Clinical proteomics最新文献

Background: Plasma-based high-plex proteomic profiling were performed in prostate cancer (PC) patients using the Olink® Explore Proximity Extension Assay to identify plasma proteins associated in different PC states and to explore potential prognostic biomarkers. The progressive PC states include local, organ-confined PC (local PC), metastatic hormone-sensitive PC (mHSPC) and metastatic castrate-resistant PC (mCRPC).

Methods: Plasma samples were uniformly processed from 84 PC patients (10 patients with local PC; 29 patients with mHSPC; 45 patients with mCRPC). A proteome-wide association study was performed to identify proteins differentially overexpressed in progressive cancer states. Specifically, a sequential screening approach was employed where proteins overexpressed from one disease state were assessed for overexpression in the progressive disease state. Linear regression, analysis of variance, and t-tests were used for this approach. Differentially expressed proteins (DEPs) in mCRPC were then used to construct a prognostic model for overall survival (OS) in mCRPC patients using the Cox Proportional Hazard Model. The predictive performance of this model was assessed using time-dependent area under the receiver operating characteristic curves (tAUC) in an independent sample of mCRPC patients. The tAUC of the prognostic model was then compared to that of a model excluding DEPs to evaluate the added value of circulatory proteins in predicting survival.

Results: Of 736 tumor-associated proteins, 26 were differentially expressed across local PC, mHSPC, and mCRPC states. Among these, 20 were overexpressed in metastatic states compared to local, and in mCRPC compared to mHSPC states. Of these 20 proteins, Ribonucleoside-diphosphate reductase subunit M2 (RRM2) was identified as a prognostic biomarker for OS in mCRPC, with a hazard ratio of 2.30 (95% confidence interval (CI) 1.17-4.51) per normalized expression unit increase. The tAUC of the model including previously identified clinical prognostic factors was 0.62 (95% CI 0.29-0.91), whereas the model that includes RRM2 with clinical prognostic factors was 0.87 (95% CI 0.51-0.98).

Conclusions: Plasma proteome profiling can identify novel circulatory DEPs associated with mCRPC state survivals. Overexpression of RRM2 is linked to poor mCRPC survival and its inclusion alongside conventional prognostic factors enhances the predictive performance of the prognostic model.

Background: Exosomes play important role in biological functions, including both normal and disease process. Multiple cell types can secret exosomes, which act as message carriers. Increased evidences reveal that exosomes are promising diagnosis biomarkers in malignant tumors.

Methods: In this study, we enrolled 78 participants, including 20 lung adenocarcinoma (LUAD), 18 lung squamous carcinoma (LUSC), 20 lung benign diseases (LUBN) and 20 healthy controls (NL) and we performed parallel reaction-monitoring (PRM)-mass spectrometry to screening the proteomic variation by label free analysis in exosomes from all groups, which has been widely used to quantify and detect target proteins.

Results: Total 14 protein were identified as candidate biomarkers, complement components C9, apolipoprotein B (APOB), filamin A (FLNA), guanine nucleotide binding protein G subunit 2 (GNB2), fermitin family homolog 3 (FERMT3) showed significantly differentiation in total lung cancer (LUAD and LUSC together), we then obtained combination analysis of 5 proteins and the area under the curve (AUC), sensitivity (SN) and specificity (SP) were 63.0%, 65.0%, and 75.0%, respectively, in comparison to NL group. And the LUAD combination panel, peroxiredoxin 6 (PRDX6), integrin alpha-IIb (ITGA2B) and hemoglobin subunit delta (HBD) revealed AUC was 95.0%, SN was 90.0% and SP was 95.0% in comparison to NL controls. In LUSC analysis, combination analysis of fibronectin 1 (FN1), pregnancy zone protein (PZP) and complement C1q tumor necrosis factor related protein 3 (C1QTNF3) showed that AUC was 88.1%, SN was 75.0%, SP was 100% in paralleled with NL group. Finally C9, FLNA, PZP were overexpressed in lung cancer H1299 and A549 cell lines and the results indicated that C9 acted as oncogenic role by increasing proliferation, migration and invasion of lung cancer cells, while FLNA and PZP played tumor-suppression by inhibition biological functions of lung cancer cells.

Conclusion: Taken together, our study revealed multiple exosomal proteins which could be applied as candidate biomarkers in diagnosis of lung cancer.

Background: Pulmonary nodule with diameters ranging 8-30 mm has a high occurrence rate, and distinguishing benign from malignant nodules can greatly improve the patient outcome of lung cancer. However, sensitive and specific liquid-biopsy methods have yet to achieve satisfactory clinical goals.

