Biomarker Research Research Articles

ALISTER – Application for lipid stability evaluation and research

Background and aimsIn lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies. Materials and methodsData of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research. ResultsOur “Application for lipid stability evaluation & research” - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively. ConclusionALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives.

Longitudinal Muscle Biopsies Reveal Inter- and Intra-Subject Variability in Cancer Cachexia: Paving the Way for Biopsy-Guided Tailored Treatment.

In the rapidly evolving landscape of cancer cachexia research, the development and refinement of diagnostic and predictive biomarkers constitute an ongoing challenge. This study aims to introduce longitudinal muscle biopsies as a potential framework for disease monitoring and treatment. The initial feasibility and safety assessment was performed for healthy mice and rats that received two consecutive muscle biopsies. The assessment was performed by utilizing three different tools. Subsequently, the protocol was also applied in leiomyosarcoma tumor-bearing rats. Longitudinal muscle biopsies proved to be a safe and feasible technique, especially in rat models. The application of this protocol to tumor-bearing rats further affirmed its tolerability and feasibility, while microscopic evaluation of the biopsies demonstrated varying levels of muscle atrophy with or without leukocyte infiltration. In this tumor model, sequential muscle biopsies confirmed the variability of the cancer cachexia evolution among subjects and at different time-points. Despite the abundance of promising cancer cachexia data during the past decade, the full potential of muscle biopsies is not being leveraged. Sequential muscle biopsies throughout the disease course represent a feasible and safe tool that can be utilized to guide precision treatment and monitor the response in cancer cachexia research.

Advances in targeted therapy and biomarker research in thyroid cancer.

Driven by the intricacy of the illness and the need for individualized treatments, targeted therapy and biomarker research in thyroid cancer represent an important frontier in oncology. The variety of genetic changes associated with thyroid cancer demand more investigation to elucidate molecular details. This research is clinically significant since it can be used to develop customized treatment plans. A more focused approach is provided by targeted therapies, which target certain molecular targets such as mutant BRAF or RET proteins. This strategy minimizes collateral harm to healthy tissues and may also reduce adverse effects. Simultaneously, patient categorization based on molecular profiles is made possible by biomarker exploration, which allows for customized therapy regimens and maximizes therapeutic results. The benefits of targeted therapy and biomarker research go beyond their immediate clinical impact to encompass the whole cancer landscape. Comprehending the genetic underpinnings of thyroid cancer facilitates the creation of novel treatments that specifically target aberrant molecules. This advances the treatment of thyroid cancer and advances precision medicine, paving the way for the treatment of other cancers. Taken simply, more study on thyroid cancer is promising for better patient care. The concepts discovered during this investigation have the potential to completely transform the way that care is provided, bringing in a new era of personalized, precision medicine. This paradigm shift could improve the prognosis and quality of life for individuals with thyroid cancer and act as an inspiration for advances in other cancer types.

Identification of diagnostic biomarkers for osteoarthritis through bioinformatics and machine learning

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by cartilage degradation, inflammatory arthritis, and joint dysfunction. Currently, there is a lack of effective early diagnostic methods and treatment strategies for OA. Bioinformatics and biomarker research provide new possibilities for early detection and personalized therapy of OA. In this study, we investigated the molecular mechanisms of OA and important signaling pathways involved in disease progression through bioinformatics analysis. Firstly, using the limma package, we analyzed the differentially expressed genes (DEGs) between normal healthy samples and OA cartilage tissue samples. These DEGs were found to be primarily involved in biological processes such as extracellular matrix (ECM) binding, immune receptor activity, and cytokine activity, as well as signaling pathways including cytokine receptors, ECM-receptor interaction, and PI3K-Akt. Gene set enrichment analysis revealed that in the OA group, signaling pathways such as AMPK, B cell receptor, IL-17, and PPAR were downregulated, while calcium signaling pathway, cell adhesion molecules, ECM-receptor interaction, TGF-β signaling pathway, and Wnt signaling pathway were upregulated. Additionally, we constructed a co-expression module network using WGCNA and identified key modules associated with OA, from which we selected 7 most predictive OA characteristic genes. Among them, ANTXR1, KCNS3, SGCD, and LIN7A were correlated with the level of immune cell infiltration. This study elucidates the mechanisms underlying the development of OA and identifies potential diagnostic markers and therapeutic targets, providing important insights for early diagnosis and treatment of OA.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders.

