Candidate Protein Biomarkers Research Articles

Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson’s disease

There is an urgent need to identify predictive biomarkers of Parkinson’s disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

Proteome Signature for Differential Diagnosis of Patients with Bilateral Lung Infiltrates

BackgroundThe differential diagnosis of bilateral lung infiltrates and prognosis prediction can be challenging for clinicians in the intensive care unit (ICU). We analyzed the proteome from bronchoalveolar lavage fluid (BALF) and determine their usefulness for evaluating the infectious causes and mortality associated with bilateral lung infiltrates.MethodsIn the ICU cohort, 136 patients with bilateral infiltrate on chest radiographs were selected, and bronchoscopy with bronchoalveolar lavage (BAL) was performed. Proteomics profiling of the exosomes in the BALF (n=20) was conducted to identify candidate protein biomarkers potentially associated with infection or mortality. The BAL samples (n=116) were used to measure the candidate biomarker levels.ResultsThe candidate biomarkers, CD20, CLIC4, SCFD1, and TAP1, were selected for the differential diagnosis of infection or mortality. The levels of CD20 were significantly elevated in patients with non-infectious causes compared to those with infectious causes (248.6±154.5versus177.6±150.9 ng·mL−1, p=0.014). The levels of CLIC4, SCFD1, and TAP1 did not differ between the two groups. As per the receiver operating characteristic analysis, CD20 was a significant predictor of non-infectious causes (area under curve, 0.668; 95% CI, 0.567–0.769; p=0.002; cut off value, 167.6 ng·mL−1; sensitivity, 74.1%; specificity, 63.2%). There were no significant differences in the concentrations of the biomarkers between survivors and non-survivors.ConclusionsOur results suggested that CD20 levels in BALF might be a useful biomarker for differentiating non-infectious and infectious diseases in patients with bilateral lung infiltrates.

Identification of Potential Growth-Related Proteins in Chick Vitreous during Emmetropization Using SWATH-MS and Targeted-Based Proteomics (MRMHR).

The vitreous humor (VH) is a transparent gelatin-like substance that occupies two-thirds of the eyeball and undergoes the most significant changes during eye elongation. Quantitative proteomics on the normal growth period in the VH could provide new insights into understanding its progression mechanism in the early stages of myopia. In this study, a data-independent acquisition (SWATH-MS) was combined with targeted LC-ESI-MS/MS to identify and quantify the relative protein changes in the vitreous during the normal growth period (4, 7, 14, 21 and 28 days old) in the chick model. Chicks were raised under normal growing conditions (12/12 h Dark/light cycle) for 28 days, where ocular measurements, including refractive and biometric measurements, were performed on days 4 (baseline), 7, 14, 21 and 28 (n = 6 chicks at each time point). Extracted vitreous proteins from individual animals were digested and pooled into a left eye pool and a right pool at each time point for protein analysis. The vitreous proteome for chicks was generated using an information-dependent acquisition (IDA) method by combining injections from individual time points. Using individual pool samples, SWATH-MS was employed to quantify proteins between each time point. DEPs were subsequently confirmed in separate batches of animals individually on random eyes (n = 4) using MRMHR between day 7 and day 14. Refraction and vitreous chamber depth (VCD) were found to be significantly changed (p < 0.05, n = 6 at each time point) during the period. A comprehensive vitreous protein ion library was built with 1576 non-redundant proteins (22987 distinct peptides) identified at a 1% false discovery rate (FDR). A total of 12 up-regulated and 26 down-regulated proteins were found across all time points compared to day 7 using SWATH-MS. Several DEPs, such as alpha-fetoprotein, the cadherin family group, neurocan, and reelin, involved in structural and growth-related pathways, were validated for the first time using MRMHR under this experimental condition. This study provided the first comprehensive spectral library of the vitreous for chicks during normal growth as well as a list of potential growth-related protein biomarker candidates using SWATH-MS and MRMHR during the emmetropization period.

