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Results for “Human Proteome Project”

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PubMed2025

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

GENCODE↗

PubMed2026

Machine learning-based analysis of oral rinse samples to identify candidate proteomic signatures for severe periodontitis: a pilot study.

This pilot study investigated whether candidate protein signatures from oral rinse samples can distinguish patients with severe periodontitis (stage III/IV) and its subtypes, generalized and localized periodontitis, from non-periodontitis controls. Participants rinsed with phosphate-buffered saline, and samples were analyzed using a Proximity Extension Assay targeting 92 inflammatory and 92 immuno-oncology proteins. A machine learning approach using repeated nested cross-validation and SHAP was implemented to identify protein signatures. The study included 38 patients (18 with localized periodontitis and 20 with generalized periodontitis) and 16 controls. After data preprocessing, 54 samples and 141 proteins were retained. Proteins Gal-1, HGF, TNFSF14, CD27, and ARG1 distinguished periodontitis from controls (ROC-AUC = 0.85, 95% CI 0.82, 0.87). For generalized periodontitis, we found a protein signature including TNFSF14, Gal-1, STAMBP, MUC-16, S100A12, HGF, CASP-8, CD27, LAP TGF-β1, TNFRSF9, and uPA (ROC-AUC = 0.92, 95% CI 0.90, 0.94). For localized periodontitis, we identified ARG1 (ROC-AUC = 0.72, 95% CI 0.68, 0.76). No proteomic signature distinguishing generalized periodontitis from localized periodontitis was identified. This pilot study indicated that oral rinses are suitable for proteomic profiling, and there was a putative protein signature that could differentiate periodontitis, generalized periodontitis, and localized periodontitis from controls. These findings warrant validation in larger independent cohorts, including a clearly defined gingivitis group, before real-world non-invasive screening applications can be considered.

Humans↗

PubMed2026

7-day longitudinal proteomics of critically ill patients: a pilot study.

An adult's health, indicated by measurable parameters, is stable over time. With the exception of circadian rhythms, variability in these parameters typically does not exceed 20%. In this pilot study, we looked into the stability of proteome in intensive care unit (ICU) patients. This was a single-center, prospective, observational pilot study of blood plasma from adult ICU patients with statistically heterogeneous patterns of clinically observed parameters. Eight week-long batches from seven patients (one patient participated twice) were analyzed by means of bottom-up proteomics. The data were analyzed with MaxQuant software against reference proteome. The obtained intensities were further processed with in-house R and Python scripts. In total, 218 proteins were identified; however, only 68 proteins appeared in all samples from all patients. Most proteins remained stable within observation (within-patient variance was less than 30%). The random-effects model also confirmed high impact of within-patient variance on the protein levels. The effects of time on the protein level variances did not exceed 5%. Z-score-based hierarchical clustering analysis revealed that the daily data of each patient were clustered together indicating that the plasma proteome of ICU patients both bears individual traits and remains stable during short-term progression of the patients' condition. Therefore, in this pilot group of patients, the analysis over seven consecutive days fails to reveal proteome dynamics.

Humans↗

PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗

PubMed2026

Proteomic Profile Differences in Immune-Related Diseases in Pediatric Patients Under Five Years Old: Asthma and IgE-Dependent Allergies-A Pilot Study.

Asthma is a heterogeneous disease that often begins in childhood and frequently occurs alongside allergic conditions. In asthma research, it is important to focus on proteins that are the primary regulators of cellular physiology. The differences in the proteome between children with asthma and those with an atopic background remain poorly understood. The present study included 130 serum samples from four groups of pediatric patients under the age of five: (1) with asthma and IgE-dependent allergies; (2) with non-atopic asthma; (3) non-asthmatics with IgE-dependent allergy; and (4) a control group without asthma and IgE-dependent allergies. The serum samples were used for protein-peptide profiling and proteomic identification using nanoLC-MALDI-TOF/TOF MS/MS. The obtained data were analyzed using univariate statistics and the STRING tool v12.0 to identify protein-protein potential interactions. A total of seven proteins were identified as discriminative between the study groups: A2M, AACT, IgG3, C3, ITIH2, IgG3 and IGK. All of them were upregulated in patients with IgE-dependent allergy compared to other study groups. STRING analysis identified functional associations among four proteins (AACT, A2M, C3, and ITIH2) with discriminatory potential for distinguishing between non-atopic asthma and non-asthmatic patients with IgE-dependent allergy. The results suggest that the identified putative protein markers overlap in cellular pathways, including those associated with the pathophysiology of asthma and allergic disorders. These findings provide further insight into the overall proteomic profile of pediatric patients with asthma and IgE-dependent allergy, highlighting its heterogeneity across the analyzed groups.

Humans↗

PubMed2025

Mendelian randomization reveals causal relationships between cytokines and male reproductive diseases.