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: aninterdisciplinary perspective.

More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMarkers in Atopic Dermatitis and Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small-scale studies through to large multicentre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important codependencies and relationships across variables and domains. We prioritize definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a particular point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses, and validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalizable to current and future research efforts.

Recent Advances in Clinical Trials in Multiple System Atrophy.

This review summarizes previous and ongoing neuroprotection trials in multiple system atrophy (MSA), a rare and fatal neurodegenerative disease characterized by parkinsonism, cerebellar, and autonomic dysfunction. It also describes the preclinical therapeutic pipeline and provides some considerations relevant to successfully conducting clinical trials in MSA, i.e., diagnosis, endpoints, and trial design. Over 30 compounds have been tested in clinical trials in MSA. While this illustrates a strong treatment pipeline, only two have reached their primary endpoint. Ongoing clinical trials primarily focus on targeting α-synuclein, the neuropathological hallmark of MSA being α-synuclein-bearing glial cytoplasmic inclusions. The mostly negative trial outcomes highlight the importance of better understanding underlying disease mechanisms and improving preclinical models. Together with efforts to refine clinical measurement tools, innovative statistical methods, and developments in biomarker research, this will enhance the design of future neuroprotection trials in MSA and the likelihood of positive outcomes.

Expression of HER2, CD34, and EBV infection in gastric cancer, do they relate?

Introduction: Gastric cancer (GC), as a multifactorial disease, exhibits a complex pathogenesis, having intra and inter-tumor heterogeneity, challenging the efficacy of current treatments. GC is a major global health problem, and better diagnostic and therapeutic strategies are needed, leading to further biomarkers research associated with innovative targeted therapies. Our aim was to study human epidermal growth factor receptor 2 (HER2), Epstein Barr virus (EBV), and CD34 expression in GC samples to understand the relationship between these markers and relate them with the clinicopathological characteristics. Methods: Thirty samples of primary carcinoma gastrectomy cases performed between February 2017 and December 2021 at the Hospital Distrital da Figueira da Foz, EPE, were studied after approval by the Ethics Committee. Immunohistochemistry assays were performed in sections of the selected tumor representative samples. Results: All samples were negative for EBV and only two GC samples were positive for HER2. A significant statistical relationship was found between gender and CD34+ stroma cells. Microvascular density and stroma CD34+ cells presented relations with clinicopathological features and a positive tendency between them. Our study was able to identify a relationship between CD34+ stroma cells and females. Conclusion: With this study, we mainly explored the potential role of CD34 as a biomarker in GC and projected possible associations of prognostic and therapeutic value toward other HER2 and EBV markers.

Considering Context-Specific microRNAs in Ischemic Stroke with Three "W": Where, When, and What.

MicroRNAs are short non-coding RNA molecules that function as critical regulators of various biological processes through negative regulation of gene expression post-transcriptionally. Recent studies have indicated that microRNAs are potential biomarkers for ischemic stroke. In this review, we first illustrate the pathogenesis of ischemic stroke and demonstrate the biogenesis and transportation of microRNAs from cells. We then discuss several promising microRNA biomarkers in ischemic stroke in a context-specific manner from three dimensions: biofluids selection for microRNA extraction (Where), the timing of sample collection after ischemic stroke onset (When), and the clinical application of the differential-expressed microRNAs during stroke pathophysiology (What). We show that microRNAs have the utilities in ischemic stroke diagnosis, risk stratification, subtype classification, prognosis prediction, and treatment response monitoring. However, there are also obstacles in microRNA biomarker research, and this review will discuss the possible ways to improve microRNA biomarkers. Overall, microRNAs have the potential to assist clinical treatment, and developing microRNA panels for clinical application is worthwhile.

The evolving management of small renal masses.