Discovery of Biomarkers of a Recombinant Human Erythropoietin Administration to Thoroughbred Geldings by Label‐Free Proteomics

ABSTRACTErythropoiesis‐stimulating agents (ESAs) continue to be a significant threat to the integrity of human and equine sports. Besides conventional direct testing, monitoring the biomarkers associated with the effects of ESAs may provide a complementary approach via indirect detection to enhance doping control. In this study, we applied label‐free proteomics to discover plasma protein biomarkers in Thoroughbred geldings after administration with a long‐acting form of recombinant human erythropoietin (rhEPO), methoxy polyethylene glycol epoetin beta, Mircera. Increased haematocrit, haemoglobin and red blood cell (RBC) levels were evidenced as early as 4 days post‐administration in all three horses to varying extents. Tryptic peptides were obtained from plasma samples and analysed by nanoflow ultra‐high‐performance liquid chromatography–high‐resolution tandem mass spectrometry (nano‐UHPLC‐HRMSMS) using data‐independent acquisition. Differential protein abundance analysis has shortlisted seven protein biomarker candidates that showed significant changes specifically after Mircera administration in the treated but not in the control geldings, which comprised downregulation of two proteins, haptoglobin (HP) and haemopexin (HPX), and upregulation of five proteins, transferrin receptor 1 (TFR1), phospholipid transfer protein (PLTP), tenascin C (TNC), vascular cell adhesion molecule 1 (VCAM1) and galectin 3 binding protein (LGALS3BP). Multivariate analysis of plasma proteome has allowed the classification of control and treated samples. This is the first report on the discovery of plasma protein biomarkers of rhEPO administration to geldings. The results lay a foundation for applications of protein biomarkers for controlling the misuse of ESAs.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications.

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study.

Multiple novel protein biomarkers have been shown to be associated with prostate cancer risk using genetic instruments. This study aimed to externally validate the associations of 30 genetically predicted candidate proteins with prostate cancer risk using aptamer-based levels in US Black and White men in the Atherosclerosis Risk in Communities (ARIC) study. Plasma protein levels were previously measured by SomaScan® using the blood collected in 1990-1992. Among 4864 eligible participants, we ascertained 667 first primary prostate cancer cases through 2015. Hazard ratios (HRs) of prostate cancer and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression for tertiles of each protein. We adjusted for age, race, and other risk factors. Of the 30 proteins and considering a nominal p trend < 0.05, two were positively associated with prostate cancer risk-RF1ML (tertile 3 vs. 1: HR = 1.23; 95% CI 1.02-1.48; p trend = 0.037) and TPST1 (1.28, 95% CI 1.06-1.55; p trend = 0.0087); two were inversely associated-ATF6A (HR = 0.80, 95% CI 0.65-0.98; p trend = 0.028) and SPINT2 (HR = 0.74, 95% CI 0.61-0.90; p trend = 0.0025). One protein, KDEL2, which was nonlinearly associated (test-for-linearity: p < 0.01) showed a statistically significant lower risk in the second tertile (HR = 0.79, 95% CI 0.65-0.95). Of these five, four proteins-ATF6A, KDEL2, RF1ML, and TPST1-were consistent in the direction of association with the discovery studies. This study validated some pre-diagnostic protein biomarkers of the risk of prostate cancer.

Association of major candidate protein biomarkers and long-term diabetic kidney disease progression among Asians with young-onset type 2 diabetes mellitus

