Clinical proteomics最新文献

Background: Osteoarthritis (OA) is a prevalent musculoskeletal disorder causing chronic pain and disability, particularly in older adults. It is a multifactorial disease characterized by joint degeneration, with varying pathophysiological mechanisms across different OA subtypes (knee, hip, spine, hand, etc.). This study aimed to explore the genetic mechanisms underlying various OA subtypes using a novel approach combining protein level ratios (rQTLs) with Mendelian Randomization (MR) analysis.

Method: We utilized publicly available Genome-Wide Association Study (GWAS) datasets on rQTLs as exposure variables and OA at various anatomical sites as outcome variables. The study involved conventional multi-related-SNP MR analyses, top-related-SNP MR analyses, advanced Bayesian MR analyses, sensitivity analyses and experiments to validate findings.

Results: Key findings include significant associations between specific rQTLs and hip OA, such as DNMBP/FKBP5 and MME-related ratios, indicating their potential role in disease pathogenesis. For knee OA, rQTLs like INPP1/MPI were associated with increased risk, while FABP5/PPCDC and LYN/TACC3 were associated with reduced risk. In contrast, most rQTLs showed minimal influence on spine OA, hand OA, finger OA, and thumb OA. Advanced Bayesian MR analyses, sensitivity analyses and experiments confirmed a significant causal effect of the DNMBP/FKBP5 ratio on hip OA risk.

Conclusions: This study provides new insights into the genetic and molecular mechanisms of OA subtypes, highlighting potential therapeutic targets. The integration of protein ratio GWAS with network MR offers a comprehensive approach to understanding the complex pathogenesis of OA and emphasizes the need for subtype-specific therapeutic strategies.

Background: Formalin-fixed paraffin-embedded (FFPE) tissue proteomics has emerged as a promising approach for precision medicine, offering access to vast clinical archives. Despite technological advances enabling identification of thousands of proteins from FFPE samples, no proteomic diagnostic tests based on FFPE tissues have achieved regulatory approval for clinical diagnostics, raising fundamental questions about the translational viability of this approach.

Main body: This review critically evaluates the realistic barriers preventing clinical translation of FFPE proteomics and identifies targeted applications with genuine promise for near-term implementation. We demonstrate that while comprehensive discovery-based proteomics faces insurmountable challenges including validation failure rates exceeding 90%, targeted proteomic strategies focused on specific clinical questions show substantially greater potential. Current implementation barriers extend beyond technical limitations to encompass economic constraints (5-10-fold higher costs than immunohistochemistry), regulatory uncertainties, and fundamental incompatibilities with clinical laboratory workflows. The persistent emphasis on increasingly complex analytical platforms may represent misallocated resources given unresolved standardization and validation challenges.

Conclusion: Strategic redirection toward targeted proteomic applications addressing specific diagnostic needs, rather than comprehensive molecular profiling, offers the most viable pathway for clinical translation. Success will require prioritizing applications where FFPE proteomics provides unique, actionable information that justifies its complexity and cost relative to established methodologies. We propose specific criteria for identifying high-impact applications and outline a pragmatic roadmap for achieving clinical implementation within realistic timeframes.

Cerebrospinal fluid (CSF) provides a unique window into brain pathology, yet challenges in unbiased mass-spectrometric (MS) discovery persist due to sample complexity and the need for optimized analytical workflows. Multiple laboratory workflows have been developed for CSF proteomics, each with distinct advantages for specific applications. To interrogate which laboratory workflow is most suitable for this biological matrix, we benchmarked five orthogonal sample-preparation strategies- MStern, Proteograph™ nanoparticle enrichment (Seer), N-glycopeptide capture (N-Gp), and two extracellular-vesicle (EV) fractions isolated by differential ultracentrifugation (P20- and P150-EV)- in CSF from 19 patients with central nervous system lymphoma. The protocols span a practical spectrum of input volume (6000-50 µL), hands-on time, and reagent cost, enabling informed method selection for translational applications. In total we performed 82 LC-MS/MS experiments and detected over 38,000 unique peptides and more than 3000 proteins across all modalities. Seer achieved the best proteomic depth (~ 17,000 unique peptides) across samples, followed by P20-EV (~ 9,000), MStern (~ 5,500), P150-EV (~ 5,000), and N-Gp (~ 1,000). None of the methods introduced systematic bias in peptide or protein isoelectric point or hydrophobicity, yet each selectively highlighted distinct biological niches: P20-EVs favoured mitochondrial signatures, N-Gp capture lysosomal and plasma membrane signatures and Seer enhanced nuclear representation. These findings demonstrate that no single protocol suffices for every research question; instead, workflow selection should align with sample-volume constraints, budget and biological question. Our comparative framework empowers investigators to match CSF proteomics strategies to specific neuro-oncological objectives, thereby accelerating the translation of CSF biomarkers into clinically actionable assays.