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Statistical Methods for Selective Biomarker Testing

AbstractBiomarkers are critically important tools in modern clinical diagnosis, prognosis, and classification/prediction. However, there are fiscal and analytical barriers to biomarker research. Selective Genotyping is an approach to increasing study power and efficiency where individuals with the most extreme phenotype (response) are chosen for genotyping (exposure) in order to maximize the information in the sample. In this article, we describe an analogous procedure in the biomarker testing landscape where both response and biomarker (exposure) are continuous. We propose an intuitive reverse-regression least squares estimator for the parameters relating biomarker value to response. An expression for robust standard error and corresponding confidence interval are derived. A simulation study is used to demonstrate that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met, and to compare the estimator to an alternative under a related sampling design. We illustrate application of proposed methods on data from a chronic pain clinical trial.

A Comprehensive Review on Biomarker and Its Role in Diseases

Biomarkers are essential to clinical practice. Understanding the organized definition, types, and roles of biomarkers in various clinical phases and diseases is crucial for understanding the significance of biomarkers in healthcare and medical research. This data highlights the significance of biomarkers in prognosis prediction, therapeutic decision guidance, and the advancement of personalized medicine, in addition to their role in illness diagnosis.
 The division of biomarkers into molecular, cellular, and imaging types offers a thorough summary of the various modalities that are employed in biomarker research. In addition, the segmentation according to clinical phases, which differentiates between prognostic, therapeutic, and diagnostic biomarkers, deepens the conversation by emphasizing the diverse functions that biomarkers fulfill in patient treatment.
 In the diagnosis, prognosis and treatment of a wide range of diseases, biomarkers are a component of a relatively new and perfect clinical instrument. Using biomarkers to research different aspects of diseases, develop new drugs, and track possible therapeutic intervention results has many advantages. They contribute to drug development, personalized medicine, and therapeutic interventions. The aim is to develop biomarkers with higher sensitivity and specificity to enhance decision-making and simplify the drug development process, add depth to the discussion, emphasizing the varied roles of biomarkers in patient care.

Differential Protease Specificity by Collagenase as a Novel Approach to Serum Proteomics That Includes Identification of Extracellular Matrix Proteins without Enrichment.

Circulating extracellular matrix (ECM) proteins are serological biomarkers of interest due to their association with pathologies involving disease processes such as fibrosis and cancers. In this study, we investigate the potential for serum biomarker research using differential protease specificity (DPS), leveraging alternate protease specificity as a targeting mechanism to selectively digest circulating ECM protein serum proteins. A proof-of-concept study is presented using serum from patients with cirrhotic liver or hepatocellular carcinoma. The approach uses collagenase DPS for digestion of deglycosylated serum and liquid-chromatography-trapped ion mobility-tandem mass spectrometry (LC-TIMS-MS/MS) to enhance the detection of ECM proteins in serum. It requires no sample enrichment and minimizes the albumin average precursor intensity readout to less than 1.2%. We further demonstrate the capabilities for using the method as a high-throughput matrix-assisted laser/desorption ionization mass spectrometry (MALDI-MS) assay coupled with reference library searching. A goal is to improve the depth and breadth of biofluid proteomics for noninvasive assays.

Diagnostic machine learning applications on clinical populations using functional near infrared spectroscopy: a review.

Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to the lack of robust and objective biomarkers. This review provides an overview of research on psychiatric diseases by using fNIRS and ML. Article search was carried out and 45 studies were evaluated by considering their sample sizes, used features, ML methodology, and reported accuracy. To our best knowledge, this is the first review that reports diagnostic ML applications using fNIRS. We found that there has been an increasing trend to perform ML applications on fNIRS-based biomarker research since 2010. The most studied populations are schizophrenia (n=12), attention deficit and hyperactivity disorder (n=7), and autism spectrum disorder (n=6) are the most studied populations. There is a significant negative correlation between sample size (>21) and accuracy values. Support vector machine (SVM) and deep learning (DL) approaches were the most popular classifier approaches (SVM=20) (DL=10). Eight of these studies recruited a number of participants more than 100 for classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features were used more than concentration changes in deoxy-hemoglobin (ΔHb) based ones and the most popular ΔHbO-based features were mean ΔHbO (n=11) and ΔHbO-based functional connections (n=11). Using ML on fNIRS data might be a promising approach to reveal specific biomarkers for diagnostic classification.

The diagnosis and management of chronic lung allograft dysfunction.