AimsWe aim to determine the association of seven major candidate protein biomarkers and diabetic kidney disease (DKD) progression among Asians with young-onset type 2 diabetes mellitus (T2DM). Methods824 T2DM patients (onset ≤ 40 years old) were classified as DKD progressors based on yearly estimated glomerular filtration rate (eGFR) decline of >3 ml/min/1.73 m2 or >40 % from baseline. Plasma leucine-rich α-2-glycoprotein 1 (pLRG1), tumor necrosis factor-receptor 1 (pTNF-R1), pigment epithelium-derived factor (pPEDF), urinary α-1-microglobulin (uA1M), kidney injury molecular 1 (uKIM-1), haptoglobin (uHP) and uromodulin (uUMOD) were measured using enzyme-linked immunoassays. ResultsOver 5.7 years of follow-up, 25.2 % of patients were DKD progressors. Elevated levels of pLRG1, pTNF-R1, pPEDF, uA1M, uKIM-1 and uHP were associated with DKD progression. The association between pTNF-R1 levels and DKD progression persisted after adjusting for clinical covariates (OR 1.84, 95 %CI 1.44–2.34, p < 0.001). The effects of pTNF-R1 were partially mediated through hyperglycemia (8 %) and albuminuria (10 %). Inclusion of pTNF-R1 in a clinical variable-based model improved the area under the receiver operating characteristics curve for predicting DKD progression by 0.02, from 0.72 (95 %CI 0.68–0.76) to 0.74 (95 %CI 0.70–0.78), p = 0.099. ConclusionsAmong seven major candidate proteins, pTNF-R1, partially mediated through hyperglycemia and albuminuria, robustly predicted DKD progression among Asians with young-onset T2DM.

Development of a Fit-For-Purpose Multi-Marker Panel for Early Diagnosis of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) suffers from a lack of an effective diagnostic method, which hampers improvement in patient survival. Carbohydrate antigen 19-9 (CA19-9) is the only FDA-approved blood biomarker for PDAC, yet its clinical utility is limited due to suboptimal performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics for the discovery, verification, and validation of novel biomarkers. A plethora of protein biomarker candidates for PDAC have been identified using LC-MS, yet few has successfully transitioned into clinical practice. This translational standstill is owed partly to insufficient considerations of practical needs and perspectives of clinical implementation during biomarker development pipelines, such as demonstrating the analytical robustness of proposed biomarkers which is critical for transitioning from research-grade to clinical-grade assays. Moreover, the throughput and cost-effectiveness of proposed assays ought to be considered concomitantly from the early phases of the biomarker pipelines for enhancing widespread adoption in clinical settings. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS protocol. In the discovery phase, we comprehensively surveyed putative PDAC biomarkers from both in-house data and prior studies. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation tests. We adopted a high-throughput protocol including a short gradient (<10min) and simple sample preparation (no depletion or enrichment steps). Additionally, we developed our assay using serum samples, which are usually the preferred biospecimen in clinical settings. We developed predictive models based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone in discriminating PDAC from non-PDAC controls including healthy individuals and patients with benign pancreatic diseases. A large-scale clinical validation is underway to demonstrate the clinical validity of our novel panel.