This study aims to explore the causal links between cytokines and four male reproductive disorders, namely abnormal spermatozoa (AS), male infertility, erectile dysfunction (ED), and hyperplasia of prostate (HP), employing a two-sample Mendelian randomization (MR) approach. Genetic associations with male reproductive diseases were derived from the IEU OpenGWAS project, with cytokine data from two GWASs focused on the human proteome and cytokines. Estimations were derived using inverse variance weighting, MR-Egger regression, weighted median, weighted model, and simple mode. Furthermore, the robustness of the findings was evaluated through Cochran's Q-test, MR-Egger regression, and leave-one-out sensitivity analysis. Fifteen unique cytokines were identified as having causal relationships with the risk of four male reproductive disorders. Specifically, for AS, interleukin-22 (IL-22), IL-12, and macrophage migration inhibitory factor were negatively correlated with AS, while tumor necrosis factor β levels were positively correlated with AS. In the context of male infertility, IL-2 receptor antagonist levels, IL-34, and granulocyte-colony stimulating factor levels were positively linked to male infertility, whereas IL-21 showed a negative relationship. Regarding ED, IL-19, IL-1β, and eotaxin levels were negatively associated with ED risk, while macrophage inflammatory protein 1β (MIP-1β) levels and interferon gamma-induced protein 10 levels were positively associated. As for HP, stromal-cell-derived factor 1α levels and MIP-1α levels revealed negative associations with HP. In conclusion, this MR analysis revealed that several cytokines were causally associated with male reproductive diseases and could be valuable in offering new insights for further mechanistic and clinical investigations of cytokines-associated male reproductive diseases.

Male↗

PubMed2026

Targeted Modulation of Abundant Proteins Enhances Proteomic Profiling of Ovarian Cancer Ascites: A Pilot Technical Workflow Comparison.

Ascites from ovarian cancer patients are increasingly recognized as a valuable biofluid for cancer research, as its protein composition reflects the disease state and may reveal biomarkers of treatment sensitivity and response. However, the detection of low-abundance proteins is hindered by the presence of highly abundant proteins such as albumin. In this study, we evaluated five protein preparation methods for their effectiveness in depleting high-abundance or enriching low-abundance proteins in ovarian cancer ascites. The Norgen (Nor), Minutes (Min), and Perchloric acid (PerCA) methods were based on abundant protein depletion, while the Urine (Uri) and Nanomics (Nano) kits focused on low-abundance protein enrichment. Processed samples were analyzed using label-free quantitative bottom-up proteomics by LC-MS/MS, followed by a bioinformatics assessment. Compared with undepleted ascites (UnD), Min, Nor, Nano, and PerCA increased protein identifications, whereas Uri produced profiles similar to those of UnD. Notably, PerCA and Nano enabled the identification of distinct protein subsets associated with cancer-related pathways, including immune responses and autophagy. PerCA enriched transmembrane and secreted immunomodulatory glycoproteins, whereas Nano enrichment primarily captured secreted, nuclear, and cytoplasmic soluble proteins. Overall, our results show that both high-abundance protein depletion and low-abundance enrichment improve ascites proteome coverage, each offering distinct advantages in identifying biologically relevant low-abundance proteins.

Female↗

PubMed2026

Pilot study identifying distinct circulating proteomic profiles associated with longitudinal CT-defined fibrotic and inflammatory sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis exhibits heterogeneous clinical trajectories ranging from self-limited disease resolution to chronic progressive fibrosis, yet reliable biomarkers capable of distinguishing these disease patterns remain lacking. Whether longitudinal CT-defined sarcoidosis phenotypes are associated with distinct circulating molecular signatures remains unknown. METHODS: We performed high-throughput plasma proteomics (SomaScan 11K) in participants with pulmonary sarcoidosis classified into longitudinal chest CT-defined progressive fibrosis, progressive nodular inflammatory disease, or resolving disease trajectories, along with healthy controls. CT phenotypes were assigned based on predefined longitudinal changes in reticulation, traction bronchiectasis, nodular involvement, and mediastinal lymphadenopathy across serial CT scans. One plasma sample per participant was selected from the study visit corresponding to the CT time point at which criteria for the assigned longitudinal phenotype were met. Principal component analysis, hierarchical clustering, pathway enrichment, and correlation-based analyses linking protein expression to quantitative CT features were used to evaluate whether distinct longitudinal CT phenotypes were associated with divergent proteomic signatures. RESULTS: Principal component analysis and hierarchical clustering suggested partial segregation by CT-defined phenotype. Longitudinal CT phenotypes were associated with distinct pathway-level proteomic signatures, with progressive fibrosis enriched for epithelial-mesenchymal transition signaling, and progressive nodular inflammatory disease enriched for mTORC1, MYC, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways. Correlation analyses showed coordinated protein-expression patterns associated with fibrotic CT features and mediastinal lymph node enlargement. DISCUSSION: These findings suggest that longitudinal CT-defined fibrotic and inflammatory sarcoidosis phenotypes are associated with distinct pathway-level proteomic signatures. This pilot study provides preliminary proof-of-concept evidence that integrating longitudinal CT imaging phenotypes with plasma proteomics may serve as a framework for future mechanistic studies and biomarker discovery in pulmonary sarcoidosis.

