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PubMed2026

NPLOC4 Constructs Tumor Immunosuppressive Microenvironment in Pan-cancer and Hepatocellular Carcinoma.

INTRODUCTION: NPLOC4 (nuclear protein localization 4 homolog) is mainly involved in DNA damage, cell cycle, and ubiquitination promotion. Nonetheless, the role of NPLOC4 in the tumor immune microenvironment (TIME) and its potential as a promising tumor therapeutic target remains unclear. METHODS: Therefore, analyses of NPLOC4 mRNA and protein expression, RNA subcellular localization, and patient prognosis associated with NPLOC4 expression were conducted across multiple tumor types. Additionally, the correlations between NPLOC4 and immune cells, non-immune cells, and immune molecules within the tumor immune microenvironment (TIME) were investigated. These analyses utilized data from various public resources, including the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), The Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), TIMER2.0, KM-Plotter, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), and Tumor Immune Single-cell Hub 2 (TISCH2). Subsequently, we utilized hepatocellular carcinoma (HCC) patients' cancer and adjacent tissues plus tumor cell lines to verify the differential RNA and protein expression of NPLOC4 via qRT-PCR and immunohistochemistry (IHC). Then, the relationship of NPLOC4 expression level with immune infiltration score, infiltration of effector immune cells, suppressive immune cells, and several vital immune checkpoints was analyzed in HCC immune microenvironment. Furthermore, the distribution of expression of NPLOC4 in various cells in the HCC microenvironment was determined through single-cell sequencing analysis. RESULTS: We discovered that NPLOC4 was up-regulated in a variety of tumors and was correlated with poor prognosis. NPLOC4 not only had the potential as a tumor prognostic marker and therapeutic target but also was strongly linked to immune cells, immune checkpoints, and immune-related molecules and pathways in HCC immune microenvironment. CONCLUSION: In summary, NPLOC4 may serve as a promising target for immunotherapy.

Humans↗

PubMed2026

Harnessing metabolomics and proteomics in a clinical trial for pulmonary arterial hypertension: insights from post-hoc analysis of the REHAB-PH trial.

BACKGROUND: The significant clinical and molecular heterogeneity of pulmonary arterial hypertension (PAH) poses challenges in identifying effective therapies. Advanced multidimensional profiling offers an opportunity to capture molecular responses and assess biomarker stability, yet its application in randomised trials remains limited. METHODS: We evaluated the multi-omic profiles of participants with PAH in a randomised, placebo-controlled trial of famotidine. Plasma metabolomic and proteomic profiling was performed at enrolment and 24 weeks. Baseline profiles were compared between treatment arms to assess randomisation balance. Intraclass correlation coefficients quantified within-subject stability over time. Linear regression models adjusting for age, sex, body mass index and PAH aetiology evaluated famotidine's molecular effects. False discovery rate was controlled for multiple comparisons. FINDINGS: For the 79 participants, baseline multi-omic profiles were similar between groups. At 24 weeks, 34 and 37 participants remained in the famotidine and placebo groups respectively. The placebo group showed high molecular stability, while greater variability was observed in the famotidine group. Famotidine treatment was associated with significant changes across 191 proteomic pathways (q-value <0.05), but no metabolomic changes remained significant after multiple-testing correction. INTERPRETATION: Integrating multi-omics into a prospective clinical trial is feasible and yields stable longitudinal profiles in the absence of intervention. While famotidine did not yield clinical benefit, associated proteomic changes illustrate how molecular profiling can reveal treatment-related biology and inform future trial design. These findings highlight the broader utility of multi-omics for evaluating drug responses and identifying molecular endotypes in PAH and beyond. FUNDING: US National Institutes of Health.

Humans↗

PubMed2025

Clinical and Multiorgan Proteomics Characteristics of the Diverse Fatal Phase in Super Elderly Patients With SARS-CoV-2 Infection: A Descriptive Study.