O-267 Extracellular vesicles as biomarkers of endometriosis

Abstract Study question Can extracellular vesicles serve as biomarkers of endometriosis, particularly for stages I and II, which are difficult to detect by ultrasound? Summary answer Our findings suggest the feasibility of defining a protein signature of endometriosis based on EV cargoes. What is known already Endometriosis is characterised by intraperitoneal lesions of endometrial tissue. Although ultrasound detection of severe endometriosis is possible, the diagnosis of peritoneal lesions currently relies on invasive laparoscopy, which leads to a delay of 8-10 years. We have previously shown that peritoneal fluid (PF) of women with endometriosis contains endometriosis specific EVs. Traced in peripheral blood (PB), EVs could serve as biomarkers of endometriosis, particularly for stages I and II, which are difficult to detect by ultrasound. Study design, size, duration Women 18-45 years of age undergoing laparoscopic surgery for endometriosis or unrelated conditions were invited to participate in our study (HREC 20-159A/HREC 20-882A). Following informed consent, we collected PF and PB samples from n = 101 participants (n = 47 controls, n = 54 cases). Exclusion criteria were pregnancy, malignancy, and menopause. Participants/materials, setting, methods We processed PF and PB samples by differential ultracentrifugation and validated the presence of EVs by nanoparticle tracking analysis (NTA), Western blotting and transmission electron microscopy (TEM). EVs were analysed by untargeted, label-based quantitative proteomics using Tandem Mass Tags (TMT). Data analysis was performed using Proteome Discoverer v2.4 (Thermo Fisher) followed by statistical analysis using R. Candidate biomarker proteins were tested by traditional and automated Western blotting (WES, ProteinSimple). Main results and the role of chance We ascertained the identity of EVs by TEM and NTA (mode size 121.8 ± 18.0 nm (blood, n = 5) and 155.9 ± 37.2 (PF, n = 6)), and signals for syntenin and ALIX in immunoblots as well as expression of CD9, CD63 and CD81 in capture bead-flow cytometry. Proteomic analysis identified 9145 protein groups across all sample groups, with 602 significantly regulated between comparisons (adjusted P-value &amp;lt;0.05). In PF, 533 proteins changed significantly in abundance (245 up/288 down), while in blood, only four proteins changed in abundance. We tested the presence/absence of two of these as candidate markers on PB EV protein preparations from n = 32 samples to date; our best marker showed a sensitivity of 67% and a specificity of 90%; upon exclusion of severe endometriosis cases, sensitivity improved to 80%. Limitations, reasons for caution Because of the changes in vesicle composition and amounts with the menstrual cycle, we have to group the samples by menstrual cycle phase and disease severity. This leaves relatively small numbers per group per condition. Another limitation concerns the relatively small amount of sample liquid and thus, EVs available for analysis. Wider implications of the findings Our findings suggest the feasibility of defining a protein signature of endometriosis based on EV cargoes. A non-invasive clinical test would allow for earlier diagnosis and thus treatment of endometriosis. It would also allow patients to initiate fertility treatment early. Trial registration number not applicable

Plasma GPI and PGD are associated with vascular normalization and may serve as novel prognostic biomarkers for lung adenocarcinoma: Multi-omics and multi-dimensional analysis

The aim of this study was to explore potential novel plasma protein biomarkers for lung adenocarcinoma (LUAD). A plasma proteomics analysis was carried out and candidate protein biomarkers were validated in 102 LUAD cases and 102 matched healthy controls. The same LUAD tumor tissues were detected to explore the correlation between the expression of candidate proteins in tissues and plasma and vascular normalization. A LUAD active metastasis mice model was constructed to explore the role of candidate proteins for lung metastasis. GPI and PGD were verified to be upregulated in plasma from LUAD patients, and the expression of GPI in tumor tissue was positively correlated with the expression of GPI in plasma and negatively correlated with the normalization of tumor blood vessels. Meanwhile, a negative correlation between the expression of GPI and PGD in plasma and tumor vascular normalization was discovered. In the LUAD active metastasis model, the lowest levels of vascular normalization and the highest expression of GPI and PGD were found in mice with lung metastases. This study found that GPI and PGD may be potential plasma biomarkers for LUAD, and monitoring those may infer the risk of metastasis and malignancy of the tumor. SignificantWe identified GPI and PGD as potential novel diagnostic and prognostic biomarkers for LUAD. PGD and GPI can be used as diagnostic biomarkers in combination with other available strategies to assist in the screening and diagnosis of LUAD, and as prognostic biomarkers aid in predict the risk of tumor metastasis and malignancy in patients with LUAD.

High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies.In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications.We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use.In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