Humans↗

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

Inflammatory Serum Olink Proteomics in Cancer-Related Pain Treated with Opioids: A Pilot Cross-Sectional and Longitudinal Study.

Opioid analgesia shows substantial interindividual variability in cancer patients, yet the underlying serum inflammatory alterations remain poorly characterized. This study collected plasma samples from 44 cancer pain patients before and after opioid initiation, quantifying 92 immunoinflammation proteins by Olink proteomics. Cross-sectional analysis identified nine differentially expressed proteins between responders and nonresponders. A five-protein nomogram involving TGF-α, EN-RAGE, CASP-8, ST1A1, and IL-10RA demonstrated superior predictive performance for opioid efficacy (AUC 0.902) compared to traditional CRP (AUC 0.625). Longitudinal analysis of this population revealed upregulation of β-NGF, MCP-4, IL-1alpha, and IL-13, and downregulation of CD6, IL-12beta, and SCF after treatment. STRING analysis clustered these proteins into three functional groups: efficacy-related (NGF), bowel-inflammation-related (IL-12/IL-13), and CD6-related. Notably, expression of IL-12β showed a significant efficacy-constipation interaction: constipation completely reversed the efficacy-IL-12 association, and higher IL-12 levels predicted favorable response only in nonconstipated patients. These findings established a pretreatment protein signature for predicting opioid efficacy and revealed systemic immune reprogramming following opioid therapy.

Humans↗

PubMed2025

Plasma proteomic markers of pain and emotional dysfunction in fibrous dysplasia/McCune-Albright syndrome.

Pain in Fibrous dysplasia/McCune-Albright syndrome (FD/MAS) remains poorly understood and inadequately managed due to uncertainties regarding clinical or biological drivers. This cross-sectional pilot study aimed to use plasma proteomics to identify markers that inform on molecular pathways associated with pain and emotional symptoms in FD/MAS. Seventeen individuals (15 females, 2 males), aged 16 to 63 years, with confirmed diagnoses of monostotic FD, polyostotic FD, or MAS participated in a single study visit conducted at Boston Children's Hospital and Massachusetts General Brigham. During the visit, participants completed validated questionnaires assessing neuropathic pain characteristics, pain interference, anxiety symptoms, depression symptoms, and perceived stress, and provided plasma samples. These samples were analyzed for 57 proteins using Olink proximity extension assay. Associations between protein concentrations and symptom scores were evaluated using Spearman's correlations with false discovery rate correction (|r| > 0.5, p < 0.05). After FDR correction, the concentrations of seven proteins (TNF-α, LTA, CCL19, CSF2, CCL2, CCL4, CCL7) significantly correlated with pain interference, HADS-depression scores, or perceived stress. Four protein concentrations (TNF-α, CCL19, CSF2, CCL7) significantly correlated with multiple clinical measures. This pilot study identified several pain-associated proteins in individuals with FD/MAS, suggesting that proteomic profiling may be a promising approach for discovering pain biomarkers. Larger, longitudinal studies are needed to validate these results and investigate whether targeting immune pathways can alleviate pain and improve emotional health in FD/MAS.

Humans↗

PubMed2024

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression.

Cirrhosis, advanced liver disease, affects 2 to 5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery top-down proteomics to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Humans↗

PubMed2025

Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.

BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.

Humans↗

PubMed2026

Blood-based proteomic profiling reveals context-dependent changes in BCL2-associated signaling during taxane therapy in breast cancer patients.

The quality of life for many cancer survivors is compromised due to severe, long-lasting side effects of chemotherapy. As part of a pilot, prospective, non-interventional study to examine the side effects of chemotherapy in breast cancer patients, we examined the change in protein expression in blood collected from patients before and after treatment with taxanes for 12 weeks. Protein expression was measured with reverse phase proteomic arrays (RPPA), which revealed divergent changes in apoptosis, senescence, and calcium signaling-related proteins depending on treatment setting (neoadjuvant vs. adjuvant). The largest change identified was BCL2 (B-cell lymphoma 2), a founding member of the BCL2 family of proteins that regulate apoptosis. Other proteins regulated by BCL2, including RB1 (retinoblastoma protein 1) and NLRP3 (NLR family pyrin domain containing 3) changed significantly over the course of treatment. These differences are consistent with intracellular calcium signaling dysregulation and activation of stress-response pathways that overlap with senescent-associated secretory phenotype (SASP)-like signaling, which has been implicated in cancer recurrence. To contextualize these observations, we generated Kaplan-Meier survival curves using publicly available proteomics data from The Cancer Proteome Atlas (TCPA). This work aims to demonstrate how blood-based proteomics can serve as a non-invasive method to monitor systemic physiological shifts during cancer therapy, offering a framework for generating hypotheses about chemotherapy timing and long-term outcomes.

