Panel Of Biomarkers Research Articles

Discovery of inflammatory response-related proteins as candidate urinary biomarkers of allergic rhinitis

ObjectivesAllergic rhinitis (AR) is a pervasive condition, affecting a significant global population, often with lifelong implications. The objective of this study is to screen for inflammatory-related differential proteins in the urine of AR patients, aiming to analyze the correlation between these identified proteins and the severity of allergic rhinitis. Design and methodsTo diagnose AR, we utilized the sIgE test to measure the allergenic responses. Based on the total IgE levels, patients were segregated into two categories: AR1 (IgE>125IU/mL) and AR2 (IgE≤125IU/mL). The differential proteins related to inflammation in the urine of patients were screened using TMT labeling combined with LC–MS/MS. Subsequently, these proteins were validated through PRM-based proteomics quantification. ResultsWe found significant increases in FCGR3A, A1AT, KRT18, and IL18 in the AR group (P<0.001). PRM-based quantification results highlighted considerable differences in the concentrations of these four proteins across varying degrees of allergic rhinitis severity. A biomarker panel comprising FCGR3A, A1AT, KRT18, and IL18 was identified, with an area under the curve (AUC) value of 0.993 for the detection of AR. ConclusionsThis study provides the first comprehensive report of combined TMT labeling LC–MS/MS quantitative proteomics and PRM analysis for discovering urinary biomarkers related to inflammatory responses in AR. These findings may be instrumental in evaluating the severity of allergic rhinitis and further our understanding of the molecular mechanisms underlying AR.

Adverse cardiovascular events in patients with cancer: a biomarker-enhanced clinical risk score

Abstract Background/Introduction Continuously improving cancer-specific survival puts a growing proportion of cancer patients at risk of major adverse cardiovascular events (MACE), but reliable tools for cardiovascular risk prediction remain unavailable (1). Purpose To assess a broad panel of cardiovascular biomarkers and clinical risk factors and to provide a novel risk score for the prediction of MACE in cancer patients. Methods Among 2’192 patients with newly diagnosed or recurrent cancer, a broad panel of cardiovascular biomarkers, including NT-proBNP, P-/E-/L-selectins, ICAM-1, VCAM-1, sLOX-1, CRP, and Lp(a) were measured by blinded study personnel. Patients were followed prospectively for the occurrence of 2-year MACE and 5-year cardiovascular death (2,3). Uni- and multivariable competing risk models were fit to assess independent associations of cardiovascular risk factors and biomarkers with MACE, and a risk score was developed. Results Beyond clinical risk factors, ICAM-1, P-/E-/L-selectins and NT-proBNP levels were independently linked to 2-year MACE risk, though shape of associations differed markedly. A clinical risk score was derived, assigning +1 point for male sex, smoking, and age ≥60 years, and +2 points for ASCVD history, yielding a bootstrapped C-statistic of 0.76 (95%CI:0.71-0.81). Implementation of cardiovascular biomarkers conferred improved performance (0.83, 95%CI:0.78-0.88). A simplified model, solely informed by clinical variables and readily available NT-proBNP (+1 point for NT-proBNP≥230pg/mL), achieved similar performance (0.80, 95%CI:0.74-0.86), with a cumulative 2-year MACE incidence of 0% in low- and 11.0% in high-risk patients. Conclusions Cancer patients are at substantial MACE risk. The herein presented first-of-its-kind risk score, integrating traditional cardiovascular risk factors and biomarkers, including NT-proBNP, effectively predicts 2-year MACE and 5-year cardiovascular death.

Prognostic Indicators for Precision Treatment of Non-Small Cell Lung Carcinoma

Non-small cell lung cancer (NSCLC) has established predictive biomarkers that enable decisions on treatment regimens for many patients. However, resistance to therapy is widespread. It is therefore essential to have a panel of molecular biomarkers that may help overcome therapy resistance and prevent adverse effects of treatment. We performed in silico analysis of NSCLC prognostic indicators, separately for adenocarcinomas and squamous carcinomas, by using The Cancer Genome Atlas (TCGA) and non-TCGA data sources in cBioPortal as well as UALCAN. This review describes lung cancer biology, elaborating on the key genetic alterations and specific genes responsible for resistance to conventional treatments. Importantly, we examined the mechanisms associated with resistance to immune checkpoint inhibitors. Our analysis indicated that a robust prognostic biomarker was lacking for NSCLC, especially for squamous cell carcinomas. In this work, our screening uncovered previously unidentified prognostic gene expression indicators, namely, MYO1E, FAM83 homologs, and DKK1 for adenocarcinoma, and FGA and TRIB1 for squamous cell carcinoma. It was further observed that overexpression of these genes was associated with poor prognosis. Additionally, FAM83 homolog and TRIB1 unexpectedly harbored copy number amplifications. In conclusion, this study elucidated novel prognostic indicators for NSCLC that may serve as targets to overcome therapy resistance toward improved patient outcomes.

Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning

Cardiovascular disease (CVD) is the leading cause of mortality, disability, and healthcare costs, with a significant impact on the elderly and contributing to premature deaths across various age groups, including those below age 70. Despite decades of transformative discoveries and clinical efforts, the challenges of diagnosis, prevention, and treatment of CVD persist on a massive scale. This study aimed to unravel potential CVD-associated biomarkers and establish a machine learning model for the risk assessment of CVD. Untargeted metabolic assay with ultra-high performance liquid chromatography-tandem mass spectrometry and routine clinical biochemistry test were undertaken on the fasting venous blood specimens from 57 subjects. Four relevant clinical traits and 164 CVD-associated metabolites were identified, especially those related to glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. The machine learning model achieved from an integrated biomarker panel of palmitic amide, oleic acid, 138-pos (the 138th detected metabolomic feature in positive ion mode), phosphatidylcholine, linoleic acid, age, direct bilirubin, and inorganic phosphate, was able to improve the accuracy of CVD risk assessment up to a high satisfactory value of 0.91. The findings indicate that disorders in the metabolic processes of biological membranes and energy are significantly associated with increased risk of vascular damage in CVD patients. With machine learning methods, the pivotal metabolites and clinical biomarkers offer a promising potential for the efficient risk assessment and diagnosis of CVD.

Combined analysis of a serum mRNA/miRNA marker signature and CA 19-9 for timely and accurate diagnosis of recurrence after resection of pancreatic ductal adenocarcinoma: A prospective multicenter cohort study.

Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples. In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a "No Evidence of Disease" and a "Recurrence" group based on clinical follow-up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7-gene marker signature with CA19-9 data. The best-performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19-9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow-up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures. TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow-up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work-up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need.

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning.

Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples. We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples. We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5. We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Cardiometabolic risk assessment in transgender individuals - differential impact of sex hormones and sex chromosomes.

While transgender individuals represent a significant group seeking medical care, the differential effect of sex on cardiometabolic risk metrics is incompletely understood. Therefore, the current study aimed to characterize the impact of sex hormones and chromosomes on a contemporary panel of cardiometabolic risk biomarkers and functional cardiovascular measurements. 17 transmen and 17 transwomen were studied at baseline (T0), 4 weeks (hormonal castration, T1), and 11 months following gender-affirming hormone treatment (T12). We analyzed carotid intima-media thickness (cIMT) and arterial stiffness, lipoproteins and other metabolites comprehensively by nuclear magnetic resonance spectroscopy and HDL-mediated cholesterol efflux capacity (CEC) from macrophages. T0 to T12 comparisons informed the impact of sex hormones, comparisons of genetic XX and XY individuals at T1 the impact of sex chromosomes. Vascular function was comparable at T12 and T0; systolic blood pressure increased in transmen (p=0.002). Transmen developed a pro-atherogenic lipoprotein profile, estrogen treatment in transwomen tended to result in improvements. Several metabolites indicating increased diabetes risk including plasma glucose were changed in transmen (p=0.025), with opposite changes in transwomen (p=0.002). Interestingly, at T1 apparent diabetes risk was lower in XX compared with XY individuals (p=0.002). CEC decreased in transwomen (p<0.01), while remaining unchanged in transmen. However, in both groups the strong positive association of apoA-I with cholesterol efflux observed at T0 was lost at T12. The results are consistent with increased cardiometabolic risk in transmen, while transwomen show beneficial changes early during gender-affirming hormone therapy. Sex chromosomes have less intrinsic effects. XY individuals and transmen display an increased apparent diabetes risk. Further research of cardiometabolic risk is needed for transgender individuals.

Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study.

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R2 = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.

Hierarchical Au@Pt nanoparticle/amino benzoic acid polymer-based hybrid material for labeled and label-free detection of interleukin-6: a comparative assessment.

Interleukin-6 (IL6) is a cytokine mainly involved in inflammatory processes associated with various diseases, from rheumatoid arthritis and pathogen-caused infections to cancer, where malignant cells exhibit high proliferation and overexpression of cytokines, including IL6. Furthermore, IL6 plays a fundamental role in detecting and differentiating tumor cells, including colorectal cancer (CRC) cells. Therefore, given its range of biological activities and pathological role, IL6 determination has been claimed for the diagnosis/prognosis of immune-mediated diseases. Herein, a comparative study is presentedof labeled and label-free electrochemical immunosensors involving a hierarchical Au@Pt nanoparticle/polymer hybrid material for detecting IL6. The electrochemical immunosensors were independently coupled to the surface of screen-printed carbon electrodes (SPCEs) previously modified with polymeric layers. While in the label-free immunosensor, an anti-IL6 antibody (IL6-Ab) was covalently bound to the modified SPCE surface, in the sandwich-like amperometric immunosensor, an anti-biotinylated-IL6 antibody (B-IL6-Ab) was attached to the electrode through biotin-avidin affinity interactions. The label-free format employed a straightforward detection of IL6 by differential pulse voltammetry (DPV). The resulting electrochemical immunosensors exhibited a linear dynamic range from 50 to 750pg/mL IL6, with detection limits (LOD) of 14.4 and 6.0pg/mL for label-free and sandwich-like immunosensors, respectively. This outstanding performance makes them versatile platforms for clinical analysis of a panel of biomarkers for early diagnosis/prognosis of inflammatory processes associated with oncological diseases, among other pathologies.

Gene expression of kynurenine pathway enzymes in depression and following electroconvulsive therapy.

This study aimed to investigate changes in mRNA expression of the kynurenine pathway (KP) enzymes tryptophan 2, 3-dioxygenase (TDO), indoleamine 2, 3-dioxygenase 1 and 2 (IDO1, IDO2), kynurenine aminotransferase 1 and 2 (KAT1, KAT2), kynurenine monooxygenase (KMO) and kynureninase (KYNU) in medicated patients with depression (n = 74) compared to age- and sex-matched healthy controls (n = 55) and in patients with depression after electroconvulsive therapy (ECT). Associations with mood score (24-item Hamilton Depression Rating Scale, HAM-D24), plasma KP metabolites and selected glucocorticoid and inflammatory immune markers known to regulate KP enzyme expression were also explored. HAM-D24 was used to evaluate depression severity. Whole blood mRNA expression was assessed using quantitative real-time polymerase chain reaction. KAT1, KYNU and IDO2 were significantly reduced in patient samples compared to control samples, though results did not survive statistical adjustment for covariates or multiple comparisons. ECT did not alter KP enzyme mRNA expression. Changes in IDO1 and KMO and change in HAM-D24 score post-ECT were negatively correlated in subgroups of patients with unipolar depression (IDO1 only), psychotic depression and ECT responders and remitters. Further exploratory correlative analyses revealed altered association patterns between KP enzyme expression, KP metabolites, NR3C1 and IL-6 in depressed patients pre- and post-ECT. Further studies are warranted to determine if KP measures have sufficient sensitivity, specificity and predictive value to be integrated into stress and immune associated biomarker panels to aid patient stratification at diagnosis and in predicting treatment response to antidepressant therapy.

Plasma-based proteomic and metabolomic characterization of lung and lymph node metastases in cervical cancer patients

Metastasis is the leading cause of mortality in cervical cancer (CC), with a particular prevalence of lymph node and lung metastases. Patients with CC who have developed distant metastases typically face a poor prognosis, and there is a scarcity of non-invasive strategies for predicting CC metastasis. In this study, we utilized label-free proteomics and untargeted metabolomics to analyze plasma samples from 25 non-metastatic, 14 with lung metastasis, and 15 with lymph node metastasis CC patients. Pathway enrichment analysis revealed a shared inflammatory process between the two metastatic groups, while the central carbon metabolism in cancer showed distinct features in the lung metastasis cohort. Additionally, cholesterol metabolism, hypoxia-inducible factor 1, and ferroptosis signaling pathways were specifically altered in the lymph node metastasis group. Utilizing the receiver operating characteristic curve analysis and Random Forest algorithm, we identified two distinct biomarker panels for the prediction of lung metastasis and lymph node metastasis, respectively. The lung metastasis panel includes properdin, neural cell adhesion molecule 1, and keratin 6 A, whereas the lymph node metastasis panel consists of quiescin sulfhydryl oxidase 1, paraoxonase 1, and keratin 6 A. Each panel exhibited significant diagnostic potential, with high area under the curve (AUC) values for lung metastasis (training set: 0.989, testing set: 0.789) and lymph node metastasis (training set: 0.973, testing set: 0.900). This study conducted an integrated proteomic and metabolomic analysis to clarify the factors contributing to lung and lymph node metastases in CC and has successfully established two biomarker panels for their prediction.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

StratosPHere 2: study protocol for a response-adaptive randomised placebo-controlled phase II trial to evaluate hydroxychloroquine and phenylbutyrate in pulmonary arterial hypertension caused by mutations in BMPR2

BackgroundPulmonary arterial hypertension is a life-threatening progressive disorder characterised by high blood pressure (hypertension) in the arteries of the lungs (pulmonary artery). Although treatable, there is no known cure for this rare disorder, and its exact cause is unknown. Mutations in the bone morphogenetic protein receptor type-2 (BMPR2) are the most common genetic cause of familial pulmonary arterial hypertension. This study represents the first-ever trial of treatments aimed at directly rescuing the BMPR2 pathway, repurposing two drugs that have shown promise at restoring levels of BMPR2 signalling: hydroxychloroquine and phenylbutyrate.MethodsThis three-armed phase II precision medicine study will investigate BMPR2 target engagement and explore the efficacy of two repurposed therapies in pulmonary arterial hypertension patients with BMPR2 mutations. Patients will be stratified based on two BMPR2 mutation classes: missense and haploinsufficient mutations. Eligible subjects will be randomised to one of the three arms (two active therapy arms and a placebo arm, all plus standard of care) following a Bayesian response-adaptive design implemented independently in each stratum and updated in response to a novel panel of primary biomarkers designed to assess biological modification of the disease.DiscussionThe results of this trial will provide the first randomised evidence of the efficacy of these therapies to rescue BMPR2 function and will efficiently explore the potential for a differential response of these therapies per mutation class to address causes rather than consequences of this rare disease.Trial registrationThe study has been registered with ISRCTN (ISRCTN10304915, 22/09/2023).

Multiplexed SERS Detection of Serum Cardiac Markers Using Plasmonic Metasurfaces.

Surface-enhanced Raman spectroscopy (SERS) possesses exquisite molecular-specific properties with single-molecule sensitivity. Yet, translation of SERS into a quantitative analysis technique remains elusive owing to considerable fluctuation of the SERS intensity, which can be ascribed to the SERS uncertainty principle, a tradeoff between "reproducibility" and "enhancement". To provide a potential solution, herein, an integrated multiplexed SERS biosensing strategy is proposed, which features two distinct advantages. First, a subwavelength-structured plasmonic metasurface consisting of alternately stacked metal-dielectric pyramidal meta-atoms is fabricated and could provide simultaneously enhanced electric and magnetic fields to enable spatially extended and weakly wavelength-dependent SERS. Second, nanomechanical perturbations are harnessed to transduce signals in the form of SERS frequency shifts, which are not directly affected by the SERS uncertainty principle. By also employing 3D printing methods, a proof-of-concept study of multiplexed detection of a panel of serum cardiac biomarkers for acute myocardial infarction is provided. Success in the development of both the electric and magnetic fields-active plasmonic metasurfaces could transform future designs of SERS substrates with newly endowed functionalities, and frequency shift-based SERS multiplexing could open new opportunities to develop innovative quantitative optical techniques for applications in chemistry, biology, and medicine.

Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice

Following a large-scale radiological event, timely collection of samples from all potentially exposed individuals may be precluded, and high-throughput bioassays capable of rapid and individualized dose assessment several days post-exposure will be essential for population triage and efficient implementation of medical treatment. The objective of this work was to validate the performance of a biomarker panel of radiosensitive intracellular leukocyte proteins (ACTN1, DDB2, and FDXR) and blood cell counts (CD19+ B-cells and CD3+ T-cells) for retrospective classification of exposure and dose estimation up to 7 days post-exposure in an in-vivo C57BL/6 mouse model. Juvenile and adult C57BL/6 mice of both sexes were total body irradiated with 0, 1, 2, 3, or 4 Gy, peripheral blood was collected 1, 4, and 7-days post-exposure, and individual blood biomarkers were quantified by imaging flow cytometry. An ensemble machine learning platform was used to identify the strongest predictor variables and combine them for biodosimetry outputs. This approach generated successful exposure classification (ROC AUC = 0.94, 95% CI: 0.90–0.97) and quantitative dose reconstruction (R2 = 0.79, RMSE = 0.68 Gy, MAE = 0.53 Gy), supporting the potential utility of the proposed biomarker assay for determining exposure and received dose in an individual.

Cardiovascular Risk in Patients with Ankylosing Spondylitis.

Objectives: Ankylosing spondylitis (AS) is an autoinflammatory, chronic disease. Patients with AS are at increased risk of cardiovascular disease (CVD). The link between AS and subclinical atherosclerosis is multifactorial and still not completely understood. The aim of this study was to examine the potential associations between carotid intima-media thickness (cIMT) and different cardiometabolic biomarkers in individuals with AS. Methods: A total of 96 patients with AS were prospectively included. cIMT was measured via ultrasonography. Multiple linear regression analysis was used to find the best predictors of cIMT values. Principal component analysis (PCA) was implemented to extract factors that were further tested via binary logistic regression analysis in relation to cIMT. Results: Waist circumference (WC), low-density lipoprotein cholesterol (LDL-c), and the BASDAI score were independently correlated with cIMT in AS patients (p = 0.037, p = 0.060, and p = 0.048, respectively; adjusted R2 = 0.113). PCA extracted four panels of biomarkers, i.e., "haematology-lipid-related factor" (i.e., ferritin, haemoglobin, HDL-c, and triglycerides), "proinflammatory-prothrombotic-related factor" (i.e., platelets, neutrophils, and C-reactive protein), "LDL-c-vitamin-related factor" (i.e., vitamins D and B12, and LDL-c), and "age-glucometabolic-related factor" (i.e., age and HbA1c), in relation to higher cIMT in patients with AS. Among these four clusters, "age-glucometabolic-related factor" was an independent predictor of increased cIMT (p < 0.001). Conclusions: In addition to traditional cardiometabolic risk factors, WC and LDL-c, the disease activity score (BASDAI) is independently related to subclinical atherosclerosis in AS patients. The joint involvement of heterogeneous cardiometabolic risk factors may reflect different pathophysiological processes of subclinical atherosclerosis in patients with AS.

Changes and associations between synovial fluid and magnetic resonance imaging markers of osteoarthritis after high tibial osteotomy

BackgroundMechanobiological mechanisms of osteoarthritis (OA) are unclear. Our objectives were to explore: 1) changes in knee joint physiology using a large panel of synovial fluid biomarkers from before to one year after high tibial osteotomy (HTO) surgery, and 2) the association of changes in the synovial fluid biomarkers with the changes in MRI measures of knee effusion-synovitis and articular cartilage composition.MethodsTwenty-six patients with symptomatic knee OA and varus alignment underwent synovial fluid aspirations and 3 T MRI before and one year after medial opening wedge HTO. Cytokine and growth factor levels in synovial fluid were measured with multiplex assays. Ontology and pathway enrichment was assessed using data protein sets with gene set enrichment analysis (GSEA), and analyzed using linear mixed effects models. MRIs were analyzed for effusion-synovitis and T2 cartilage relaxation time using manual segmentations. Changes in biomarker concentrations were correlated to changes in MRI effusion-synovitis volume and articular cartilage T2 relaxation times.ResultsDecreased enrichment in Toll-like receptor and TNF-α signalling was detected one year after HTO. The leading contributors to this reduction included IL-6, TNF-α and IL-1β, whereas the highest contributors to positive enrichment were EGF, PDGF-BB and FGF-2. Effusion-synovitis volume decreased (mean [95%CI]) one year after HTO (-2811.58 [-5094.40, -528.76mm3]). Effusion-synovitis volume was moderately correlated (r [95% CI]) with decreased MMP-1 (0.44 [0.05; 0.71]), IL-7 (0.41 [0.00; 0.69]) and IL-1β (0.59 [0.25; 0.80]) and increased MIP-1β (0.47 [0.10; 0.73]). Medial tibiofemoral articular cartilage T2 relaxation time decreased (mean [95% CI]) one year after HTO (-0.33 [-2.69; 2.05]ms). Decreased T2 relaxation time was moderately correlated to decreased Flt-3L (0.61 [0.28; 0.81]), IL-10 (0.47 [0.09; 0.73]), IP-10 (0.42; 0.03–0.70) and increased MMP-9 (-0.41 [-0.7; -0.03]) and IL-18 (-0.48 [-0.73; -0.10]).ConclusionsDecreased aberrant knee mechanical loading in patients with OA is associated with decreased biological and imaging measures of inflammation (measured in synovial fluid and on MRI) and increased anabolic processes. These exploratory findings suggest that improvement in knee loading can produce long-term (one year) improvement in joint physiology.

Triple and quadruple optimization for feature selection in cancer biomarker discovery

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on prediction accuracy that neglects clinical utility and validation feasibility. We introduce three- and four-objective optimization strategies based on genetic algorithms to identify clinically actionable biomarkers in omics studies, addressing classification tasks aimed at distinguishing hard-to-differentiate cancer subtypes beyond histological analysis alone. Our hypothesis is that by optimizing more than one characteristic of cancer biomarkers, we may identify biomarkers that will enhance their success in external validation. Our objectives are to: (i) assess the biomarker panel’s accuracy using a machine learning (ML) framework; (ii) ensure the biomarkers exhibit significant fold-changes across subtypes, thereby boosting the success rate of PCR or immunohistochemistry validations; (iii) select a concise set of biomarkers to simplify the validation process and reduce clinical costs; and (iv) identify biomarkers crucial for predicting overall survival, which plays a significant role in determining the prognostic value of cancer subtypes. We implemented and applied triple and quadruple optimization algorithms to renal carcinoma gene expression data from TCGA. The study targets kidney cancer subtypes that are difficult to distinguish through histopathology methods. Selected RNA-seq biomarkers were assessed against the gold standard method, which relies solely on clinical information, and in external microarray-based validation datasets. Notably, these biomarkers achieved over 0.8 of accuracy in external validations and added significant value to survival predictions, outperforming the use of clinical data alone with a superior c-index. The provided tool also helps explore the trade-off between objectives, offering multiple solutions for clinical evaluation before proceeding to costly validation or clinical trials.

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly “Phenylalanine, tyrosine and tryptophan biosynthesis”, “Phenylalanine metabolism”, “Glycerolipid metabolism” and “Arginine and proline metabolism”. We propose a three-metabolite diagnostic panel—comprising isoleucine, TMAO, and glucose—that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

A dataset for developing proteomic tools for pathogen detection via differential cell lysis of whole blood samples

This data descriptor presents a curated dataset for pathogen detection and identification (Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans) directly from whole-blood samples. The dataset was created using differential cell lysis combined with rapid extraction, digestion, and mass spectrometry-based proteomics. Our method offers a rapid diagnostic alternative to traditional culture, enabling timely disease management, such as sepsis. Highlighting our dataset’s uniqueness, it features a three-tier structure: Spectral Libraries of Pathogens for identifying peptide peaks for putative biomarkers; Spiked pathogen in blood MS data for biomarker panel optimization through varied concentration samples; and Parallel Reaction Monitoring (PRM) data from sepsis patients for validating our biomarker panel, achieving 83.3% sensitivity within seven hours without microbial enrichment culture. This dataset serves as a comprehensive reference for bioinformatic tool development and biomarker panel proposals, advancing microbial detection, antimicrobial resistance, and epidemiological studies.