This study aims to identify the risk factors associated with clinical outcomes and the proteomic changes in organs related to fatal SARS-CoV-2 infection within the super-elderly population. This retrospective analysis included all elderly individuals with COVID-19 admitted to the Second Medical Center of PLA General Hospital from December 2022 to January 2023. The follow-up period ended on March 30, 2023. During this time, epidemiological, demographic, laboratory, and outcome data were analyzed descriptively. Proteomic sequencing was performed on super-elderly patients who died from COVID-19 at different stages of the disease. A total of 352 elderly COVID-19 patients, with a mean age of 89.84 ± 8.54 years, were included in this study. During a median follow-up period of 98 days, 79 patients died. Deceased patients were older and more likely to have cardiovascular and cerebrovascular diseases, with a lower prevalence of lipid-lowering therapy. The number of deaths in the acute and post-acute phases were 34 and 45, respectively. Proteomics data suggest that the immune systems of patients who died in the acute phase underwent a more rapid and severe onslaught. Patients in the post-acute phase showed higher levels of viral genome replication and a more robust immune response. However, the over-activation of the immune system led to systemic organ dysfunction. Effective management of comorbidities may improve the prognosis of COVID-19 in super-elderly patients. The continuous replication of the SARS-CoV-2 virus and its subsequent impact on the immune system are critical determinants of survival time in this demographic.

Humans↗

PubMed2025

Proteomic Analysis of 442 Clinical Plasma Samples From Individuals With Symptom Records Revealed Subtypes of Convalescent Patients Who Had COVID-19.

After the coronavirus disease 2019 (COVID-19) pandemic, the postacute effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have gradually attracted attention. To precisely evaluate the health status of convalescent patients with COVID-19, we analyzed symptom and proteome data of 442 plasma samples from healthy controls, hospitalized patients, and convalescent patients 6 or 12 months after SARS-CoV-2 infection. Symptoms analysis revealed distinct relationships in convalescent patients. Results of plasma protein expression levels showed that C1QA, C1QB, C2, CFH, CFHR1, and F10, which regulate the complement system and coagulation, remained highly expressed even at the 12-month follow-up compared with their levels in healthy individuals. By combining symptom and proteome data, 442 plasma samples were categorized into three subtypes: S1 (metabolism-healthy), S2 (COVID-19 retention), and S3 (long COVID). We speculated that convalescent patients reporting hair loss could have a better health status than those experiencing headaches and dyspnea. Compared to other convalescent patients, those reporting sleep disorders, appetite decrease, and muscle weakness may need more attention because they were classified into the S2 subtype, which had the most samples from hospitalized patients with COVID-19. Subtyping convalescent patients with COVID-19 may enable personalized treatments tailored to individual needs. This study provides valuable plasma proteomic datasets for further studies associated with long COVID.

Humans↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2025

High-affinity CD16A polymorphism associated with reduced risk ofsevere COVID-19.

CD16A is an activating Fc receptor on NK cells that mediates antibody-dependent cellular cytotoxicity (ADCC), a key mechanism in antiviral immunity. However, the role of NK cell-mediated ADCC in SARS-CoV-2 infection remains unclear, particularly whether it limits viral spread and disease severity or contributes to the immunopathogenesis of COVID-19. We hypothesized that the high-affinity CD16AV176 polymorphism influences these outcomes. Using an in vitro reporter system, we demonstrated that CD16AV176 is a more potent and sensitive activator than the common CD16AF176 allele. To assess its clinical relevance, we analyzed 1,027 patients hospitalized with COVID-19 from the Immunophenotyping Assessment in a COVID-19 cohort (IMPACC), a comprehensive longitudinal dataset with extensive transcriptomic, proteomic, and clinical data. The high-affinity CD16AV176 allele was associated with a significantly reduced risk of ICU admission, mechanical ventilation, and severe disease trajectories. Lower anti-SARS-CoV-2 IgG titers were correlated to CD16AV176; however, there was no difference in viral load across CD16A genotypes. Proteomic analysis revealed that participants homozygous for CD16AV176 had lower levels of inflammatory mediators. These findings suggest that CD16AV176 enhances early NK cell-mediated immune responses, limiting severe respiratory complications in COVID-19. This study identifies a protective genetic factor against severe COVID-19, informing future host-directed therapeutic strategies.