Humans↗

PubMed2024

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts.

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Humans↗

PubMed2026

ProMeta: a meta-learning framework for robust disease diagnosis and prediction from plasma proteomics.

MOTIVATION: The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. RESULTS: Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of ∼0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts. AVAILABILITY AND IMPLEMENTATION: The source code of ProMeta is available at GitHub (https://github.com/lihan97/ProMeta).

Proteomics↗

PubMed2026

Pre-treatment polyfunctionality percentage (PFA) of CD8+ T cells is associated with development of immune-related adverse events (irAEs) in patients receiving immune checkpoint inhibitors (ICIs).

INTRODUCTION: Immune checkpoint inhibitors (ICIs) have improved cancer survival, but immune-related adverse events (irAEs) occur frequently and can have devastating consequences. There are no validated methods to evaluate risk of irAEs prior to initiation of ICIs. MATERIALS AND METHODS: We conducted a pilot study evaluating the ability of blood-based, single-cell secretomic analysis to characterize irAEs. A total of 10 patients with thoracic malignancies who were scheduled to receive ICIs were enrolled. Each patient had a pre-ICI blood sample drawn as well as a sample at the time of irAE development or 12 weeks after ICI initiation, whichever came first. Utilizing IsoPlexis's IsoLight system, polyfunctionality percentages (PFAs) and strength indices (PSIs) were analyzed for CD4+ and CD8+ T cells. RESULTS: Five patients developed irAEs and 5 patients did not develop irAEs. Pre- and post-ICI CD8+ T cell PFA was significantly elevated in patients who developed irAEs compared with those who did not (p = 0.017 and p = 0.014, respectively). CONCLUSIONS: In this pilot study, pre-ICI CD8+ T cell PFA was associated with development of irAEs. While this is a pilot study, this is a first step toward developing a blood-based, streamlined assay to assess risk of irAEs prior to initiation of ICIs. Validation in larger cohorts is warranted.

Humans↗

PubMed2026

Multiomics profiling of plasma reveals lipid-immune dysregulation and exosome remodeling in mpox and mpox-HIV co-infection.

BACKGROUND: Monkeypox virus (MPXV) infects diverse human cell types, and human immunodeficiency virus (HIV) co-infection is common. The immunometabolic consequences of MPXV infection, and how it may be altered by HIV, remain poorly defined. METHODS: We performed quantitative plasma lipidomics and precise metabolomics in a discovery cohort (n = 81) comprising MPXV-monoinfected (MPLWOH), MPXV-HIV-coinfected (MPLWH), and HIV-monoinfected (PLWH) patients and healthy controls, integrating exosome proteomics, cytokine profiling, and transcriptomics of exosome-treated HepG2 and A549 cells for functional interpretation. An independent validation cohort (n = 65) was used to assess cross-cohort reproducibility. FINDINGS: MPXV infection induced broad lipid remodeling, with elevations in phosphatidylserine (PS) and phosphatidylethanolamine (PE) and reductions in phosphatidylcholine (PC), lysophospholipids, cholesteryl ester (CE), and exosomal lecithin-cholesterol acyltransferase (LCAT) and lipoprotein lipase (LPL). These lipid alterations were correlated with tissue injury markers and inflammatory cytokines. The MPLWH group exhibited more severe metabolic disruption, including marked sulfatide (SL) depletion, lower cholesterol and high-density lipoprotein cholesterol (HDL-c), and extensive rewiring of lipid-cytokine associations. SL depletion in MPLWH correlated with abundances of COPI-mediated retrograde trafficking proteins in exosomes. Transcriptomic profiling of exosome-treated cells provided functional validation: MPLWOH exosomes induced lipid metabolism and repair-associated epithelial programs, while MPLWH exosomes drove phospholipid remodeling and acute inflammatory and mucosal barrier-stress responses. CONCLUSIONS: MPXV infection reprograms host lipid metabolism and exosome composition, with HIV co-infection amplifying inflammatory, metabolic, and trafficking disruptions. These convergent multi-omics signatures link systemic lipid dysregulation to exosome-mediated immunomodulation and identify potential targets for host-directed interventions. FUNDING: This study was funded by the Major Project of Guangzhou National Laboratory.

Adult↗