Multiple Disease Biomarkers Research Articles

Back to the Origin: Mechanisms of circRNA-Directed Regulation of Host Genes in Human Disease.

Circular RNAs (circRNAs) have been shown to be pivotal regulators in various human diseases by participating in gene splicing, acting as microRNA (miRNA) sponges, interacting with RNA-binding proteins (RBPs), and translating into short peptides. As the back-splicing products of pre-mRNAs, many circRNAs can modulate the expression of their host genes through transcriptional, post-transcriptional, translational, and post-translational control via interaction with other molecules. This review provides a detailed summary of these regulatory mechanisms based on the class of molecules that they interact with, which encompass DNA, mRNA, miRNA, and RBPs. The co-expression of circRNAs with their parental gene productions (including linear counterparts and proteins) provides potential diagnostic biomarkers for multiple diseases. Meanwhile, the different regulatory mechanisms by which circRNAs act on their host genes via interaction with other molecules constitute complex regulatory networks, which also provide noticeable clues for therapeutic strategies against diseases. Future research should explore whether these proven mechanisms can play a similar role in other types of disease and clarify further details about the cross-talk between circRNAs and host genes. In addition, the regulatory relationship between circRNAs and their host genes in circRNA circularization, degradation, and cellular localization should receive further attention.

Nanoscale Extracellular Vesicle-Enabled Liquid Biopsy: Advances and Challenges for Lung Cancer Detection.

Lung cancer is responsible for the death of over a million people worldwide every year. With its high mortality rate and exponentially growing number of new cases, lung cancer is a major threat to public health. The high mortality and poor survival rates of lung cancer patients can be attributed to its stealth progression and late diagnosis. For a long time, intrusive tissue biopsy has been considered the gold standard for lung cancer diagnosis and subtyping; however, the intrinsic limitations of tissue biopsy cannot be overlooked. In addition to being invasive and costly, it also suffers from limitations in sensitivity and specificity, is not suitable for repeated sampling, provides restricted information about the tumor and its molecular landscape, and is inaccessible in several cases. To cope with this, advancements in diagnostic technologies, such as liquid biopsy, have shown great prospects. Liquid biopsy is an innovative non-invasive approach in which cancer-related components called biomarkers are detected in body fluids, such as blood, urine, saliva and others. It offers a less invasive alternative with the potential for applications such as routine screening, predicting treatment outcomes, evaluating treatment effectiveness, detecting residual disease, or disease recurrence. A large number of research articles have indicated extracellular vesicles (EVs) as ideal biomarkers for liquid biopsy. EVs are a heterogeneous collection of membranous nanoparticles with diverse sizes, contents, and surface markers. EVs play a critical role in pathophysiological states and have gained prominence as diagnostic and prognostic biomarkers for multiple diseases, including lung cancer. In this review, we provide a detailed overview of the potential of EV-based liquid biopsy for lung cancer. Moreover, it highlights the strengths and weaknesses of various contemporary techniques for EV isolation and analysis in addition to the challenges that need to be addressed to ensure the widespread clinical application of EV-based liquid biopsies for lung cancer. In summary, EV-based liquid biopsies present interesting opportunities for the development of novel diagnostic and prognostic platforms for lung cancer, one of the most abundant cancers responsible for millions of cancer-related deaths worldwide.

A microfluidic chip with integrated plasma separation for sample-to-answer detection of multiple chronic disease biomarkers in whole blood

Point-of-care testing of multiple chronic disease biomarkers is crucial for timely intervention and management of chronic diseases. Here, a “sample-to-answer” microfluidic chip was developed for simultaneous detection of multiple chronic disease biomarkers in whole blood by integrating a plasma separation module. The whole detection process is very convenient, i.e., just add whole blood and get the results. The chip successfully achieved the simultaneous detection of total cholesterol, triglycerides, uric acid, and glucose in undiluted whole blood within 21 min, including 6 min for plasma separation and 15 min for enzymatic chromogenic reactions. Moreover, the sensitivity levels of on-chip detection of chronic disease biomarkers can also meet clinically relevant thresholds. The chip is easy to use and has significant potential to improve home self-management of chronic diseases and enhance healthcare outcomes.