Humans↗

PubMed2024

Analysis of Antibiotic Response in Clinical Wound Pseudomonas aeruginosa Isolates: Unveiling Proteome Dynamics of Tobramycin-Tolerant Phenotype.

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic human pathogen, causing serious chronic infections. P. aeruginosa can adapt efficiently to antibiotic stressors via different genotypic or phenotypic strategies such as resistance and tolerance. The adaptation regulatory system is not always very well understood. In this study, we use shotgun proteomics to investigate the system-level response to tobramycin in two clinical wound P. aeruginosa isolates and PAO1. We profiled each strain for its antibiotic drug-tolerant phenotype using supra-minimum inhibitory concentrations (supra-MICs) of tobramycin and applied proteomics to investigate the protein expression profiles. The MIC revealed that all isolates were susceptible to tobramycin but at supra-MICs at stationary growth, a degree of tolerance was observed for the isolates. We identified around 40% of the total proteins encoded by the P. aeruginosa genome and highlighted shared and unique protein signatures for all isolates. Comparative proteome profiling in the absence of antibiotic treatment showed divergent fingerprints, despite similarities in the growth behavior of the isolates. In the presence of tobramycin, the isolates shared a common response in the downregulation of proteins involved in the two-component system, whereas stress response proteins were present at higher levels. Our findings provide insight into the use of proteomic tools to dissect the system-level response in clinical isolates in the absence and presence of antibiotic stress.

Pseudomonas aeruginosa↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

Integrative pan-cancer analysis of transferrin reveals context-dependent prognostic associations and links to immune and metabolic disease-related programs.

BACKGROUND: Iron metabolism is closely linked to tumor biology, yet the pan-cancer significance of transferrin (TF), the major circulating iron-transport protein, remains insufficiently defined. Although TF has been implicated in cancer-related processes, its prognostic relevance, immune associations, and broader disease-related transcriptional context have not been systematically characterized across tumor types. OBJECTIVE: This study aimed to perform an integrative pan-cancer analysis of TF to characterize its expression patterns, clinical associations, immune context, pathway features, and pharmacogenomic correlations, and to explore whether TF-related signals extend to selected metabolic and chronic organ injury settings. METHODS: We used multiple public databases, including The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Gene Expression Omnibus (GEO), and Cancer Cell Line Encyclopedia (CCLE), to integrate transcriptomic, proteomic, and clinical data across 33 tumor types and selected non-malignant conditions. TF expression was evaluated across normal tissues, tumors, and cell lines, followed by survival analysis, immune infiltration analysis, TMB/MSI and methylation assessment, pathway enrichment, and drug-response correlation. Independent GEO cohorts of non-alcoholic steatohepatitis (NASH), heart failure (HF), and liver cirrhosis (LC) were used for cross-disease extension. Selected findings were further explored in OA/PA-treated hepatocytes, 786-O renal carcinoma cells, and AC16 cardiomyocytes. RESULTS: TF showed pronounced tissue specificity and cancer-type-dependent dysregulation. Across pan-cancer cohorts, the most consistent adverse survival associations were observed in kidney renal clear cell carcinoma (KIRC) and stomach adenocarcinoma (STAD), where TF remained associated with overall survival (OS) in multivariable analyses. TF expression was also correlated with cancer-type-specific immune infiltration patterns and selected drug-response profiles. Across independent NASH, HF, and LC datasets, TF expression was elevated and TF-associated pathways partially overlapped with those observed in cancer. In vitro experiments provided preliminary support that TF modulation is associated with proliferative phenotypes in KIRC cells and stress- and metabolism-related phenotypes in hepatocyte and cardiomyocyte models. CONCLUSION: These findings support TF as a context-dependent biomarker candidate in cancer, with the most consistent prognostic relevance observed in KIRC and STAD. Rather than establishing a unified mechanism across diseases, this study provides an integrative framework suggesting that TF is associated with malignant behavior, immune context, and selected metabolic stress-related programs, and warrants further mechanistic investigation.