Methods: We enrolled three cohorts and a total of 185 patients diagnosed with benign (BE) and malignant (MA) pulmonary nodules. Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified plasma proteome from these patients. We then performed logistic regression analysis to classify benign from malignant nodules, using cohort 1 as discovery data set and cohort 2 and 3 as independent validation data sets. We also developed a targeted multi-reaction monitoring (MRM) method to measure the concentration of the selected six peptide markers in plasma samples.

Results: We quantified a total of 451 plasma proteins, with 15 up-regulated and 5 down-regulated proteins from patients diagnosed as having malignant nodules. Logistic regression identified a six-protein panel comprised of APOA4, CD14, PFN1, APOB, PLA2G7, and IGFBP2 that classifies benign and malignant nodules with improved accuracy. In cohort 1, the area under curve (AUC) of the training and testing reached 0.87 and 0.91, respectively. We achieved a sensitivity of 100%, specificity of 40%, positive predictive value (PPV) of 62.5%, and negative predictive value (NPV) of 100%. In two independent cohorts, the 6-biomarker panel showed a sensitivity, specificity, PPV, and NPV of 96.2%, 35%, 65.8%, and 87.5% respectively in cohort 2, and 91.4%, 54.2%, 74.4%, and 81.3% respectively in cohort 3. We performed a targeted LC-MS/MS method to quantify plasma concentration of the six peptides and applied logistic regression to classify benign and malignant nodules with AUC of the training and testing reached 0.758 and 0.751, respectively.

Conclusions: Our study identified a panel of plasma protein biomarkers for distinguishing benign from malignant pulmonary nodules that worth further development into a clinically valuable assay.

One of the main causes of morbidity and death in pediatric patients is sepsis. Of the 48.9 million cases of sepsis reported globally, 41.5% involve children under the age of five, with 2.9 million deaths associated with the disease. Clinicians must identify and treat patients at risk of sepsis or septic shock before late-stage organ dysfunction occurs since diagnosing sepsis in young patients is more difficult than in adult patients. As of right now, omics technologies that possess adequate diagnostic sensitivity and specificity can assist in locating biomarkers that indicate how the disease will progress clinically and how the patient will react to treatment. By identifying patients who are at a higher risk of dying or experiencing persistent organ dysfunction, risk stratification based on biomarkers generated from proteomics can enhance prognosis. A potentially helpful method for determining the proteins that serve as biomarkers for sepsis and formulating theories on the pathophysiological mechanisms behind complex sepsis symptoms is plasma proteomics.

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging.

Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions.

Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) while discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

Background: To investigate the association between admission glucose and HbA1c values and inflammatory plasma proteins in hospitalized patients with acute ST-elevation myocardial infarction (STEMI).

Methods: This analysis was based on 345 STEMI patients recorded by the population-based Myocardial Infarction Registry Augsburg between 2009 and 2013. Using the OLINK inflammatory panel, a total of 92 protein biomarkers were measured in arterial blood samples, which were obtained within the scope of cardiac catheterization immediately after admission. The associations between admission glucose and HbA1c levels and the 92 protein markers were investigated using multivariable linear regression models.

Results: Admission glucose showed significantly positive associations with the inflammatory markers IL-10, IL-8, IL-6, FGF-21, IL-7, ST1A1, MCP-1, 4E-BP1, SIRT2, STAMBP and IL-18R1 after Bonferroni adjustment. HbA1c values were only significantly associated with IL-18R1. In stratified analyses, admission glucose was not significantly associated with any plasma protein in the diabetes subgroup, while there were several protein markers that showed significantly positive associations with admission glucose in STEMI patients without known diabetes, namely IL-10, CCL20, IL-8, MCP-1 and IL-6.

Conclusions: Admission glucose in patients hospitalized due to an acute STEMI seems to be related to an inflammatory and immune-related response, expressed by an increase in inflammation-related plasma proteins in particular in non-diabetic patients with stress hyperglycemia. The present results may open new avenues for the development of biomarkers suitable as potential diagnostic or prognostic clinical markers.

Background: Hypertension is a critical public health issue worldwide. The identification of specific proteomic biomarkers in the Qatari population aims to advance personalized treatment strategies.

Methods: We conducted proteomic profiling on 778 Qatari individuals using an aptamer-based SOMAscan platform to analyze 1,305 biomarkers. Statistical analysis involved two-way ANOVA and association analyses with FDR correction, alongside pathway and gene-set enrichment analyses using Reactome and DisGeNET databases.