Agnostic identification of plasma biomarkers for postpartum hemorrhage risk

Postpartum hemorrhage is difficult to predict, is associated with significant maternal morbidity, and is the leading cause of maternal mortality worldwide. The identification of maternal biomarkers that can predict increased postpartum hemorrhage risk would enhance clinical care and may uncover mechanisms that lead to postpartum hemorrhage. This retrospective case-control study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and postpartum hemorrhage cases. Maternal plasma samples were procured from a cohort of >60,000 participants in a single institution's perinatal repository. Postpartum hemorrhage was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours after delivery. Postpartum hemorrhage cases (n=30) were matched by maternal age and delivery mode (vaginal or cesarean) with controls (n=56). Mass spectrometry was used to identify differentially abundant proteins using integrated peptide peak areas. Statistically significant differences between groups were defined as P<.05 after controlling for multiple comparisons. By study design, cases and controls did not differ in race, ethnicity, gestational age at delivery, blood type, or predelivery platelet count. Cases had slightly but significantly lower predelivery and postdelivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 77 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<.05). Of these differentially abundant plasma proteins, most had likely liver or placental origins. Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Significantly differentially abundant proteins with the highest fold change (prostaglandin D2 synthase, periostin, and several serine protease inhibitors) did not correlate with predelivery hematocrit or hemoglobin but identified postpartum hemorrhage cases with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (0.802-0.874). Incorporating predelivery hemoglobin with these candidate proteins further improved the identification of postpartum hemorrhage cases. Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and postpartum hemorrhage cases. Several of these proteins are known to participate in biologically plausible pathways for postpartum hemorrhage risk and have potential value for predicting postpartum hemorrhage. These findings identify candidate protein biomarkers for future validation and mechanistic studies.

Novel serum protein biomarker panel for early diagnosis of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers. Late presentation of disease at the time of diagnosis is one of the major reasons for dismal prognostic outcomes for PDAC patients. Currently, there is a lack of clinical biomarkers, which can be used to diagnose PDAC patients at an early resectable stage. This study performed proteomic mass spectrometry to identify novel blood-based biomarkers for early diagnosis of PDAC. Serum specimens from 88 PDAC patients and 88 healthy controls (60 discovery cohort and 28 validation cohort) were analyzed using data independent acquisition high resolution mass spectrometry to identify candidate biomarker proteins. A total of 249 proteins were identified and quantified by the mass spectrometric analysis. Six proteins were markedly (>1.5 fold) and significantly (p < .05; q < 0.1) increased in PDAC patients compared to healthy controls in discovery cohort. Notably, four of these six proteins were significantly upregulated in an independent validation cohort. The top three upregulated proteins (i.e., Polymeric Immunoglobulin Receptor [PIGR], von Willebrand Factor [vWF], and Fibrinogen) were validated using enzyme linked immunosorbent assay, which led to selection of PIGR and vWF as a diagnostic biomarker panel for PDAC. The panel showed high ability to diagnose early stage (stage I and II) PDAC patients (area under the curve [AUC]: 0.8926), which was further improved after the addition of clinically used prognostic biomarker (Ca 19-9) to the panel (AUC: 0.9798). In conclusion, a novel serum protein biomarker panel for early diagnosis of PDAC was identified.

Abstract A111: Development and analytical validation of a multiplexed targeted mass spectrometry assay to detect ovarian cancer protein biomarkers in sera

Abstract A111: Development and analytical validation of a multiplexed targeted mass spectrometry assay to detect ovarian cancer protein biomarkers in sera

Proteomics techniques in protein biomarker discovery

AbstractProtein biomarkers represent specific biological activities and processes, so they have had a critical role in cancer diagnosis and medical care for more than 50 years. With the recent improvement in proteomics technologies, thousands of protein biomarker candidates have been developed for diverse disease states. Studies have used different types of samples for proteomics diagnosis. Samples were pretreated with appropriate techniques to increase the selectivity and sensitivity of the downstream analysis and purified to remove the contaminants. The purified samples were analyzed by several principal proteomics techniques to identify the specific protein. In this study, recent improvements in protein biomarker discovery, verification, and validation are investigated. Furthermore, the advantages, and disadvantages of conventional techniques, are discussed. Studies have used mass spectroscopy (MS) as a critical technique in the identification and quantification of candidate biomarkers. Nevertheless, after protein biomarker discovery, verification and validation have been required to reduce the false‐positive rate where there have been higher number of samples. Multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), and selected reaction monitoring (SRM), in combination with stable isotope‐labeled internal standards, have been examined as options for biomarker verification, and enzyme‐linked immunosorbent assay (ELISA) for validation.