Iron metabolism↗

PubMed2026

Proteomic Profile in Retinopathy of Prematurity: A Secondary Analysis of the Mega Donna Mega Randomized Clinical Trial.

IMPORTANCE: Identifying early proteomic profiles in infants who develop severe retinopathy of prematurity (ROP) may reveal targets for preventive interventions to reduce retinal vessel loss and the subsequent risk of severe ROP. OBJECTIVE: To assess early longitudinal profiles of blood protein levels in preterm infants with or without severe ROP and the effect of arachidonic acid (AA) and docosahexaenoic acid (DHA) supplementation. DESIGN, SETTING, AND PARTICIPANTS: This was an exploratory, post hoc analysis of serum proteome profiles in preterm infants in the double-masked Mega Donna Mega (MDM) randomized clinical trial using targeted Olink Proximity Extension Assay proteomics covering 538 analytes. The setting was 3 university hospitals in Sweden and included extremely preterm infants born before 28 weeks of gestational age (GA), from 2016 to 2019. Data were analyzed from January to March 2025. EXPOSURES: All infants received standard nutrition; additionally, half received enteral lipid supplementation with AA/DHA (100/50 mg/kg per day) from birth to term equivalent age. MAIN OUTCOMES AND MEASURES: Longitudinal protein profiles during the first month of life were examined using mixed models for repeated measures, adjusted for GA, study center, and AA/DHA supplementation, and tested for the interaction between severe ROP (stage ≥3 and/or treated) and postnatal age. RESULTS: A total of 177 extremely preterm infants (mean [SD] GA, 25.6 [1.4] weeks; 100 male [56.5%]) were included, of whom 50 (28.2%) developed severe ROP. Of 538 longitudinal analyzed proteins, 109 protein profiles in the first month of life associated with severe ROP, proteins related to immune response, apoptotic processes, blood coagulation, and lipid metabolism. The most pronounced association with severe ROP was a fast rise in fibroblast growth factor 21 (FGF-21; β = 0.68; 95% CI, 0.39-0.97; Q =.002) and tissue plasminogen activator (tPA; β = 0.21; 95% CI, 0.13-0.29; Q <.001) during the first postnatal days. The increase in serum FGF-21 level in the first week of life was associated with lower GA, lower birth weight, low enteral energy intake, and more days receiving mechanical ventilation. No association was observed between AA/DHA supplementation and the proteome. CONCLUSIONS AND RELEVANCE: In this post hoc exploratory analysis of data from the MDM randomized clinical trial, a fast rise in FGF-21 levels, a metabolic stress-induced hormone, during the first postnatal days was strongly associated with the development of severe ROP in extremely preterm infants. These findings suggest that early interventions improving bioenergetic status may help prevent severe ROP. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03201588.

Humans↗

PubMed2026

An open benchmark and language models for AI in aging biology.

Over the past two decades, human aging has been characterized across DNA methylation, transcriptomic, proteomic, and clinical modalities, yet no benchmark evaluates whether AI systems can interpret these heterogeneous data types in the context of aging biology. We introduce LongevityBench, an open suite of 17 tasks spanning five biodata domains, and use it to assess 18 frontier AI systems from six developer teams. Despite recent advances in AI, no single model dominates all tasks, with omics-based age prediction being the hardest task regardless of scale. To test whether these gaps can be closed without frontier-scale resources, we fine-tuned a family of five multitask Longevity-LLMs on domain-specific aging data. The compact (0.6B-9B parameters) Longevity-LLMs matched or exceeded far larger frontier systems on LongevityBench, showing that general-purpose language models can be adapted to structured-omics tasks. We publicly release the benchmark, models, and Longevity Claw, an agentic research interface for aging researchers.

Aging↗

PubMed2026

Proteogenomic analysis of pediatric and AYA high-grade glioma reveals age-dependent biology, female-male differences, and kinase targets.

High-grade gliomas (HGGs) in children and adolescents and young adults (AYA) exhibit distinct biology across the neurodevelopmental spectrum. To dissect tumor-intrinsic molecular characteristics independent of developmental variation, we perform comprehensive proteogenomic analyses of tumors from 112 HGG patients aged 0-40 years. Our multi-omics analysis identifies two AYA subgroups-adolescents (aged 15-26 years) and young adults (aged 26-40 years)-with distinct molecular profiles and survival outcomes. Tumor-normal comparisons and survival modeling highlight roles of oxidative phosphorylation and neuronal system biology in glioma progression. Causal network analysis and cell line studies provide a rationale for personalized therapies targeting candidate kinases, such as CDK8. Survival modeling, clustering, and immune-landscape analyses identify proteins, post-translational modifications, and immune signatures linked to outcomes and reveal clinically relevant differences between male and female patients.

adolescent and young adult glioma↗

PubMed2025

Diagnosing the undiagnosed: AI-enhanced multimodal modeling for placental mesenchymal dysplasia in high-risk pregnancies.

Placental mesenchymal dysplasia (PMD) is a rare vascular placental disorder that mimics molar pregnancy but often coexists with a viable fetus, making its misdiagnosis potentially devastating. In high-risk pregnancies, artificial intelligence (AI)-enhanced multimodal modeling - incorporating imaging, genomics, proteomics, and clinical features - offers a transformative diagnostic strategy. Leveraging Bayesian hyperparameter optimization for model refinement, this approach improves diagnostic accuracy while reducing uncertainty and clinician hesitation. Recent clinical studies support its efficacy and interpretability through SHAP and LIME models, while real-time surgical enhancements using Bayesian methods highlight its broader clinical utility. Despite current challenges such as data heterogeneity and integration barriers, multimodal AI provides unprecedented resolution in placental analysis, enabling precise differentiation between PMD and similar fetopathies. Ultimately, this advancement supports timely, non-invasive diagnosis, personalized management, and emotionally informed decision-making aligned with ethical AI implementation standards.

Bayesian optimization↗

PubMed2026

A lipid-immune network signature defines susceptibility to asparaginase-associated pancreatitis.

BACKGROUNDAsparaginase is essential for curing acute lymphoblastic leukemia (ALL), but its use is limited by asparaginase-associated pancreatitis (AAP), a severe and unpredictable toxicity lacking validated prospective biomarkers. We sought to define early systemic molecular features of susceptibility to AAP.METHODSWe performed longitudinal lipidomic and proteomic profiling in two independent pediatric ALL cohorts (n = 161; 79 AAP cases, 82 controls) using paired blood samples collected before asparaginase exposure and at the end of induction therapy (including a single dose of asparaginase), thereby capturing pre-injury biology rather than consequences of pancreatitis. We applied differential abundance and network-based analyses and integrated lipid-cytokine associations using proteomics.RESULTSAcross cohorts, we identified a reproducible lysophosphatidylcholine-centered (LPC-centered) signature characterized by attenuated induction therapy-associated LPC responses and disruption of LPC coregulation at the network level. Proteomic profiling revealed enrichment of cytokine signaling pathways, and integrative analyses demonstrated altered lipid-cytokine coupling, including a flip in association direction for LPC species and IL-18 between cases and controls. Although IL-18/LPC ratios did not differ globally, elevated postinduction IL-18/LPC ratios identified AAP risk within a protocol-defined very high-risk ALL subgroup (AUC = 0.81).CONCLUSIONThese findings support a systems-level model in which failure of coordinated lipid-immune responses under therapeutic stress confers vulnerability to AAP, providing a framework for validation and mitigation strategies.TRIAL REGISTRATIONNCT00400946; NCT01574274; NCT03020030 (parent trials).FUNDINGServier Pharmaceuticals (IIT-95014-027-USA); SDRC (P30DK116074); Stanford SPARK; Fonds de Recherche du Québec - Santé; Fondation Charles-Bruneau; Leukemia & Lymphoma Society of Canada.

Adolescent↗

PubMed2025

Long COVID in Elderly COPD Patients: Clinical Features, Pulmonary Function Decline, and Proteomic Insights.

BACKGROUND: Elderly patients with chronic obstructive pulmonary disease (COPD) face a heightened risk of developing long coronavirus disease (COVID); however the exact clinical characteristics and underlying mechanisms remain unclear. METHODS: We enrolled 85 elderly COPD patients, of whom 43 reported newly onset persistent fatigue (the most dominant complaint of long COVID) within 1 year after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and they were allocated to the Long-COVID group. The remaining 42 patients were assigned to the Control group. Patients completed questionnaires, pulmonary function tests, chest CT, routine laboratory tests, and blood proteomic analysis. RESULTS: Long-COVID patients had a longer course of COPD (> 5 years, 76.8% vs 52.4%) and duration of SARS-CoV-2 infection (10.0 days vs 7.0 days) (All P < 0.05), higher symptom burden, worse pulmonary ventilation function and a more rapid decrease in DLCO (All P < 0.05). Proteomic analysis indicated disruptions in inflammation and energy metabolism, potentially underlying long COVID in these patients. The machine learning model identified wheezing, the duration of SARS-CoV-2 infection, EIF2S3 (eukaryotic translation initiation factor 2 subunit gamma), current FEV1/FVC (%), and the course of COPD as key features distinguishing Long-COVID patients, and exhibited excellent performance. CONCLUSION: Elderly COPD patients with a longer COPD course and duration of COVID-19 are more prone to develop long COVID, with decreased pulmonary ventilation and diffusion ability. Disordered inflammation regulation and energy metabolism may be the potential mechanisms, highlighting the importance of monitoring inflammation and metabolic dysregulation in elderly COPD patients after recovery from COVID-19.

Humans↗

PubMed2025

A clinically applicable method for early interstitial lung disease detection in incident rheumatoid arthritis cases: integration of protein biomarkers and clinical factors.

BACKGROUND: This study aimed to develop an early diagnostic method integrating proteomic biomarkers and clinical parameters for screening interstitial lung disease (ILD) in patients with newly diagnosed rheumatoid arthritis (RA) through a multi-phase research strategy. METHODS: A three-phase study was conducted: (1) Discovery: Tandem mass tag (TMT)-labeled quantitative proteomics with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyzed serum protein profiles in 5 RA-ILD and 5 RA-non-ILD patients, identifying candidates via bioinformatics. (2) Verification: Enzyme-linked immunosorbent assay (ELISA) validated candidates in an independent cohort (13 RA-ILD vs 14 RA-non-ILD). (3) Application: Biomarkers combined with clinical indicators (Krebs von den Lungen-6 [KL-6], age, sex) were evaluated in 110 patients (51 RA-ILD vs 59 RA-non-ILD) to build a predictive model. RESULTS: Proteomic analysis identified matrix metalloproteinase-3 (MMP3), von Willebrand factor (VWF), and other significantly differentially expressed proteins. ELISA validation confirmed that serum MMP3 and VWF levels were significantly higher in the RA-ILD group than in the RA-non-ILD group (p = 0.025 and 0.027, respectively). Expanded validation demonstrated superior diagnostic performance when combining MMP3 and VWF with KL-6 (area under the curve [AUC] = 0.90). The nomogram prediction model based on univariate analysis exhibited excellent discrimination (AUC = 0.89) and calibration. CONCLUSION: This systematic study from discovery to validation identified MMP3 and VWF as potential biomarkers for RA-ILD. The integrated predictive model combining these biomarkers with clinical parameters (KL-6, age, sex) provides a potential tool for early ILD screening in RA patients, offering novel strategies for early diagnosis and intervention of RA-ILD.

Humans↗

PubMed2026

Proteomic Identification of Pig Xenoantigens for Clinical Xenotransplantation.

Xenotransplantation using genetically engineered pig organs offers a promising solution to the shortage of donor organs for life-saving transplantations. However, human-preformed antibodies against unknown pig xenoantigens remain a significant barrier to successful xenotransplantation. Current methods for characterizing these antibodies or xenoantigens are limited to cellular-level cross-match assays. In this study, we developed a novel approach to identify pig xenoantigens, including peptide and glycopeptide epitopes, that react with human-preformed antibodies. First, human-preformed antibodies against xenoantigens were enriched from plasma using immobilized pig kidney proteins. The enriched antibodies were then immobilized and used to isolate pig kidney proteins, peptides, and intact glycopeptides, followed by liquid chromatography-tandem mass spectrometry analysis. This dual-level approach identified 221 peptides corresponding to 153 proteins, with a significant enrichment of plasma membrane and extracellular proteins. Notably, 11 peptides were unique to pig sequences, suggesting their potential role in driving xenogeneic immune responses. Glycoproteomic analysis identified 122 intact glycopeptides, predominantly complex/hybrid glycoforms, and Neu5Gc-containing glycans. Our method effectively identifies peptides and intact glycopeptides reactive to human-preformed antibodies, providing critical insights for discovering xenoantigens. These findings could guide genetic engineering strategies and enhance recipient candidate screening for xenotransplantation, ultimately increasing the feasibility and success of xenogeneic organ transplantation.

Animals↗

PubMed2026

AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms remain poorly understood, and reliable biomarkers for early prediction and prevention are lacking. OBJECTIVE: The CONFUSED study aims to identify molecular and clinical predictors of POD by integrating clinical data with proteomic, transcriptomic, and epigenetic analyses. The primary objective is to develop predictive models for POD using multimodal data. Secondary objectives include the identification of delirium-associated genes, proteins, and epigenetic signatures, as well as the exploration of patient subgroups at increased risk for POD. METHODS: CONFUSED is a prospective observational cohort study conducted at a German university hospital. Adult patients undergoing major surgery under general anesthesia will be enrolled until 100 cases of POD have been observed, which is expected to require a total sample size of approximately 200 to 300 patients. Blood samples are collected at 4 predefined time points: before premedication, immediately after surgery, and on postoperative days 2 and 5. Samples undergo comprehensive proteomic profiling, transcriptomic analysis using RNA microarrays, DNA methylation analysis, and genotyping of selected polymorphisms. Clinical data, including demographics, comorbidities, perioperative variables, medications, and delirium assessments using the Confusion Assessment Method (CAM) and CAM for the intensive care unit, are systematically recorded. Statistical analyses include univariate and multivariate methods, as well as machine learning approaches such as random forests and support vector machines, to identify relevant biomarkers and develop predictive models. The study protocol follows STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines and was approved by the responsible ethics committees. RESULTS: The study was registered in the German Clinical Trials Register (DRKS00033854) on March 18, 2024. Recruitment started in January 2024 and is ongoing at the time of manuscript submission. As of now, 135 patients have been enrolled. Sample collection and laboratory analyses are ongoing. Data analysis began in January 2026, with first results anticipated in July 2026. Final data lock is anticipated after the completion of recruitment. CONCLUSIONS: By integrating multimodal molecular data with clinical parameters and applying advanced machine learning techniques, the CONFUSED study aims to improve the prediction and understanding of POD. The results are expected to support the development of personalized preventive strategies and contribute to improved perioperative care for patients at risk of POD.

Humans↗