Glycosaminoglycans (GAGs) are linear polysaccharide chains that are usually linked to proteins to create proteoglycans and play an essential role in the extracellular matrix (ECM). Mucopolysaccharidoses (MPS) are a group of rare disorders that arise due to impairment in the breakdown of glycosaminoglycans (GAGs). Key technological advances in mass spectrometry (MS) have had a significant impact on the study and diagnosis of MPS, as well as its clinical management. This review summarizes the current applications of mass spectrometry-based approaches in MPS, emphasizing its role in the understanding of pathophysiological disease mechanisms, and towards improved patient care. Mass spectrometry-based proteomics and metabolomics have identified novel biomarkers and metabolic perturbations related to the pathophysiology of MPS. In addition, mass spectrometry-based glycomics analyses have been employed for the structural characterization of GAGs to reveal their heterogeneity. The sensitivity and specificity of liquid chromatography tandem mass spectrometry (LC-MS/MS) as compared to conventional methods for the quantitation of GAGs have revolutionized diagnostics. High-resolution mass spectrometers such as Orbitrap and Fourier transform ion cyclotron resonance, permit more accurate GAG characterization. Mass spectrometry has also proven valuable in monitoring patients undergoing treatment, thereby allowing the sensitive monitoring of the therapeutic efficacy of both enzyme replacement and gene therapies. Mass spectrometry has enabled improved newborn screening and multiplex assays for screening multiple MPS types. Despite the important contributions of mass spectrometry to enhance MPS research and clinical management, there still remain challenges related to long and complex sample preparation processes, lack of standardization and lack of accessibility in routine clinical settings. We envision that future initiatives will incorporate multiple omics technologies to obtain a more holistic view of the pathophysiology of MPS. Fortunately, mass spectrometry technologies and methods continue to evolve rapidly, promising further advancements in MPS diagnosis, monitoring of patients on therapy and research that should ultimately lead to improved patient outcomes and quality of life.

Background: Premature coronary artery disease (PCAD) is characterized by early onset, rapid progression, and poor prognosis, which seriously affects patients' health and quality of life. In this study, we analyzed the proteomic network and biological pathways of PCAD patients by bioinformatics methods, and mined out the key differential proteins, which provided a theoretical basis for clinical intervention.

Methods: Patients who attended the heart center of the First Affiliated Hospital of Xinjiang Medical University from January 2023 to December 2024 and completed coronary angiography were selected. According to the relevant inclusion and exclusion criteria, a total of 129 patients were included, including 69 in the PCAD group and 60 in the control group. The clinical baseline data of the patients were systematically analyzed. Plasma protein extraction, trypsin digestion and mass spectrometry were completed. The mass spectrometry data were initially separated with the help of proteomics software, and the differential proteins were functionally enriched by RStudio software. Protein interaction networks were constructed by STRING platform and core differential proteins screened were visualized using Cytoscape software (MCODE plug-in).

Results: Differences in gender, smoking, alcohol consumption, hypertension, diabetes, HDL-C, Glu, FIB, LPa, NT-pro-BNP, PCT, and IL-6 were statistically significant (P < 0.05). Sex (P = 0.009, OR = 6.782,95% CI: 1.600-28.746), FIB (P = 0.001, OR = 2.662,95% CI: 1.471-4.818), and LPa (P = 0.041, OR = 1.002,95% CI: 1.000-1.004) were independent risk factors for PCAD. A total of 348 up-regulated proteins and 92 down-regulated proteins were screened by bioinformatics analysis. The occurrence of PCAD is associated with protein synthesis, intercellular communication, molecular interactions, ribosomal metabolism, glyoxylate and dicarboxylic acid metabolic pathways. Ribosomal and translational proteins influence the development of PCAD.

Conclusion: In this study, we found that gender, FIB, and LPa are risk factors for PCAD. The analysis identified 348 up-regulated and 92 down-regulated proteins. Among them, the differentially expressed proteins DHX9, F7, APCS, and PROC were closely related to the biological process of PCAD. The screened ribosomal and translational proteins showed high-frequency associations in protein-protein interaction networks, providing potential differentially expressed proteins for a deeper understanding of the disease.

Background: This study aims to investigate the impact of high altitude (HA) hypoxia on ferroptosis in lung tissue and the evaluate the preventative effect of Dimethyl fumarate (DMF) on lung inflammation.