Chronic lung allograft dysfunction (CLAD) remains a life-threatening complication following lung transplantation. Different CLAD phenotypes have recently been defined, based on the combination of pulmonary function testing and chest computed tomography (CT) scanning and spurred renewed interests in differential diagnosis, risk factors and management of CLAD. Given their crucial importance in the differential diagnosis, we will discuss the latest development in assessing the pulmonary function and chest CT scan, but also their limitations in proper CLAD phenotyping, especially with regards to patients with baseline allograft dysfunction. Since no definitive treatment exists, it remains important to timely identify clinical risk factors, but also to assess the presence of specific patterns or biomarkers in tissue or in broncho alveolar lavage in relation to CLAD (phenotypes). We will provide a comprehensive overview of the latest advances in risk factors and biomarker research in CLAD. Lastly, we will also review novel preventive and curative treatment strategies for CLAD. Although this knowledge has significantly advanced the field of lung transplantation, more research is warranted because CLAD remains a life-threatening complication for all lung transplant recipients.

Biomarkers for Alzheimer's Disease – An Overview

AbstractAlzheimer's disease (AD) is a complex neurodegenerative disorder with a significant global impact on public health. The emergence of atypical clinical phenotypes challenges traditional diagnostic approaches, necessitating a deeper exploration of biomarkers for accurate identification. The US Food and Drug Administration (FDA) classification of biomarkers and their integration into different stages of AD provide a structured framework for their application in research and clinical settings. Within the context of AD drug development, biomarkers are essential for participant selection, target engagement evaluation, and assessment of pathological hallmarks, including Aβ and tau protein abnormalities. The incorporation of nanoparticles with a biodegradable approach introduces innovative strategies to address the complexities of AD. This paper extensively discusses biomarkers associated with synaptic dysfunction, neuroinflammation, and glial activation, recognizing their significance in elucidating disease mechanisms. Common pathologies such as synuclein and TDP‐43 further underscore the multifaceted nature of AD. Current biomarkers for AD diagnosis, encompassing cerebral spinal fluid (CSF) biomarkers and various imaging modalities, reflect the ongoing efforts to enhance early detection and monitoring. Intriguingly, novel biomarkers continue to emerge, offering promising avenues for improved understanding and intervention. Current review provides a comprehensive survey of biomarkers for AD, elucidating their diverse roles across different aspects of the disease. By highlighting their contributions to diagnosis, drug development, and mechanistic insights, this overview underscores the importance of biomarker research in the pursuit of effective AD management and treatment strategies.

Future theranostic strategies: emerging ovarian cancer biomarkers to bridge the gap between diagnosis and treatment

Ovarian cancers are a complex and heterogenic group of malignancies that are difficult to detect, diagnose and treat. Fortunately, considerable knowledge of ovarian cancer specific biomarkers has been generated, that is pertinent to the development of novel theranostic platforms by combining therapies and diagnostics. Genomic and proteomic data has been invaluable in providing critical biomolecular targets for ovarian cancer theranostic approaches. Exploitation of the wealth of biomarker research that has been conducted offers viable targets as beacons for ovarian cancer detection, diagnosis, and therapeutic targeting. These markers can be used in theranostics, a treatment strategy that combines therapy and diagnostics and is common in nuclear medicine, where radionuclides are used for both diagnosis and treatment. The development of theranostics has taken substantial focus in recent years in the battle against ovarian cancer. Yet to date only one theranostic technology has emerged in clinical practice. However, given the wealth of ovarian cancer biomarkers the field is poised to see the emergence of revolutionary disease treatment and monitoring outcomes through their incorporation into the development of theranostic strategies. The future of ovarian cancer treatment is set to enable precise diagnosis, targeted treatment, and vigilant monitoring. This review aims to assess the status of ovarian cancer diagnostic tools and biomarkers in practice, clinical development, or pre-clinical development, highlighting newly emerging theranostic applications.

Proposed Framework for Conducting Clinically Relevant Translational Biomarker Research for the Diagnosis, Prognosis and Management of Sepsis

Proposed Framework for Conducting Clinically Relevant Translational Biomarker Research for the Diagnosis, Prognosis and Management of Sepsis

Progress in research of tumor biomarkers and molecular imaging probes for gastric cancer

Progress in research of tumor biomarkers and molecular imaging probes for gastric cancer