Results: The study identified 26 significant protein biomarkers associated with hypertension. Notably, QORL1 and BMP1 were identified as novel protein biomarkers. Enrichment analysis linked these biomarkers to critical pathways involved in vascular biology, immune system responses, and pathologies like arteriosclerosis and coronary artery disease. Correlation analyses highlighted robust interactions, particularly between QORL1 and various Apolipoprotein E isoforms, suggesting these biomarkers play pivotal roles in the molecular mechanisms underlying hypertension.

Conclusions: This research enhances our understanding of the molecular basis of hypertension in the Qatari population and supports the development of precision medicine approaches for treatment.

Background: Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor. Notwithstanding tremendous efforts having been put in multi-omics research to profile the dysregulated molecular mechanisms and cellular pathways, there is still a lack of understanding about the glycoproteomic of GBM. Glycosylation as one of the most important post-translational modifications is crucial in regulating cell proliferation and relevant oncogenic pathways.

Results: In the study, we systematically profiled N-glycoproteomics of para-cancerous and cancerous tissues from GBM patients to reveal the site-specific N-glycosylation pattern defined by intact glycopeptides. We identified and quantified 1863 distinct intact glycopeptides (IGPs) with 161 N-linked glycan compositions and 326 glycosites. There were 396 IGPs from 43 glycoproteins differed between adjacent tissues and GBM. Then, proteomic and glycoproteomic data were combined, and the normalized glycosylation alteration was calculated to determine whether the difference was attributed to the global protein levels or glycosylation. The altered glycosylation triggered by site-specific N-glycans and glycoprotein abundance, as well as glycosite heterogeneity, were demonstrated. Ultimately, an examination of the overall glycosylation levels revealed a positive contribution of sialylated or/and fucosylated glycans.

Conclusions: Overall, the dataset highlighted molecular complexity and distinct profiling at translational and post-translational levels, providing valuable information for novel therapeutic approaches and specific detection strategies.

Formalin-fixed paraffin-embedded (FFPE) tissues present an important resource for cancer proteomics. They are more readily available than fresh frozen (FF) tissues and can be stored at ambient temperature for decades. FFPE blocks are largely stable for long-term preservation of tumour histology, but the antigenicity of some proteins in FFPE sections degrades over time resulting in deteriorating performance of immunohistochemistry (IHC). It is not known whether FFPE sections that have previously been cut from blocks and used for liquid chromatography-mass spectrometry (LC-MS) analysis at a later time are affected by storage time or temperature. We determined the stability of FFPE sections stored at room temperature (RT) versus - 80 °C over 48 weeks. The stored sections were processed at different timepoints (n = 11) and compared to sections that were freshly cut from FFPE blocks at each timepoint (controls). A total of 297 sections (rat brain, kidney and liver stored at RT, - 80 °C or freshly cut) were tryptically digested and analysed on TripleTOF 6600 mass spectrometers in data-dependent acquisition (DDA) mode. Kidney and liver digests were also analysed in data-independent acquisition (DIA) mode. The number of proteins and peptides identified by DDA with ProteinPilot and some common post-translational modifications (PTMs) were unaffected by the storage time or temperature. Nine of the most common FFPE-associated modifications were quantified using DIA data and all were unaffected by storage time or temperature. Therefore, FFPE tissue sections are suitable for proteomic studies for at least 48 weeks from the time of sectioning.

Integrating functional proteomics and next-generation sequencing (NGS) offers a comprehensive approach to unraveling the molecular intricacies of breast cancer. This study investigates the functional interplay between genomic alterations and protein expression in Taiwanese breast cancer patients. By analyzing 61 breast cancer samples using tandem mass tag (TMT) labeling and mass spectrometry, coupled with whole-exome sequencing (WES) or targeted sequencing, we identified key genetic mutations and their impact on protein expression. Notably, pathogenic variants in BRCA1, BRCA2, PTEN, and PIK3CA were found to be clinically relevant, potentially guiding targeted therapy decisions. Additionally, we discovered trans correlations between specific gene alterations (FANCA, HRAS, PIK3CA, MAP2K1, JAK2) and the expression of 22 proteins, suggesting potential molecular mechanisms underlying breast cancer development and progression. These findings highlight the power of integrating proteomics and NGS to identify potential therapeutic targets and enhance personalized medicine strategies for Taiwanese breast cancer patients.