Flexible and Stretchable Electrodes for In Vivo Electrophysiological and Electrochemical Monitoring†

Comprehensive SummaryIn vivo monitoring of bioelectrical and biochemical signals with implanted electrodes has received great interest over the past decades. However, this faces huge challenges because of the severe mechanical mismatch between conventional rigid electrodes and soft biological tissues. In recent years, the emergence of flexible and stretchable electrodes offers seamless and conformable biological‐electronic interfaces and has demonstrated significant advantages for in vivo electrochemical and electrophysiological monitoring. This review first summarizes the strategies for electrode fabrication from the point of substrate and conductive materials. Next, recent progress in electrode functionalization for improved performance is presented. Then, the advances of flexible and stretchable electrodes in exploring bioelectrical and biochemical signals are introduced. Finally, we present some challenges and perspectives ranging from electrode fabrication to application.Key ScientistsIn 2001, a seminal work by Kipke et al. first showed flexible polyimide‐based intracortical electrode arrays.[1] This electrode was further expanded to 252‐channel using microelectromechanical systems technology by Stieglitz et al. in 2009 and achieved large‐scale cortical recordings.[2] Later, Lieber et al. created mesh electronics that allow for seamless and minimally invasive three‐dimensional interpenetration with nerve tissues, opening up unique applications for flexible electronics.[3] Subsequently, Rogers et al. described bioresorbable electronics for transient electrical activity recordings in 2016.[4] And Frank et al. proposed polymer electrode arrays capable of resolving single neurons in 2019.[5] It wasn't until 2020 that a significant breakthrough in biochemical signals monitoring by Peng et al. demonstrated functionalized carbon nanotube fibre bundles for multiple disease biomarkers monitoring.[6] Later on, Mooney et al. established the first fully viscoelastic electrode arrays for neural recordings from the brain and heart in 2021.[7] Recently, Bao et al. presented tissue‐mimicking, stretchable neurotransmitter interfaces for monitoring the brain and gut.[8]

Klotho in Cancer: Potential Diagnostic and Prognostic Applications

Klotho proteins, αKlotho, βKlotho, and γKlotho, exert tumor-suppressive activities via the fibroblast growth factor receptors and multiple cell-signaling pathways. There is a growing interest in Klotho proteins as potential diagnostic and prognostic biomarkers for multiple diseases. However, recent advances regarding their roles and potential applications in cancer remain disperse and require an integrated analysis. The present review analyzed research articles published between 2012 and 2022 in the Cochrane and Scopus scientific databases to study the role of Klotho in cancer and their potential as tools for diagnosing specific cancer types, predicting tumor aggressiveness and prognosis. Twenty-six articles were selected, dealing with acute myeloid leukemia and with bladder, breast, colorectal, esophageal, gastric, hepatocellular, ovarian, pancreatic, prostatic, pulmonary, renal, and thyroid cancers. αKlotho was consistently associated with improved prognosis and may be useful in estimating patient survival. A single study reported the use of soluble αKlotho levels in blood serum as a tool to aid the diagnosis of esophageal cancer. γKlotho was associated with increased aggressiveness of bladder, breast, and prostate cancer, and βKlotho showed mixed results. Further clinical development of Klotho-based assays will require careful identification of specific tumor subtypes where Klotho proteins may be most valuable as diagnostic or prognostic tools.