Correlation of potential diagnostic biomarkers (circulating miRNA and protein) of bipolar II disorder

ObjectivesWe previously identified certain peripheral biomarkers of bipolar II disorder (BD-II) including circulating miRNAs (miR-7-5p, miR-142–3p, miR-221–5p, and miR-370–3p) and proteins (Matrix metallopeptidase 9 (MMP9), phenylalanyl-tRNA synthetase subunit beta (FARSB), peroxiredoxin 2 (PRDX2), carbonic anhydrase 1 (CA-1), and proprotein convertase subtilisin/kexin type 9 (PCSK9)). We try to explore the connection between these biomarkers. MethodsWe explored correlations between the peripheral levels of above circulating miRNAs and proteins in our previously collected BD-II (N = 96) patients and control (N = 115) groups. We further searched TargetScan and BioGrid websites to identify direct and indirect interactions between these protein-coding genes and circulating miRNAs. ResultsIn the BD-II group, we identified significant correlations between the miR-221–5p and CA-1 (rho = −0.323, P = 0.001), FARSB (rho = 0.251, P = 0.014), MMP-9 (rho = 0.313, P = 0.002) and PCSK9 (rho = 0.252, P = 0.014). The miR-370–3p also significantly correlated with FARSB expression (rho = 0.330, P = 0.001) and PCSK9 expression (rho = 0.221, P = 0.031) in the BD-II group. Our findings were in line with the modulating axis identified from TargetScan and BioGrid, miR-221–5p/CA-1/MMP9 and miR-370–3p/FARSB/PCSK9, suggesting their association with BD-II. ConclusionOur result supported that peripheral candidate miRNA and protein biomarkers may interact in BD-II. We concluded that miR-221–5p/CA-1/MMP9 and miR-370–3p/FARSB/PCSK9 axes might act a critical role in the pathomechanism of BD-II.

Exploring candidate biomarkers for rheumatoid arthritis through cardiovascular and cardiometabolic serum proteome profiling.

RA patients are at higher risk of cardiovascular disease, influenced by therapies. Studying their cardiovascular and cardiometabolic proteome can unveil biomarkers and insights into related biological pathways. This study included two cohorts of RA patients: newly diagnosed individuals (n=25) and those with established RA (disease duration >25 years, n=25). Both cohorts were age and sex-matched with a control group (n=25). Additionally, a longitudinal investigation was conducted on a cohort of 25 RA patients treated with methotrexate and another cohort of 25 RA patients treated with tofacitinib for 6 months. Clinical and analytical variables were recorded, and serum profiling of 184 proteins was performed using the Olink technology platform. RA patients exhibited elevated levels of 75 proteins that might be associated with cardiovascular disease. In addition, 24 proteins were increased in RA patients with established disease. Twenty proteins were commonly altered in both cohorts of RA patients. Among these, elevated levels of CTSL1, SORT1, SAA4, TNFRSF10A, ST6GAL1 and CCL18 discriminated RA patients and HDs with high specificity and sensitivity. Methotrexate treatment significantly reduced the levels of 13 proteins, while tofacitinib therapy modulated the expression of 10 proteins. These reductions were associated with a decrease in DAS28. Baseline levels of SAA4 and high levels of BNP were associated to the non-response to methotrexate. Changes in IL6 levels were specifically linked to the response to methotrexate. Regarding tofacitinib, differences in baseline levels of LOX1 and CNDP1 were noted between non-responder and responder RA patients. In addition, response to tofacitinib correlated with changes in SAA4 and TIMD4 levels. In summary, this study pinpoints molecular changes linked to cardiovascular disease in RA and proposes candidate protein biomarkers for distinguishing RA patients from healthy individuals. It also highlights how methotrexate and tofacitinib impact these proteins, with distinct alterations corresponding to each drug's response, identifying potential candidates, as SAA4, for the response to these therapies.

Plasma Protein Biomarkers Distinguish Multisystem Inflammatory Syndrome in Children From Other Pediatric Infectious and Inflammatory Diseases.

Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious hyperinflammatory complication following infection with severe acute respiratory syndrome coronavirus 2. The mechanisms underpinning the pathophysiology of MIS-C are poorly understood. Moreover, clinically distinguishing MIS-C from other childhood infectious and inflammatory conditions, such as Kawasaki disease or severe bacterial and viral infections, is challenging due to overlapping clinical and laboratory features. We aimed to determine a set of plasma protein biomarkers that could discriminate MIS-C from those other diseases. Seven candidate protein biomarkers for MIS-C were selected based on literature and from whole blood RNA sequencing data from patients with MIS-C and other diseases. Plasma concentrations of ARG1, CCL20, CD163, CORIN, CXCL9, PCSK9 and ADAMTS2 were quantified in MIS-C (n = 22), Kawasaki disease (n = 23), definite bacterial (n = 28) and viral (n = 27) disease and healthy controls (n = 8). Logistic regression models were used to determine the discriminatory ability of individual proteins and protein combinations to identify MIS-C and association with severity of illness. Plasma levels of CD163, CXCL9 and PCSK9 were significantly elevated in MIS-C with a combined area under the receiver operating characteristic curve of 85.7% (95% confidence interval: 76.6%-94.8%) for discriminating MIS-C from other childhood diseases. Lower ARG1 and CORIN plasma levels were significantly associated with severe MIS-C cases requiring inotropes, pediatric intensive care unit admission or with shock. Our findings demonstrate the feasibility of a host protein biomarker signature for MIS-C and may provide new insight into its pathophysiology.

P192 Personalizing treatment for predicting response to infliximab in children with Crohn’s disease using proteomic biomarkers

Abstract Background Identifying minimally invasive diagnostic biomarkers that predict and monitor response to TNF inhibitors would enable treatment to be personalized and increase knowledge of the biological pathways associated with response or lack thereof. Methods Children and adolescents with Crohn’s disease who started treatment with infliximab (IFX) were enrolled in a prospective, multi-center (n=6) trial to identify biomarkers that predict response to IFX. Electronic case report forms were prepared by Studytrax (Macon, GA). Subjects &amp;lt;18 years of age were diagnosed with Crohn’s disease using standard clinical/pathologic criteria. Clinical evaluation and serum samples were obtained prior to initiation of IFX, after induction (dose 4), at six months, and after one year or at the time of treatment withdrawal. There were 119 subjects enrolled and 102 subjects completed induction. Non-responders (n=15) were defined prior to the 4th infusion as having a PCDAI &amp;gt; 10, an elevated Physician Global Assessment (PGA) or requiring dose escalation or medication change. Responders at the 4th infusions were either in remission (PUCAI ≤ 10) or were described as quiescent by PGA. Using the 7k SomaScan (SomaLogic; Boulder, CO) proteomics platform, quantitative protein profiles were generated from banked pre-treatment (V1) and after the three-dose induction (V2) to identify candidate protein biomarkers predictive of therapeutic response. Associations between the 7,310 proteins detected with SomaScan and response to IFX were assessed using t test. Response predictor models were developed using linear regression with backward selection. We report our preliminary findings from a discovery cohort of 56 subjects who completed induction. Results There were 15/56 (27%) non-responders and 41/56 (73%) responders at V2 after IFX induction. SomaScan analysis of the 56 individuals identified sets of differentially expressed proteins discriminating between non-responders and responders at both V1 and V2 and also defined protein signatures that changed from V1 to V2 for responders and non-responders, respectively. Strikingly, only responders demonstrated significant decreases in pro-inflammatory pathways at V2 compared to V1, thereby correlating well with anticipated reduced inflammation in responders. A 6-protein linear regression model performed with an AUC of 0.99 for predicting IFX response at pre-treatment V1. Conclusion In a discovery cohort from a multicenter, prospective trial of children and adolescents treated with infliximab, a comprehensive proteomics platform was able to identify serum proteins that are highly associated with response to infliximab and accurately predict response prior to treatment.

Prognostic model to predict the incidence of radiographic knee osteoarthritis

ObjectiveEarly diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting...