D and involvement of the proximal small intestine in T2D pathology. This study investigates the role of the duodenum in the pathophysiology of T2D, in line with the hypothesis, that exclusion of duodenum/jejunum in T2D (for example, in RYGB surgery) prevents release of unidentified factors that impair insulin sensitivity, leading to T2D remission. The study aimed at identifying novel protein therapeutic targets in human duodenum in T2D and clarifying aetiology. METHODS: Proteome analysis using nano LC-MS/MS was performed on human duodenum surgical samples: T2D (n = 9), pre-diabetics (n = 6) and non-diabetics (n = 11). Proteins were quantified and analysed using MaxQuant and Perseus software. Linear discriminant (LDA), repeated k-fold cross-validation and receiver operating characteristic (ROC) analyses were used to segregate disease groups for the highest-ranking proteins. Pilot mRNA expression of five selected protein targets was analysed using real-time RT-qPCR on duodenum from human and the pre-clinical high-fat diet C57BL/6J mouse model of pre-diabetes. RESULTS: Proteomics identified and quantified significant changes in 23 proteins (one-way-ANOVA and Holm adjusted pairwise comparisons), differentiating pre-diabetes and T2D. LDA analyses accurately distinguished disease states and identified highest ranking for 10 proteins distinguishing type 2 diabetics, pre-diabetics and non-diabetics with first linear discriminant LD1 explaining 92% of differences; of which three proteins increased in pre-diabetes (DYNC1LI1, KIF5B and MAPRE1; ROC AUC = 0.89,0.82 and 0.86) are involved in vesicle and organelle transport along microtubules to and from the periphery; and seven proteins decrease in T2D (FUBP3, TPPP, NDUFAB1, ATP2B4, NPLOC4, DHRS4, CHMP3; ROC AUC = 0.81, 0.81, 0.77, 0.83, 0.82, 0.83, 0.76) revealing disruption in gene transcription, microtubule stability, mitochondrial function, Ca2+ signalling, unfolded protein response, redox status and multivesicular body formation. Two secreted proteins (APOA4 and RBP4; ROC AUC = 0.79 and 0.80) were also increased in T2D. Modelling using k-fold cross-validation, identified predictive power for 17 of the 23 proteins. At the mRNA level only borderline significant changes in Chmp3 and Fubp3 were detected using the duodenum of the pre-clinical high-fat diet C57BL/6J mouse model of pre-diabetes. CONCLUSION: Overall, this study addresses a critical knowledge gap, supports our hypothesis, and contributes to a deeper understanding of intestinal involvement in T2D pathogenesis, by examining how changes in the duodenum proteome landscape might reveal mechanisms that contribute to metabolic imbalance in T2D. While these results are currently purely cross-sectional and associative, and their contribution to causality and pathophysiological i","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of human kallikrein 6 in the cerebrospinal fluid of patients with progressive and non-progressive alzheimer's disease: correlation with other biomarkers.","authors":"Miyo K Chatanaka, Antoninus Soosaipillai, Amanda Cano, Adelina Orellana, Mercè Boada, Ioannis Prassas, Xavier Morató, Eleftherios P Diamandis","doi":"10.1186/s12014-025-09577-x","DOIUrl":"10.1186/s12014-025-09577-x","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) biomarkers in plasma and cerebrospinal fluid (CSF) are useful for disease diagnosis, prognosis, risk assessment and monitoring therapy response, as well as for uncovering altered disease pathways. Previously, we and others cloned a novel gene, KLK6, which encodes a serine protease of the kallikrein family. The protein (hK6) is highly expressed in the brain, spinal cord and cerebellum.</p><p><strong>Methods: </strong>To examine the correlation of hK6 concentration in CSF with various clinicopathological variables in AD, we used a quantitative ELISA system. The variables examined included patient age, sex, MMSE score, APOE status, amyloid β 1-42 (Αβ1-42), phosphorylated Tau 181 (p-Tau181), total Tau (t-Tau). Previously, using a cohort of Swedish and Norwegian patients, we established a positive correlation between CSF hK6 and age as well as the levels of core AD biomarkers in four groups of patients (cognitively normal, MCI without progression to AD, MCI with progression to AD within 2 years and AD dementia). In this investigation, our goal was to validate these previous data with a large and independent patient cohort from Spain.</p><p><strong>Results: </strong>We found that CSF hK6 is minimally or not affected by patient age and sex, but it significantly correlates with MMSE score and CSF Aβ1-42, p-Tau1811 and t-Tau.</p><p><strong>Conclusions: </strong>We conclude that these correlations further support our previous findings and suggest that hK6 may be an additional biomarker for AD and may play some role in the pathogenesis of AD.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13104293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147462812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinic-first sepsis recognition in the ICU: a proteomics-guided, parsimonious model with independent validation.","authors":"A Khaleghi Ardabili, S Rice, A Samuelsen, Ruth-Ann Brown, Anthony S Bonavia","doi":"10.1186/s12014-026-09597-1","DOIUrl":"10.1186/s12014-026-09597-1","url":null,"abstract":"<p><strong>Background: </strong>Sepsis recognition in the ICU remains variable and relies on consensus clinical criteria rather than biomarker-defined rules. Routine laboratory and physiologic data often overlap with noninfectious critical illness, obscuring early identification. We evaluated whether discovery proteomics could prioritize a concise set of routinely obtainable clinical variables, yielding a practical, clinic-first model that distinguishes sepsis from other critical illness.</p><p><strong>Methods: </strong>In a prospective, single-center pilot at an academic medical center, we enrolled adults within 48 h of critical illness onset (sepsis and non-sepsis comparators). Plasma proteomics by LC-MS/MS with diaPASEF identified proteins differentiating groups and guided selection of proteome-enriched routine variables for modeling. A Random Forest classifier was trained in a Discovery cohort (n = 55) and evaluated in an independent Validation cohort (n = 59), with prespecified attention to discrimination, parsimony, and feasibility for electronic health record (EHR) deployment.</p><p><strong>Results: </strong>Twelve plasma proteins differed between groups at FDR < 0.10, supporting biological separation. A parsimonious model using routine predictors ± CCL3 achieved AUC 0.73 in Discovery and AUC 0.76 in the independent Validation cohort. Recursive feature elimination demonstrated a parsimony plateau at ~ 9 variables; beyond this threshold, further reduction degraded accuracy. Notably, blood urea nitrogen, CCL3 (measured by multiplex immunoassay), and creatinine were the final features retained before performance declined, aligning with renal stress and inflammatory signaling. Figures present ROC curves and the parsimony profile, highlighting a minimal variable set compatible with typical ICU workflows and decision-support systems.</p><p><strong>Conclusions: </strong>A proteomics-informed, clinic-first strategy produced a parsimonious set of routine variables that discriminated sepsis from other ICU critical illness with clinically meaningful accuracy and an immediately actionable footprint. Because most predictors are routinely captured in the EHR, the model is EHR-compatible; CCL3 is readily measurable on standard immunoassay platforms if adopted locally. These findings justify multicenter studies to confirm generalizability and calibration, evaluate real-time integration into ICU workflows, and test whether an early recognition adjunct improves timeliness of sepsis care and patient outcomes.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}