Point-of-care testing of four chronic disease biomarkers in blood based on a low cost and low system complexity microfluidic chip with integrated oxygen-sensitive membrane

Point-of-care testing (POCT) of multiple chronic disease biomarkers is crucial for effective prevention and management of chronic diseases. Here, we developed a sensing platform based on microfluidics chip and oxygen-sensitive membrane (OSM), which utilized enzymatic oxygen consumption and OSM based signal conversion for portable detection of glucose, total cholesterol, uric acid, and triglyceride in blood. Their detection limits were 0.04 mM, 0.07 mM, 0.01 mM, and 0.05 mM, respectively, meeting clinical requirements. To avoid the reliance on laboratory equipment, a handheld mini centrifuge modified from a toy fan was developed for plasma separation and a smartphone was used for signal acquisition. In addition, signal conversion based OSM simplified reagent composition. Dry storage of reagents on the chip simplified operations and improved the stability of reagents. For chronic disease biomarkers in blood, the results obtained by our chips were consistent with commercial kits. We believe that this sensitive, low-cost, equipment-free sensing platform has a significant potential in self-management of chronic diseases.

Immune responses and disease biomarker long-term changes following COVID-19 mRNA vaccination in a cohort of rheumatic disease patients.

The longitudinal responses towards multiple doses of COVID-19 mRNA vaccines in patients with systemic autoimmune diseases remain incompletely understood. While observational studies suggested the safety of COVID-19 mRNA vaccines in rheumatic disease patients, laboratory evidence is lacking. Here we evaluated seroreactivity, clinical manifestions, and multiple disease biomarkers after 2 or 3 doses of COVID-19 mRNA vaccines in a cohort of patients with rheumatic diseases. Most patients generated high SARS-CoV-2 spike-specific neutralizing antibodies comparable to those in healthy controls after 2 doses of mRNA vaccines. The antibody level declined over time but recovered after the third dose of the vaccine. Patients with systemic lupus erythematosus (SLE) or psoriatic arthritis (PsA) remained without significant flares post-vaccination. The changes in anti-dsDNA antibody concentration and expression of type I interferon (IFN) signature genes were highly variable but did not show consistent or significant increases. Frequency of double negative 2 (DN2) B cells remained largely stable. Our data provide experimental evidences indicating the efficacy and safety of repeated COVID-19 mRNA vaccination in rheumatic disease patients.

Genetically instrumented LDL-cholesterol lowering and multiple disease outcomes: A Mendelian randomization phenome-wide association study in the UK Biobank.

Lipid-lowering medications are widely used to control blood cholesterol levels and manage a range of cardiovascular and lipid disorders. We aimed to explore the possible associations between LDL lowering and multiple disease outcomes or biomarkers. We performed a Mendelian randomization phenome-wide association study (MR-PheWAS) in 337 475 UK Biobank participants to test for associations between four proposed LDL-C-lowering genetic risk scores (PCSK9, HMGCR, NPC1L1 and LDLR) and 1135 disease outcomes, with follow-up MR analyses in 52 serum, urine, imaging and clinical biomarkers. We used inverse-variance weighted MR in the main analyses and complementary MR methods (weighted median, weighted mode, MR-Egger and MR-PRESSO) as sensitivity analyses. We accounted for multiple testing with false discovery rate correction (P < 2.0 × 10-4 for phecodes, P < 1.3 × 10-2 for biomarkers). We found evidence for an association between genetically instrumented LDL lowering and 10 distinct disease outcomes, suggesting potential causality. All genetic instruments were associated with hyperlipidaemias and cardiovascular diseases in the expected directions. Biomarker analyses supported an effect of LDL-C lowering through PCSK9 on lung function (FEV [beta per 1 mg/dL lower LDL-C -1.49, 95% CI -2.21, -0.78]; FVC [-1.42, 95% CI -2.29, -0.54]) and through HMGCR on hippocampal volume (beta per 1 mg/dL lower LDL-C 6.09, 95% CI 1.74, 10.44). We found genetic evidence to support both positive and negative effects of LDL-C lowering through all four LDL-C-lowering pathways. Future studies should further explore the effects of LDL-C lowering on lung function and changes in brain volume.

