Combination Of Biomarkers Research Articles

Landscape of Biomarkers and Pathologic Response in Rectal Cancer: Where We Stand?

Colorectal cancer (CRC) is a neoplasm with a high prevalence worldwide, with a multimodal treatment that includes a combination of chemotherapy, radiotherapy, and surgery in locally advanced stages with acceptable pathological complete response (pCR) rates, this has improved with the introduction of total neoadjuvant therapy (TNT) reaching pCR rates up to 37% in compare with classic neoadjuvant treatment (NAT) where pCR rates of around 20–25% are achieved. However, the patient population that benefits most from this therapy has not been determined, and there is a lack of biomarkers that can predict the course of the disease. Multiple biomarkers have been studied, ranging from hematological and molecular markers by imaging technique and combinations of them, with contradictory results that prevent their use in routine clinical practice. In this review, we evaluate the most robust prognostic biomarkers to be used in clinical practice, highlighting their advantages and disadvantages and emphasizing biomarker combinations and their predictive value.

Rationale and Design of the PE-TRACT Trial - A Multicenter Randomized Trial to Evaluate Catheter-Directed Therapy for the Treatment of Intermediate-Risk Pulmonary Embolism

The optimal management of patients with intermediate-risk pulmonary embolism (PE), who have right heart dysfunction (determined by a combination of imaging and cardiac biomarkers) but a normal blood pressure, is uncertain. These patients suffer from reduced functional capacity and a lower quality of life over the long-term, despite use of anticoagulant therapy. Catheter-directed therapy (CDT) is a promising treatment for acute PE that rapidly removes thrombus and potentially improves cardiac dysfunction. However, CDT has risk and is costly, and it is not known whether it improves long-term cardiorespiratory fitness and/or quality of life compared with anticoagulation alone. We are therefore conducting an open-label, assessor-blinded, multicenter randomized trial, the Pulmonary Embolism: Thrombus Removal with Catheter-Directed Therapy (PE-TRACT) Study, to compare CDT plus anticoagulation (CDT group) with anticoagulation alone (No-CDT group) in 500 patients with intermediate-risk PE. The primary study hypothesis is that CDT will increase the peak oxygen uptake (peak VO2) with cardiopulmonary exercise testing at 3 months and reduce New York Heart Association (NYHA) Class at 12 months compared with No-CDT. These two primary efficacy outcomes will be analyzed sequentially using a "gatekeeping" procedure; for NYHA class to be compared, peak oxygen consumption must first be shown to be significantly increased by CDT. Safety and cost-effectiveness will also be assessed. When completed, PE-TRACT will provide important evidence regarding the benefits and risks of CDT to treat intermediate-risk PE compared with anticoagulation alone.

An automated blood test for glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) to predict the absence of intracranial lesions on head CT in adult patients with mild traumatic brain injury: BRAINI, a multicentre observational study in Europe

Following mild traumatic brain injury (mTBI), elevated concentrations of brain-specific blood proteins glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) may be indicative of intracranial lesions normally detected by head CT scans. We sought to validate the performance of this combination of biomarkers at predetermined cutoff values with an automated immunoassay to predict which patients did not have intracranial lesions. This prospective, observational study was conducted in France and Spain at 16 emergency departments. Adult patients with mTBI were eligible if they had a head CT scan and gave a 10-ml blood sample within 12h of injury. GFAP and UCH-L1 serum concentrations were measured and analysed, in less than an hour time, according to predefined cutoff values of 22pg/ml and 327pg/ml, respectively. Serum concentrations of S100B protein were concomitantly determined in a subset of patients. The primary outcome measures were the sensitivity and negative predictive value (NPV) of the combined GFAP-UCH-L1 test to rule out intracranial lesions on head CT scans. gov (NCT04032509). Between August 2019 and June 2021, 1508 patients were recruited, and 1438 were included in the main analysis. Median age was 69 years (IQR 44-83). Most patients (74%) presented 3h after trauma. 179 (12.4%) patients were positive for intracranial lesions by CT. The sensitivity of the combined test was 98.3% (95% CI 95.0-99.7) and the specificity 24.9 (95% CI 22.6-27.4), with a NPV of 99.1% (95% CI 97.1-99.8). Three patients with a positive CT scan had negative biomarker test results. S100B had a sensitivity of 83.0% (95% CI 76.2-88.2) and a NPV of 94.2% (95% CI 91.6-96.0). Patients with higher biomarker values more frequently had poorer recovery at 3 months after injury. Testing for GFAP and UCH-L1, using validated cutoffs obtained with a new, fast automated immunoassay platform, accurately predicted the absence of intracranial lesions on head CT following mTBI. This study is co-funded by the European Institute of Innovation and Technology (EIT) Health, a body of the European Union (Grant nº19474). Biomarkers tests were funded by bioMérieux.