Methods: Proteomic analysis was performed in plasma of volunteers ascending to high altitude, lung tissue of ALI rats, co-cultured lung epithelial (BEAS-2B cells) and macrophages (THP-1 cells) under hypoxia, either individually or in co-culture setting. DMF was pre-treated with rats or BEAS-2B cells before ferroptosis indexes and inflammatory cytokines were determined. Knock-down or overexpression of SLC7A11 in BEAS-2B cell was performed to further verify the role of DMF in alleviating ferroptosis and inflammation in ALI.

Results: Proteomic analysis of human plasma, rat lung tissue and lung epithelial cells identified Differentially expressed proteins (DEPs) enriched in the ferroptosis. HA exposure increased inflammatory response and lung injury, which could be alleviated by DMF. Co-culture of two cell types lead to a more pronounced ferroptotic response in BEAS-2B cells and an elevated level of cytokine expression in THP-1 cells under hypoxia condition, which could also be ameliorated by DMF. Knockdown of SLC7A11 results in a reversal of ferroptosis and macrophage mediated inflammation, which were improved by increasing Nrf2 expression through DMF treatment.

Conclusion: This study revealed that a reciprocal regulatory relationship between ferroptosis of lung epithelial cells and macrophage-mediated inflammation was one of the critical mechanisms contributing to HA exposure triggered ALI. Furthermore, DMF could alleviates hypoxia induced ALI by upregulating Nrf2/SLC7A11 pathway, making it a potential protective agent against HA hypoxia induced ALI.

Background: Cytochrome P450 (CYP450) enzymes are essential for drug metabolism, xenobiotic detoxification, and procarcinogen activation, playing a pivotal role in both normal physiology and cancer biology. Their expression varies significantly across tissues and tumour types, reflecting the metabolic heterogeneity of cancers. Understanding these variations is critical for developing targeted therapies, optimizing drug efficacy, and minimizing toxicity. This study aimed to comprehensively profile CYP450 expression across colorectal cancer (CRC), head and neck squamous cell carcinoma (HNSCC), breast cancer, and hepatic cancer models using proteomic techniques.

Methods: We analysed various cancer models (cell lines, xenografts, and patient tissue biopsies) using gel electrophoresis coupled with liquid chromatography-mass spectrometry (GEL-LC-MS). Equal amounts of protein were separated by gel electrophoresis, and the 45-65 kDa molecular weight range was analysed on the Orbitrap Fusion Mass Spectrometer.

Results: Distinct CYP450 expression profiles were observed across cancer types. In CRC, CYP2W1 and CYP2S1 were highly expressed, while CYP1B1 and CYP2W1 were prominent in HNSCC, highlighting their potential as biomarkers and therapeutic targets. Breast cancer models predominantly expressed CYP2J2 and CYP2S1, whereas CYP3A and CYP2C subfamily members were enriched in hepatic cancer, underscoring their roles in xenobiotic metabolism and drug clearance.

Conclusion: This study provides the first comprehensive semi-quantitative proteomic map of CYP450 isoforms across multiple cancer models. The findings reveal metabolic heterogeneity and identify clinically relevant targets, offering a foundation for future functional studies and personalized therapeutic strategies.

Background: Multiple myeloma (MM), the second most prevalent hematological malignancy, carries high morbidity with variability in clinical progression among patients. This necessitates accurate risk stratification for effective therapy and life planning. While extensively genomically and transcriptomically characterized, MM remains modestly studied from a proteomic perspective. As proteomics is a closer measure of phenotype than genomic and transcriptomic assessments, addressing this gap in the literature may yield new insights into disease biology and novel biomarkers.

Methods: Herein, we applied a new sample preparation approach for mass-spectrometry based proteomics to bone marrow interstitial fluid (BMIF) from patients with MM or its precursors.

Results: We achieved deep coverage of the proteome, identifying > 11,000 protein groups (PGs) across our cohort, with an average of ~ 8900 PGs per sample. Of these, 194 PGs were significantly associated with overall survival (OS). These survival-associated PGs were enriched for those involved in coagulation, and clustering newly diagnosed MM (NDMM) based on coagulation-related proteins revealed three distinct groups characterised by globally high, medium, and low intensity of coagulation-related proteins. The group with low intensity of coagulation-related PGs had significantly reduced OS (log-rank p = 0.00078). Clustering was independent of measured clinical covariates, including chemotherapeutic regimens used, Revised International Staging System (R-ISS stage), International Normalised Ratio (INR), and age, among others.