Expression pattern of miR-16-2-3p and its prognostic values on pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a debilitating illness that easily occurs in adolescents. microRNAs (miRNAs) are potential biomarkers for multiple diseases. This paper was to elaborate on the expression of miR-16-2-3p in childhood ALL and its clinical values on ALL diagnosis and prognosis. First, serum miR-16-2-3p expression in ALL children and healthy volunteers was measured using RT-qPCR. Next, diagnostic potential and prognostic values of miR-16-2-3p on ALL were analyzed through receiver operating characteristic (ROC) curve, Kaplan–Meier survival curve, and multivariate Cox regression analysis, respectively. No significant difference was observed in the clinical baseline data between ALL patients and healthy children. ALL patients showed downregulated serum miR-16-2-3p (0.65 ± 0.27) (p < .01), whose area under the ROC curve was 0.837 with a cut-off value of 0.745 (67.92% sensitivity, 96.94% specificity). ALL patients with higher miR-16-2-3p expression had higher survival rates than those with lower miR-16-2-3p expression. Low miR-16-2-3p expression predicted poor prognosis of ALL patients. After adjusting LDH and lymphomyelocyte proportion (p = 0.003, HR = 0.003, 95%CI = 0.000–0.145), miR-16-2-3p was recognized as an independent prognostic factor for ALL patient survival. Briefly, low serum miR-16-2-3p expression in ALL children could aid ALL diagnosis and predict poor prognosis.

The Applications of Electrochemical Immunosensors in the Detection of Disease Biomarkers: A Review.

Disease-related biomarkers may serve as indicators of human disease. The clinical diagnosis of diseases may largely benefit from timely and accurate detection of biomarkers, which has been the subject of extensive investigations. Due to the specificity of antibody and antigen recognition, electrochemical immunosensors can accurately detect multiple disease biomarkers, including proteins, antigens, and enzymes. This review deals with the fundamentals and types of electrochemical immunosensors. The electrochemical immunosensors are developed using three different catalysts: redox couples, typical biological enzymes, and nanomimetic enzymes. This review also focuses on the applications of those immunosensors in the detection of cancer, Alzheimer's disease, novel coronavirus pneumonia and other diseases. Finally, the future trends in electrochemical immunosensors are addressed in terms of achieving lower detection limits, improving electrode modification capabilities and developing composite functional materials.

The Emerging Value of Circular Noncoding RNA Research in Cancer Diagnosis and Treatment.

Circular RNAs (circRNA) are a recently described class of RNA molecules that have attracted substantial attention as new components of disease mechanisms and as potential biomarkers in multiple diseases, including cancer. CircRNAs are often highly conserved and exhibit developmental stage- and disease-specific expression. Several studies have reported circRNA expression patterns that are associated with specific cancer types and with patient prognosis. Here, we overview the active registered clinical trials that investigate the value of circRNAs as cancer biomarkers and discuss the potential of circRNAs in clinical cancer care. Taken together, circRNAs are actively being investigated as diagnostic, predictive, and prognostic biomarkers, and their potential to serve as therapeutic intervention points motivates ongoing translational and clinical research.

DigEST: Digital plug‐n‐probe disease Endotyping Sensor Technology

AbstractIn this work, we propose a novel diagnostic workflow—DigEST—that will enable stratification of disease states based on severity using multiplexed point of care (POC) biosensors. This work can boost the performance of current POC tests by enabling clear, digestible, and actionable diagnoses to the end user. The scheme can be applied to any disease model, which requires time‐critical disease stratification for personalized treatment. Here, urinary tract infection is explored as the proof‐of‐concept disease model and a four‐class classification of disease severity is discussed. Our method is superior to traditional enzyme‐linked immunosorbent assay (ELISA) as it is faster and can work with multiple disease biomarkers and categorize diseases by endotypes (or disease subtype) and severity. To map the nonlinear nature of biochemical pathways of complex diseases, the method utilizes an established supervised machine learning model for digital classification. This scheme can potentially boost the diagnostic power of current electrochemical biosensors for better precision therapy and improved patient outcomes.