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

Platelets: The unsung heroes in the battle against tuberculosis – A comprehensive review of platelet indices as diagnostic beacons and pathophysiological players

This review explores the emerging role of platelet indices in tuberculosis (TB) as potential biomarkers for diagnosis, disease monitoring, and understanding pathogenesis. Platelet count, mean platelet volume, platelet distribution width, and plateletcrit have shown significant alterations in TB patients compared to healthy controls and those with other respiratory conditions. These changes are attributed to the inflammatory response, direct platelet activation by Mycobacterium tuberculosis, and involvement in granuloma formation. Recent studies have investigated the diagnostic and prognostic value of platelet indices in TB, including their potential for differentiating TB from other respiratory infections, monitoring treatment response, and assessing disease severity. However, challenges such as lack of specificity and measurement variability need to be addressed. The review also highlights the multifaceted role of platelets in TB pathogenesis, including immune modulation and antimicrobial functions. Future research directions include large-scale validation studies, exploration of combined biomarker panels, and investigation of platelet indices in pediatric TB and TB-diabetes comorbidity. As our understanding of platelet-TB interactions grows, these indices may contribute to more personalized and effective TB management strategies.

From biomarker discovery to combined therapies: Advancing hepatocellular carcinoma treatment strategies.

This editorial reviews advances in hepatocellular carcinoma (HCC) treatment, focusing on a triple therapy approach and biomarker discovery. Zhang et al discuss the synergistic potential of transarterial chemoembolization combined with tyrosine kinase inhibitors and PD-1 inhibitors. Meanwhile, Li et al identify protein tyrosine phosphatase non-receptor II (PTPN2) as a biomarker for poor prognosis and immune evasion in HCC. The studies highlight the importance of combined therapies and biomarkers in improving HCC treatment efficacy and patient outcomes, with PTPN2 emerging as a potential therapeutic target. This article supplements the aforementioned studies with more recent research advancements, focusing on the molecular mechanisms and clinical applications of biomarkers.

Integrated multi-omics characterization across clinically relevant subgroups of long COVID

Abstract With SARS-CoV-2 became regional epidemics, substantial amount of patients suffers from post-acute sequelae of COVID-19 (PASC, aka long COVID). Exploring the pathogenesis and especially the heterogenicity features of long COVID subgroups is of paramount importance for etiology understandings. In this study, through integrative multi-omics analyses encompassing transcriptomics, proteomics, and metabolomics, long COVID patients exhibited overall elevated MAPK pathway activation, while patients recovered from long COVID showed down-regulation of this response. Long COVID heterogenicity is described by multi-omics distinct signatures for each subgroup. Multisystemic (MULTI) symptoms subgroup is characterized by enhanced glycerophospholipid and ether lipid metabolism, Neurological (NEU) by augmented glycoprotein synthesis metabolism, Cardio cerebral (CACRB) by increased pyruvate metabolism and suppressed macrophage polarization, Musculoskeletal + Systemic (MSK + SYST) by elevated glycerophospholipid metabolism, and Cardiopulmonary (CAPM) by inhibited NF-κB signaling pathways. ABHD17A, CSNK1D, PSME4 and SYVN1 were general long COVID combination biomarkers, while CRH (MULTI), FPGT (NEU), CBX6 (CACRB) and RBBP4 (CAPM) were selected as serum-specific subgroup proteins. Our study provides commonly shared and distinct pathophysiology explanation underpinning PASC, paving the way for the future diagnosis and therapeutic interventions.

Soluble Urokinase-Type Plasminogen Activator Receptor and Inflammatory Biomarker Response with Prognostic Significance after Acute Neuronal Injury - a Prospective Cohort Study.

Aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS), and traumatic brain injury (TBI) are severe conditions impacting individuals and society. Identifying reliable prognostic biomarkers for predicting survival or recovery remains a challenge. Soluble urokinase type plasminogen activator receptor (suPAR) has gained attention as a potential prognostic biomarker in acute sepsis. This study evaluates suPAR and related neuroinflammatory biomarkers in serum for brain injury prognosis. This prospective study included 31 aSAH, 30 IS, 13 TBI, and three healthy controls (n = 77). Serum samples were collected on average 5.9days post-injury, analyzing suPAR, IL-1β, cyclophilin A, and TNFα levels using ELISA. Outcomes were assessed 90days post-injury with the modified Rankin Scale (mRS), categorized as favorable (mRS 0-2) or unfavorable (mRS 3-6). Statistical analyses included 2-tailed t-tests, Pearson's correlations, and machine learning linear discriminant analysis (LDA) for biomarker combinations. Elevated suPAR levels were found in brain injury patients compared to controls (p = 0.017). Increased suPAR correlated with unfavorable outcomes (p = 0.0018) and showed prognostic value (AUC = 0.66, p = 0.03). IL-1β levels were higher in the unfavorable group (p = 0.0015). LDA combinatory analysis resulted a fair prognostic accuracy with canonical equation = 0.775[suPAR] + 0.667[IL1-β] (AUC = 0.77, OR 0.296, sensitivity 93.1%, specificity 53.1%, p = 0.0007). No correlation was found between suPAR and CRP or infection status. Elevated suPAR levels in acute brain injury patients were associated with poorer outcomes, highlighting suPAR's potential as a prognostic biomarker across different brain injury types. Combining IL-1β with suPAR improved prognostic accuracy, supporting a multimodal biomarker approach for predicting outcomes.

Biomarkers improving genetic and metastatic disease prediction in paraganglioma: insights from a prospective study.

Identifying the risk of malignancy and genetic status in primary paraganglioma or pheochromocytoma (PPGL) is a key challenge. The aim was to assess the diagnostic accuracy of genomic, metabolomic and histopathological biomarkers for predicting metastatic and genetic status. COMETE-TACTIC is a prospective study (NCT02672020) conducted from November 2015 to March 2019 across 16 referral centers. Tumor samples and liquid biopsies from 231 consecutive patients with PPGL were collected. Germline and somatic genetic status were determined by NGS. SDHB, SDHA and CA9 immunohistochemistries were performed on tumor tissues. TERT promoter methylation was assessed by pyrosequencing. Metabolomic profile and circulating miRNAs were measured in liquid biopsies by gas chromatography MS/MS and TaqMan assay quantified by droplet digital PCR, respectively. Tumor analysis outperformed germline analysis for determining genetic status. Positive SDHA and SDHB staining combined with negative CA9 labeling indicated the absence of SDHx and VHL variants. Plasma succinate levels above 4.94µM identified SDHx mutation carriers with 65% sensitivity and 92% specificity (AUC-ROC 0.82, 95%CI 0.70-0.93). Among circulating miRNAs, miR-483-5p was the best classifier of metastatic status (AUC-ROC 0.64, 95%CI 0.52-0.77). A sum of dinucleotide methylation rate of TERT promoter CpGs above 42% predicted metastatic status (AUC-ROC 0.75, 95%CI 0.65-0.85). Multivariate analyses showed that biomarker combinations significantly predicted SDHx status (AUC-ROC 0.99, 95%CI 0.98-1.00) and metastatic potential (AUC-ROC 0.93, 95%CI 0.84-1). Circulating miR-483-5p, plasma succinate, TERT promoter methylation, and SDHB immunostaining are valuable for PPGL risk stratification. Combining biomarkers with clinical data provides excellent diagnostic accuracy for metastatic patients (AUC-ROC 0.97, 95%CI 0.93-1).

Dual-Locked Chemiluminescent Probe Enables Precise Imaging and Timely Diagnosis of Colitis via Chymotrypsin/Vanin-1 Cascade Activation.

The development of precise diagnosis and the discovery of individualized drugs go together to provide effective therapy against inflammatory bowel disease (IBD). The exploitation of the unique imaging advantages of chemiluminescent probes represents a pivotal strategy for achieving this goal. Nevertheless, the dual-locked strategy, which is believed to enhance precision, is rarely employed in the design of chemiluminescent probes. A novel dual-locked chemiluminescent probe, BPan-CL, was designed based on IBD candidate biomarkers chymotrypsin (CHT) and vanin-1. BPan-CL exhibited specific reactivity and chemiluminescence response when subjected to simultaneous stimulation of CHT and vanin-1, with a signal-to-noise ratio superior to that of the fluorescent probe with the same dual-locked mode. In both live cell and IBD mice imaging, BPan-CL demonstrated superior sensitivity compared to its single-locked counterpart, Pan-CL. In contrast to Pan-CL, BPan-CL was able to more accurately identify IBD and healthy mice by in vivo imaging and allowed for early prediction of IBD using a noninvasive fecal test. BPan-CL has identified CHT and vanin-1 as valuable combinatorial biomarkers for accurate and early IBD diagnosis. This strategy has significant potential for use in biomedical imaging and future individualized therapies.

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers.

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Evaluating the GALAD Score for Detection of Hepatocellular Carcinoma in Patients With Cirrhosis

Introduction: Early diagnosis of hepatocellular carcinoma (HCC) is crucial but challenging, and late detection limits its treatment and prognosis. We aimed to evaluate the GALAD score as a novel yet highly accurate and promising diagnostic tool for HCC. Methods: A prospective and retrospective cohort study was conducted in 196 adult patients with cirrhosis, including 102 with HCC and 94 without. The diagnostic performance of the GALAD score for HCC detection was compared with that of single biomarkers. Results: In patients with cirrhosis with HCC, the GALAD score was 2.5 (95% CI: −2.43 to 11.09), which was significantly higher than the GALAD score of −2.46 (95% CI: −6.15 to 2.04) in patients with cirrhosis without HCC (P<0.001). Patients with multiple tumors had a significantly higher GALAD score than those with a single tumor (P=0.0081). There was a moderate correlation between the GALAD score and tumor size in patients with cirrhosis (r=0.44; P<0.001). The GALAD score had an area under the receiver operating characteristic curve of 0.91, higher than that of all single biomarkers used to diagnose HCC (all P<0.001). The optimal cutoff for diagnosing HCC using the GALAD score was −0.518, achieving a sensitivity of 87.25%, specificity of 82.98%, positive predictive value of 84.62%, and negative predictive value of 84.78%. At this cutoff, the GALAD score demonstrated superior sensitivity compared with single or combined biomarkers. Conclusions: The GALAD score shows promise in detecting HCC in patients with cirrhosis. The GALAD score could be applied in clinical practice to diagnose HCC in patients with cirrhosis, and calculating the GALAD score in clinical settings may help predict tumor size and quantity before imaging results become available.

Feasibility of Prostate Apex Cancer Diagnosis Based on the Combination of Magnetic Resonance Imaging Radiomics and Biomarkers.

Traditional diagnostic methods have limitations in accurately identifying and characterising prostate apex cancer. Therefore, exploring innovative approaches such as magnetic resonance imaging (MRI) radiomics, biomarker assessments and clinical pathological features is essential to improve diagnostic accuracy. This retrospective study evaluated diagnostic data from 52 patients with prostate apex cancer and 52 healthy individuals. MRI radiomics features-including grey-level non-uniformity, co-occurrence homogeneity, first order skewness, grey level co-occurrence matrix (GLCM) correlation, wavelet-low-high-low (wavelet-LHL) energy and prostate apparent diffusion coefficient (ADC) values-were compared between the groups. Biomarker levels, including Free Prostate-Specific Antigen (fPSA), Prostate-Specific Antigen (PSA), Ratio of Free to Total Prostate-Specific Antigen (f/tPSA), Prostate Volume (PV) and Prostate-Specific Antigen Density (PSAD), were also measured and analysed. Statistical analyses included t-tests, chi-square tests, correlation analyses and receiver operating characteristic (ROC) analyses. Significant differences were observed between the healthy and cancer groups in several MRI radiomics features: Grey-level non-uniformity (57.23 ± 7.31 vs. 69.54 ± 9.84, p < 0.001), co-occurrence homogeneity (0.29 ± 0.05 vs. 0.21 ± 0.07, p < 0.001), first order skewness (2.91 ± 0.61 vs. 3.85 ± 0.71, p < 0.001), GLCM correlation (0.72 ± 0.06 vs. 0.62 ± 0.07, p < 0.001), wavelet-LHL energy (264.14 ± 30.12 vs. 311.24 ± 42.13, p < 0.001) and prostate ADC value (1.29 ± 0.25 vs. 0.98 ± 0.15 × 10-3 mm2/s, p < 0.001). Biomarker levels also differed significantly: fPSA (0.93 ± 0.50 vs. 1.97 ± 1.69 ng/mL-1, p = 0.032), PSA (6.69 ± 2.55 vs. 17.45 ± 7.85 ng/mL-1, p = 0.048), f/tPSA (0.14 ± 0.07 vs. 0.11 ± 0.07 ng/mL-1, p = 0.020), PV (42.16 ± 8.32 vs. 38.43 ± 8.92 mL, p = 0.030) and PSAD (0.17 ± 0.08 vs. 0.49 ± 0.29 µg/L/mL-1, p = 0.040). The combined model of these parameters achieved a sensitivity of 0.865, a specificity of 0.962 and an area under the curve of 0.913. The integration of MRI radiomics, biomarker assessments and clinical pathological features presents a promising approach for diagnosing prostate apex cancer.

Changes in immuno-inflammatory and antioxidant biomarkers in serum and cerebrospinal fluid of dogs with distemper

Canine distemper virus (CDV) causes a multisystemic disease with central nervous system involvement in dogs. Little is known about the role of immuno-inflammatory and redox-state biomarkers in the pathogenesis and diagnosis of naturally occurring CDV infection. Thus, the objectives of this study were: 1) to evaluate the potential differences in a profile of cytokines/chemokines, and inflammatory and redox-status biomarkers in serum between dogs with CDV-infection and healthy dogs, and 2) possible correlations between serum/blood and cerebrospinal fluid (CSF) of these biomarkers in dogs with CDV-infection.Two groups of dogs were designed: 10 with CDV-infection, and 10 healthy. A total of 13 cytokines/chemokines, 3 inflammatory (C-reactive protein [CRP], haptoglobin [Hp], and butyrylcholinesterase [BChE]) and 3 antioxidants of redox status (cupric reducing antioxidant capacity [CUPRAC], Thiol, and ferric reducing ability of plasma [FRAP]) were analyzed in serum and CSF samples. Serum IL-7, IL-8, MCP-1, CRP, Hp, FRAP and Thiol levels were higher (P < 0.05) in dogs with CDV compared to controls. There were significant (P < 0.05) correlations only in IL-6 and MCP-1 between CSF and serum.In conclusion, deregulated immune response, raised inflammation, and imbalances of redox homeostasis and antioxidant defense status may play role in the pathophysiological mechanism of neurological form of CDV-infection. The combination of clinical features and cytokine biomarkers (IL-7, IL-8 and MCP-1) might facilitate clinical diagnosis for neural involvement in dogs with CDV. Some cytokine/chemokine (IL-6 and MCP-1) in CSF are highly correlated with those of serum, indicating that serum samples could reflect the possible changes of these analytes in CSF.

Size matters: Biomolecular compositions of small and large extracellular vesicles in the urine of glioblastoma patients.

The promise of urinary extracellular vesicles (uEVs) in biomarker discovery is emerging. However, the characteristics and compositions of different uEV subpopulations across normal physiological and pathological states require rigorous explication. We recently reported proteomic signatures of small (s)-uEVs (<200nm membranous nanoparticles) and described putative biomarkers corresponding to the diagnosis, tumour burden and recurrence of the lethal adult primary brain tumour, glioblastoma. Here, we comprehensively characterise uEV populations with significantly different mean and mode particle sizes obtained by differential centrifugation at 100,000×g (100K-uEVs; smaller) and 17,000×g (17K-uEVs; larger) using Fourier-transform infrared spectroscopy and quantitative data-independent acquisition mass spectrometry. We show distinct differences in protein and lipid content, prominent protein secondary structures, and proteome distributions between uEV populations that can distinguish glioblastoma patients from healthy controls and correspond to clinically relevant tumour changes (i.e., recurrence and treatment resistance). Among the key findings is a putative seven-protein biomarker panel associated with 17K-uEVs that could distinguish all glioblastoma patients from healthy controls and accurately classify 98.2% of glioblastoma samples. These novel, significant findings demonstrate that both uEV populations offer individual and combined biomarker potential. Further research is warranted to elucidate the complete diagnostic, prognostic, and predictive capabilities of often-neglected 17K-uEV populations.

Development and validation of a tumor marker-based model for the prediction of lung cancer: an analysis of a multicenter retrospective study in Shanghai, China.

The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889-0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making.

Screening and Predictive Biomarkers for Down Syndrome Through Amniotic Fluid Metabolomics.

Down syndrome (DS) is a congenital disorder caused by the presence of an extra copy of all or part of chromosome 21. It is characterized by significant intellectual disability, distinct facial features, and growth and developmental challenges. The utilization of metabolomics to analyze specific metabolic markers in maternal amniotic fluid may provide innovative tools and screening methods for investigating the early pathophysiology of trisomy 21 at the functional level. Amniotic fluid samples were obtained via amniocentesis from 57 pregnancies with DS and 55 control pregnancies between 173/7 and 240/7weeks of gestation. The targeted metabolomics focused on 34 organic acids, 17 amino acids, and 5 acylcarnitine metabolites. The untargeted metabolomics analysis concentrated on lipid profiles and included 602 metabolites that met quality control standards. Principal Component Analysis, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and false discovery rate (FDR) adjustments were applied. MetaboAnalystR 5.0 was used to perform the metabolic pathway analysis on the identified differential metabolites. Fifty differential metabolites, including L-glutamine, eight organic acids, and 41 lipids, were significantly altered in DS based on three criteria: VIP > 1 in the OPLS-DA model, FDR-adjusted p-value < 0.05, and |log2FC|>log2(1.5) from a volcano plot of all detected metabolites. An analysis of 212 differential metabolites, selected from both targeted and untargeted approaches (VIP > 1 in the OPLS-DA model and FDR-adjusted p-value < 0.05), revealed significant changes in nine metabolic pathways. Fourteen key metabolites were identified to establish a screening model for DS, achieving an area under the curve of 1.00. Our results underscore the potential of metabolomics approaches in identifying concise and reliable biomarker combinations that demonstrate promising screening performance in DS.

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods

Background and aimsAutism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention. Materials and MethodsSequential window acquisition of all theoretical spectra-mass spectrometry (SWATH-MS)-based proteomics technology and 30 DSM-V defined ASD cases versus age- and sex-matched controls were initially evaluated, and candidate biomarkers were screened using machine learning methods. Candidate biomarkers were validated by targeted proteomics multiple reaction monitoring (MRM) analysis using an independent group of 30 ASD cases vs. controls. ResultsFifty-one differentially expressed proteins (DEPs) were identified by SWATH analysis. They were associated with the immune response, complements and coagulation cascade pathways, and apolipoprotein-related metabolic pathways. Machine learning analysis screened 10 proteins as biomarker combinations (TFRC, PPBP, APCS, ALDH1A1, CD5L, SPARC, FGG, SHBG, S100A9, and PF4V1). In the MRM analysis, four proteins (PPBP, APCS, FGG, and PF4V1) were significantly different between the groups, and their combination as a screening indicator showed high potential (AUC = 0.8087, 95 % confidence interval 0.6904–0.9252, p < 0.0001). ConclusionsOur study provides data that suggests that a few plasma proteins have potential use in screening for ASD.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

The characterization of serum proteomics and metabolomics across the cancer trajectory in chronic hepatitis B‐related liver diseases

AbstractHepatocellular carcinoma (HCC) is a deadly cancer that emerges from a continuous progression of liver cells from normal to abnormal, often following infections by hepatitis B/C viruses (HBV/HCV), liver fibrosis, and liver cirrhosis (LC), ultimately culminating in cancer. However, there is currently limited systematic molecular analysis of biomarkers at different stages of HCC progression using multi‐omics approaches. We carried out an innovative pipeline by utilizing targeted proteomics and metabolomics to identify potential biomarkers for early detection of HCC in 316 participants, including healthy adults and patients diagnosed with HBV, HCV, LC, and HCC from three independent cohorts. We first established a detailed database of candidate biomarkers for HCC containing 3059 proteins and 103 metabolites, and identified pivotal candidates implicated in the progressive trajectory of liver cancers. Through our developed DeepPRM, scheduled multiple reaction monitoring (MRM)‐targeted approach, and machine learning‐based computational pipeline, we identified an eight‐biomolecular‐based combination with an accuracy rate of 91.43% for early diagnosis of HCC, and a 12‐biomolecular‐based combination with an accuracy rate of 80.00% for detecting changes in HBV–LC progression. These two biomarker combinations significantly improved accuracy compared to traditional tumor biomarkers. Our extensive analysis provides valuable proteomic and metabolomic data resources that will contribute to a deeper understanding of liver disease progression and enhance the identification of potential therapeutic targets.