Conclusion: Our findings support the value of fluid-based proteomic assessment of MM and suggest that coagulation-related PGs could serve as valuable novel biomarkers for risk stratification in multiple myeloma, warranting further investigation into this area.

Background: Infantile epileptic spasm syndrome (IESS) presents significant therapeutic challenges, with the molecular mechanisms underlying variable responses to adrenocorticotropic hormone (ACTH) remaining poorly understood. This study aimed to identify ACTH-specific therapeutic biomarkers in IESS patients with effective (EF) and ineffective (IEF) responses to ACTH, providing potential clues for therapeutic interventions and insights into IESS pathogenesis.

Methods: Sixty IESS patients were recruited and allocated into the EF group (n = 30) and IEF group (n = 30), alongside 40 age- and gender-matched healthy controls. Plasma samples were analyzed using data-independent acquisition (DIA) proteomics to identify differentially expressed proteins (DEPs). Functional annotation of DEPs was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Receiver operating characteristic (ROC) curve analysis was employed to construct a diagnostic biomarker model. Enzyme-linked immunosorbent assay (ELISA) validation ensured the robustness of our findings.

Results: A total of 114 proteins were identified as uniquely associated with the EF group. GO and KEGG analyses revealed DEPs in pathways related to humoral immune response regulation, phagocytosis, complement and coagulation cascades, and metabolic processes. ROC curve analysis highlighted complement component 8 beta (C8β), Plasminogen (PLG), Haptoglobin (HP), Aldolase A (ALDOA), and Collagen Type XVIII Alpha 1 (COL18A1) as potential predictive biomarkers for ACTH efficacy, each achieving an area under the curve value above 0.8. Quantitative ELISA validation confirmed higher levels of C8β and PLG, and lower levels of HP, ALDOA, and COL18A1, in the EF group compared to the IEF group, consistent with the DIA results.

Conclusions: These findings offer novel insights into the molecular mechanisms underlying ACTH response variability in IESS and propose candidate plasma protein biomarkers for predicting ACTH treatment efficacy. This study, combining DIA-MS proteomics with targeted ELISA validation in plasma from individuals with IESS, provides evidence that the identified proteins warrant further investigation as candidate biomarkers to refine therapeutic strategies and monitor patient responses.

This study, using a two-sample and two-step Mendelian randomization (MR) approach, reveals a causal relationship between specific circulating plasma proteins and osteoporosis risk, and further identifies key deCODE Genetics plasma proteins (measured in a different population and using an independent proteomic platform) mediating the effects of upstream UKB plasma proteins.Notably, proteins such as NT5C, GREM1, BOLA1, and CCL19 were found to partially mediate the effects of upstream UKB plasma proteins on bone health. These findings shed light on a multi-tiered protein regulatory network underlying osteoporosis and provide potential targets for therapeutic intervention.

Introduction: Osteoporosis is a multifactorial skeletal disorder characterized by reduced bone mineral density (BMD) and increased fracture risk. Circulating plasma proteins are emerging as potential mediators of bone metabolism, yet their causal roles and inter-protein regulatory mechanisms in osteoporosis remain unclear.

Methods: We conducted a comprehensive two-sample MR study using protein quantitative trait loci (pQTL) data from the UK Biobank Pharma Proteomics Project (UKB; n = 54,219) and deCODE Genetics (n = 35,559) to investigate the causal effects of 2,923 and 4,907 plasma proteins, respectively, on osteoporosis risk (10,461 cases, 473,264 controls from FinnGen). A two-step MR framework was further applied to assess whether deCODE plasma proteins mediated the effects of UKB proteins on osteoporosis. Causal estimates were derived using inverse variance weighted (IVW) as the primary method, with additional sensitivity analyses including MR-Egger, MR-PRESSO, and leave-one-out tests.

Results: Eighty-three UKB plasma proteins were causally associated with osteoporosis (FDR < 0.01), including known regulators (e.g.,GALNT3, IL18, IL7R) and novel candidates (e.g., NUDT2,SMOC2). Seven deCODE proteins also showed significant effects, includingGREM1, PRRG4, NT5C, and CCL19. Two-step MR analyses revealed that NT5C, BOLA1, GREM1, and CCL19 significantly mediated the effects of upstream UKB proteins on osteoporosis, with mediation proportions ranging from 3.93% to 17.95%, supporting multi-tiered protein-to-protein causal pathways.

Conclusion: This study systematically identifies circulating plasma proteins with causal effects on osteoporosis and highlights key intermediaries mediating these effects. Our findings provide novel insights into protein-mediated regulatory networks in bone metabolism and offer promising targets for future therapeutic interventions.