Target-triggered assembly of functional G-quadruplex DNAzyme nanowires for sensitive detection of miRNA in lung tissues

MicroRNAs (miRNAs) are important biomarkers for multiple diseases including cancers, neurodegenerative diseases, and cardiovascular diseases. Accurate detection of miRNAs is imperative for early diagnosis and biomedical research. Herein, we demonstrate target-triggered assembly of G-quadruplex DNAzyme nanowires for sensitive detection of miRNAs in lung tissues by integrating primer generation-mediated rolling circle amplification (PG-RCA) with primer exchange reaction (PER). Target miRNA hybridizes with the circular template to initiate PG-RCA, producing numerous primers. The obtained primers then react with the catalytic hairpin DNA template to trigger PER, producing the polymer G-quadruplex DNAzyme nanowires. Eventually, thioflavin T (ThT) as a fluorescence indicator selectively interacts with the G-quadruplex DNAzyme to generate an amplified signal. This assay is capable of detecting miRNA over a large dynamic range (up to 6 orders of magnitude) and it exhibits high sensitivity with a limit of detection (LOD) of 61.34 aM. Moreover, this assay displays excellent specificity and it can distinguish even a single-base mutation in miR-486–5p. Furthermore, this method can quantify the miR-486–5p concentration in different types of cells, and it enables the identification of differentially expressed miR-486–5p in lung tissues of normal persons and nonsmall-cell lung carcinoma (NSCLC) patients, holding promising applications in biomedical analysis and cancer prognosis.

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

Wireless accessing of salivary biomarkers based wearable electrochemical sensors: A mini-review

The seemingly inevitable advancement in non-invasive monitoring of biomarkers in saliva has spurred wearable electronics and miniature electrochemical sensors to help us lead a comfortable, healthier life. Saliva is a readily available biofluid with rich components such as electrolytes, metabolites, and protein biomarkers. However, fabricating a flexible tiny wearable platform tethered to the body for quantifying biomarkers and retrieving the data from a smartphone via a wireless module. Miniaturized circuits (include a flexible printed circuitry board) with electrochemical sensor modules securely attached to a dental ornament device for intra-oral monitoring of multiple significant disease biomarkers and gain access to them from a customized app of a portable wireless monitor (e.g. Mobile phone, iPad, laptop) by connecting via a microcontroller and Bluetooth enabled settings. This mini-review will focus on the wireless wearable electrochemical sensing motifs developed for saliva biomarkers, challenges, and a quick future outlook.

Biofabrication of Multiplexed Electrochemical Immunosensors for Simultaneous Detection of Clinical Biomarkers in Complex Fluids.

Simultaneous detection of multiple disease biomarkers in unprocessed whole blood is considered the gold standard for accurate clinical diagnosis. Here, this study reports the development of a 4-plex electrochemical (EC) immunosensor with on-chip negative control capable of detecting a range of biomarkers in small volumes (15µL) of complex biological fluids, including serum, plasma, and whole blood. A framework for fabricating and optimizing multiplexed sandwich immunoassays is presented that is enabled by use of EC sensor chips coated with an ultra-selective, antifouling, and nanocomposite coating. Cyclic voltammetry evaluation of sensor performance is carried out by monitoring the local precipitation of an electroactive product generated by horseradish peroxidase linked to a secondary antibody. EC immunosensors demonstrate high sensitivity and specificity without background signal with a limit of detection in single-digit picogram per milliliter in multiple complex biological fluids. These multiplexed immunosensors enable the simultaneous detection of four different biomarkers in plasma and whole blood with excellent sensitivity and selectivity. This rapid and cost-effective biosensor platform can be further adapted for use with different high affinity probes for any biomarker, and thereby create for a new class of highly sensitive and specific multiplexed diagnostics.

