Clinical proteomics最新文献

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.

Background: Saliva-derived small extracellular vesicles (sEVs) are emerging as potential biomarkers for coronary artery disease (CAD). Early identification of these biomarkers is essential for effective management and improved patient outcomes. Our study aimed to isolate and characterize sEVs from saliva to identify non-invasive protein signatures in younger CAD patients.

Methods: Saliva sEVs were obtained from 20 CAD patients aged 18-65 years, and 20 healthy controls matched for age and gender. The saliva exosome or sEVs isolation was performed using differential ultracentrifugation and sucrose density gradient methods, and we characterized the sEVs using transmission electron microscopy, scanning electron microscopy, and nanoparticle tracking analysis. Western blotting was done with exosome markers including Anti-Flotilin-1, Anti-TSG-101, and Anti-CD63. Differentially expressed proteins (DEPs) were identified through label-free LC-MS/MS Orbitrap and data was analyzed using Proteome Discoverer 3.0 and statistical analysis using MetaboAnalyst 6.0. Protein-protein interaction network, gene ontology, and pathways enrichment analysis were performed.

Results: We identified 506 proteins using label-free LC-MS/MS proteomic approaches, with 18 significant DEPs. Notable upregulated proteins included mainly Cystatin-S (CST1/CST2/CST4), Protein S100, alpha-amylase, and Gelsolin (GSN), while downregulated proteins included Serum albumin (ALB) and Apolipoprotein A1 (APOA1). These proteins are linked to inflammation and salivary secretions largely.

Conclusions: For the first time, we present unique saliva sEVs protein signatures associated with CAD. Validation in larger cohorts may establish Cystatin S as a potential diagnostic biomarker for CAD.

The preservation of tissue architecture and morphology in formalin-fixed paraffin-embedded (FFPE) tissues enables spatial resolution at the cellular and sub-cellular levels. Laser capture microdissection (LCM) combined with liquid chromatography tandem mass spectrometry analysis permits collection of tissue areas with spatial context for proteome profiling from FFPE slides. In this study, we performed proteome profiling of non-diseased renal tubulointerstitial tissue in a cohort of young (< 40 years) and old (> 70 years) individuals with the goal of spatially correlating the histomorphology to the proteomic profile. To perform in-depth characterization of renal tubulointerstitium and to identify renal aging-associated proteins, a multiplexing strategy using tandem mass tags (TMT) was employed, resulting in the quantitation of 7,355 proteins. Our approach allowed for identification of proteins with low abundance such as fibrocystin and ninein-like protein. Notably, 162 solute carrier proteins from 47 solute carrier families were identified, which were enriched in proximal and distal tubule cells. Finally, we discovered a proteomic signature associated with renal aging, which includes metalloproteinase inhibitor 3, nicotinamide N-methyltransferase, matrix metallopeptidase 7, phenazine biosynthesis-like domain-containing protein and solute carrier family 23 member 1. Overall, our study demonstrates the power of LCM combined with proteomics to leverage archived FFPE tissue samples for investigating proteomic alterations in the renal tubulointerstitium with age at a high depth of proteome coverage.

Background: Systemic amyloidosis is a potentially fatal protein misfolding disorder usually underdiagnosed in low- and middle-income countries, where limited awareness and restricted access to diagnostic tools contribute to prolonged diagnostic journeys and delayed diagnoses. Accurate identification of the precursor protein is essential but remains a challenge, particularly in resource-limited settings. This study aimed to perform mass spectrometry (MS) for amyloid subtyping and to use it as the reference method to evaluate the consistency of a clinical-laboratory model (CLM) and immunohistochemistry (IHC) in determining the amyloid subtype.

Methods: In this retrospective, observational, single-center study, MS was performed on tissue biopsies from patients diagnosed with systemic amyloidosis between 2009 and 2018 at a public university hospital in Brazil. An IHC panel of four antibodies (anti-kappa, -lambda, -serum amyloid A, -transthyretin) was performed on samples with sufficient material. Review of medical records assessed the amyloid subtype determined by the clinical-laboratory model (CLM), which was based on clinical presentation, laboratory and imaging data, genetic testing, and pathological findings available at the time of the initial diagnosis.

Results: From 127 patients, 48 were excluded due to unavailable biopsies or insufficient material for MS analysis. The final cohort consisted of 79 patients, 61% male, with a median age of 61 years. Biopsies from 13 different tissues were analyzed by MS, revealing the following amyloid subtypes: AL (56%), ATTR (25%), AA (6%), AFib (3%), AH (1%). Seven cases (9%) remained inconclusive. IHC correctly subtyped amyloid in 28% of cases but failed in 66%. In 80% of patients the CLM correctly identified the amyloid subtype. However, it generated incorrect typing leading to inappropriate treatments.

Conclusion: The consistency analysis between the CLM, IHC and MS demonstrated the superiority of MS in amyloid subtyping from tissue biopsies. While the CLM failed in 20% of cases and resulted in inappropriate treatments due to false-positive results, IHC showed very limited diagnostic performance, contrasting with results from reference centers, with less than one-third of cases correctly classified. These findings reinforce the role of MS as a more accurate and cost-competitive method for amyloid subtyping in middle-income countries.

Background: The treatment of aortic valve stenosis (AVS) remains limited to aortic valve replacement (AVR). No pharmacotherapy has yet proven efficacious, and its development is challenged by sexual dimorphism. Women display extensive valve fibrosis, and men present remarkably higher valve calcification. To accelerate the development of sex-personalised therapies, deeper molecular insights are needed. Hence, we aimed to characterise AVS sexual dimorphism using proteomics.

Methods: Fifty surgically excised valves (50% women) were homogenised, and the proteins were quantified by LC-MS/MS. The influence of differentially expressed proteins (DEPs) in sexual dimorphism was appraised using bioinformatics. DEPs were validated using immunohistochemistry, qRT-PCR and ELISA, with 30 additional valves.

Results: We quantified ~ 4,000 proteins and 76 DEPs between sexes. CD163, CD74, and NADPH oxidase-2 (NOX2) were more abundant in men's valves and central in a protein-protein interaction network. Functional enrichment analysis (FEA) supported increased lipoprotein binding and macrophage activation in men's valves, confirmed by increased CD74 + cell infiltration (immunohistochemistry). Aminopeptidase N, coagulation factor XIII, and metalloreductase STEAP4 were more abundant in men's valves at the transcript and protein levels. FEA indicated a women-specific dysregulation of spliceosomal proteins that may dictate a pro-fibrotic phenotype, which was observed histologically. A higher glutathione peroxidase-1/NOX2 ratio (ELISA) was found in women, suggesting increased protection against oxidative stress.

Conclusions: Proteomics confirms sexual dimorphism in AVS. Women displayed a higher degree of fibrotic remodelling, whereas men displayed greater immune cell infiltration and were less protected from oxidation, favouring calcification. Proteomics identified putative targets for a sex-personalised AVS modulation.