Clinical proteomics最新文献

Background: Lung adenocarcinoma (LUAD) is a common culprit of cancer-related deaths. Recent studies have revealed that succinylation-related genes (SRGs) are pivotal in cancer. However, the comprehensive characteristics and clinical significance of SRGs in LUAD occurrence are not clear. Therefore, our goal is to dig out the succinylation-related prognostic feature genes in LUAD.

Methods: We identified differentially expressed SRGs in LUAD, and established the LUAD prognostic model using analyses of multivariate, LASSO, and univariate Cox regression. Based on clinical information and riskscore, we graphed a nomogram of the prognostic model and analyzed the independent prognostic ability of the riskscore. Analyses of immune assessment, mutation frequency, and drug sensitivity were carried out on LUAD patients.

Results: A 9-gene prognostic model was successfully set up in this project. The receiver operation characteristic (ROC) curves illustrated that the model effectively predicted the risk of LUAD patients. The levels of immune infiltration and immune scores of LUAD patients in the high-risk (HR) group were greatly lower than those in the low-risk (LR) group. Furthermore, compared to the LR group, the HR group had a significantly elevated gene mutation rate. ENPP3 and SLC22A8 may respond to targeted drugs more sensitively. The low-expression groups of ENPP3 and SLC22A8 genes may have higher drug sensitivity to Nilotinib, ARRY-614, and Megestrol acetate, with lower drug resistance.

Conclusion: The above results indicated that the prognostic model established using SRGs can be a predictive marker for LUAD prognosis, offering references for LUAD treatment and evaluation.

Background: Triple-negative breast cancer (TNBC) is a breast cancer subtype with the highest recurrence rates, for which response to neoadjuvant chemotherapy (NACT) is a critical prognostic factor. Liquid biopsy (LB) is an emerging approach in the personalization of TNBC management; among the numerous circulating molecules assessable by LB, serum proteins are gaining interest, owing to their multiple roles in cancer progression and anti-cancer immune response. Here we report findings of interim analyses in the INSTIGO trial, which aims to discover circulating protein biomarkers of TNBC response to NACT.

Patients and methods: Blood samples were collected at diagnosis and at the time of post-NACT surgery from 30 non-metastatic TNBC patients. NACT consisted of standard carboplatin-paclitaxel and epirubicin-cyclophosphamide regimens. A panel of 21 proteins was quantified in serum using high-sensitivity multiplex immunoassays (Luminex MAP^® technology).

Results: Among the 24 analysable patients, 13 had pathological complete response (pCR) and 11 were without pCR. In the pCR group, mean CX3CL1, angiopoietin-2 (ANGPOI-2), CD40 and PD-L1 levels increased significantly after NACT (p < 0.001, p < 0.001, p = 0.006, and p = 0.02, respectively). In the non-pCR group, mean CXCL5 level tended to decrease after treatment (p = 0.06). When the difference in protein levels between the end and the start of NACT was measured for each patient (Δ of a given protein), ΔCX3CL1, ΔCXCL5 and ΔANGPOI-2 differed significantly between pCR and non-pCR patients (p = 0.003, p = 0.04, p = 0.04, respectively). The baseline concentrations of CCL5, IL8, TIE2, CX3CL1 and CXCL5 tended to be associated with response to NACT, with higher levels observed among the non-pCR patients; however, statistical significance was not reached.

Conclusion: Our findings highlight the potential of circulating proteins to be biomarkers of TNBC response to NACT. Pre/post-NACT changes in CX3CL1, CXCL5, CD40, ANGPOI-2 and PD-L1 levels suggest their relevance for assessing NACT efficacy. These results will be validated at completion of the INSTIGO trial.

Trial registration: ClinicalTrials.gov, identifier NCT04438681.

Purpose: Uveitis is an inflammatory ocular disease with diverse etiologies and pathogeneses. It potentially leads to significant visual impairment and socioeconomic burden. Proteomic analysis can provide insights into protein-driven mechanisms that may improve diagnosis, monitor disease progression, and identify therapeutic targets. Here, we summarize the proteomic results from studies investigating the aqueous and vitreous humor in eyes with uveitis versus non-inflammatory controls.

Methods: A comprehensive search of 15 databases was conducted on January 26, 2024. Studies were included if they performed proteomic analyses using mass spectrometry on aqueous or vitreous humor from uveitis patients. The selection, data extraction, and risk of bias assessment were performed independently by multiple reviewers, with a third reviewer consulted in case of disagreement. Six studies met the eligibility criteria, comprising 176 eyes of uveitis patients and 105 control eyes.

Results: Two proteins, complement C1q subcomponent subunit B and C1q subcomponent subunit C, were consistently upregulated in five studies, underscoring the role of complement activation in uveitis pathogenesis. Three additional proteins - alpha-2-HS-glycoprotein, apolipoprotein A-I, and alpha-1-antichymotrypsin - were upregulated in four studies, highlighting the significance of inflammatory modulation. Ceruloplasmin, an acute-phase reactant, was upregulated in four studies. Gelsolin kininogen-1, and alpha-1-antitrypsin were upregulated in three studies, indicating a pro-inflammatory shift towards increased vascular permeability and recruitment of inflammatory cells.

Conclusion: The identified proteome changes highlight central biological processes in uveitis, notably complement activation, acute-phase response, pro-inflammatory shift, and increased vascular permeability. The identified proteins can potentially support future diagnostic and therapeutic advances in uveitis.

Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) are widely used in MS-based proteomics. However, a comprehensive evaluation of their data characteristics-including protein and peptide identification, differential expression analysis, and the performance in revealing biological insights-remains lacking. In this study, we conducted a systematic comparison of DDA and DIA across three model sample types: one disease model, two drug-treated models, and their respective controls. Our analysis extended beyond conventional metrics such as total protein and peptide counts, precision, and accuracy, to include data completeness, detection of positive control markers, reproducibility, functional annotation reliability, and sources of methodological variation. The results demonstrated that DIA outperformed DDA in terms of protein identification (disease group: 7,735 vs. 5,067; drug-treated group 1: 7,987 vs. 4,605), quantitative coverage (average quantifiable protein ratio: DIA 98-99% vs. DDA 95-96%), and reproducibility (intragroup correlation coefficients: DIA > 0.98 vs. DDA 0.93-0.98). We also found DIA exhibited lower variability (intragroup CV < 10% vs. > 15% for DDA) and improved accuracy for low-abundance and housekeeping proteins. Additionally, the functional enrichment analyses further revealed DIA's superior capability in detecting pathway activation. Finally, discrepancies between DIA and DDA were primarily attributed to proteins identified with ≤ 5 peptides, the exclusion of single-peptide proteins enhanced overall data quality. Overall, this study systematically assess the overall capabilities of DDA and DIA approaches in uncovering biologically relevant findings and driving mechanistic insights within authentic pharmacological and disease models, thereby offering practical guidance for methodological choices in future research.

Background: Epilepsy is one of the most common neurological disorders in humans and in dogs. Treatment currently focuses on alleviating symptoms, and a wide range of anti-seizure medications (ASMs) is available. Still, over one-third of patients have an inadequate response to ASM. The proteome of cerebrospinal fluid (CSF)-derived extracellular vesicles (EVs) offers a potential source of biomarkers for drug-resistant epilepsy (DRE).

Methods: We utilised a spontaneous canine epilepsy model to study the proteomic content of CSF-derived EVs as a source of biomarkers for DRE. We included 37 drug-naïve dogs with recent onset epilepsy and confirmed diagnosis of idiopathic epilepsy. CSF samples were collected at the onset of epilepsy. After the first visit, ASM treatment was started in all dogs and they were followed up for at least 12 months. After the follow-up period, based on their response to ASM treatment, dogs were grouped as either drug-responsive or drug-resistant. We isolated CSF-derived EVs with ultrafiltration combined with size-exclusion chromatography and then performed proteomic analysis with liquid chromatography-tandem mass spectrometry. A comparison between the drug-responsive and drug-resistant dogs was conducted regarding clinical factors and CSF-derived EV proteomic data.

Results: Younger age at seizure onset and occurrence of cluster seizures were identified as risk factors for drug-resistance. The proteomic analysis of normalised data identified five proteins with differential abundance between the two groups: KRT4, an uncharacterised immunoglobulin-like domain-containing protein (IgDCPa), F2, DSC1b, and LOC607874. A receiver operating characteristic analysis was performed, revealing a predictive value of ≥ 0.90 for two combinations of three proteins (KRT4, IgDCPa, and F2 (area under curve (AUC) = 0.91, confidence interval (CI) = 0.78-1.00); DSC1b, F2, and IgDCPa (AUC = 0.90, CI = 0.78-1.00)).

Conclusions: Proteins with differential abundance studied here are associated with epilepsy due to their potential involvement in critical processes such as neuroprotection, inflammation, cell integrity, and immune response. The observed reduction in the abundance of these proteins in drug-resistant dogs suggests that disruptions in these processes may contribute to the severity of the condition and its resistance to treatment. Results from this pilot study warrant further study in a larger cohort.

Background: The complement system plays a crucial role in immune regulation and inflammation, contributing to intervertebral disc degeneration (IDD) pathogenesis. While tissue-specific complement activation in degenerated discs is well-documented, its systemic expression in degenerative disc disease (DDD) plasma remains unclear.

Methods: This study employed high-throughput mass spectrometry to analyze the plasma and tissue proteomes of 40 DDD patients, comprising of Modic change (MC) and non-Modic change (NMC) patients, alongside 20 healthy volunteers (HV). Only plasma and no plasma-matched tissue samples were collected from HV group.

Results: Proteomic analysis identified 707 proteins in DDD plasma and 655 in HV plasma, with 508 common. Complement and coagulation cascades were enriched, with 46 complement proteins identified. The DDD-plasma group exhibited upregulation of most complement proteins, except for C1q complex (1.38-fold) and Complement Factor D (CFD, 0.64-fold), alongside slight downregulation of vitronectin (VTN), clusterin (CLU), and complement C8G. Elevated C-reactive protein (CRP) levels were observed in DDD-plasma, indicating systemic inflammation. Correlation analysis revealed weak associations between plasma and tissue complement protein levels, suggesting potential regulatory mechanisms.

Conclusion: Our study reveals that systemic complement alterations in DDD, supporting their potential as blood-based biomarkers reflecting localized disc pathology. Further validation in larger cohorts across different disease stages is needed to explore their diagnostic and therapeutic implications. Data are available via ProteomeXchange with identifier PXD063403.

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.