Covering the Gap Between Sleep and Cognition - Mechanisms and Clinical Examples.

A growing number of studies have shown the strong relationship between sleep and different cognitive processes, especially those that involve memory consolidation. Traditionally, these processes were attributed to mechanisms related to the macroarchitecture of sleep, as sleep cycles or the duration of specific stages, such as the REM stage. More recently, the relationship between different cognitive traits and specific waves (sleep spindles or slow oscillations) has been studied. We here present the most important physiological processes induced by sleep, with particular focus on brain electrophysiology. In addition, recent and classical literature were reviewed to cover the gap between sleep and cognition, while illustrating this relationship by means of clinical examples. Finally, we propose that future studies may focus not only on analyzing specific waves, but also on the relationship between their characteristics as potential biomarkers for multiple diseases.

Rapid and Sensitive Detection of miRNA Based on AC Electrokinetic Capacitive Sensing for Point-of-Care Applications.

A sensitive and efficient method for microRNAs (miRNAs) detection is strongly desired by clinicians and, in recent years, the search for such a method has drawn much attention. There has been significant interest in using miRNA as biomarkers for multiple diseases and conditions in clinical diagnostics. Presently, most miRNA detection methods suffer from drawbacks, e.g., low sensitivity, long assay time, expensive equipment, trained personnel, or unsuitability for point-of-care. New methodologies are needed to overcome these limitations to allow rapid, sensitive, low-cost, easy-to-use, and portable methods for miRNA detection at the point of care. In this work, to overcome these shortcomings, we integrated capacitive sensing and alternating current electrokinetic effects to detect specific miRNA-16b molecules, as a model, with the limit of detection reaching 1.0 femto molar (fM) levels. The specificity of the sensor was verified by testing miRNA-25, which has the same length as miRNA-16b. The sensor we developed demonstrated significant improvements in sensitivity, response time and cost over other miRNA detection methods, and has application potential at point-of-care.

Proteomic Analysis of Plasma sEVs Reveals That TNFAIP8 Is a New Biomarker of Cell Proliferation in Diabetic Retinopathy.

Small extracellular vesicles (sEVs) derived from the plasma have been increasingly recognized as important vehicles of intercellular communication and potential sources of new biomarkers for multiple diseases. In this study, proteomic profiles of plasma sEVs from normal subjects and diabetic patients with or without diabetic retinopathy (DR) were systematically compared using iTRAQ-based quantitative proteomics. Among a total of 901 identified proteins in plasma sEVs (false discovery rate (FDR) < 1%), 90 proteins were found to have significantly changed levels in DR. Based on the findings from the proteomic analysis, the role of tumor necrosis factor-α-induced protein 8 (TNFAIP8) in promoting human retinal microvascular endothelial cell (HRMEC) proliferation was investigated. The enzyme-linked immunosorbent assay (ELISA) showed that TNFAIP8 levels in plasma sEVs and vitreous are elevated in DR, whereas not statistically different in large EVs (lEVs) and plasma. In addition, in vitro experiments demonstrated that 4-hydroxynonenal (4-HNE) increased the expression of TNFAIP8 in HRMECs. TNFAIP8 significantly increased HRMECs cell viability and promote cell migration and tube formation, and the depletion of TNFAIP8 impaired HRMEC proliferation. We demonstrated that TNFAIP8 in plasma sEVs could be used as a potential biomarker of DR. Functional studies suggested that TNFAIP8 might be an important mediator of angiogenesis in DR.

A Novel Approach of Transcriptomic microRNA Analysis Using Text Mining Methods: An Early Detection of Multiple Sclerosis Disease

Multiple sclerosis is an autoimmune disease that causes psychological impacts and severe physical disabilities, including motor disabilities and partial blindness. This work introduces an early detection method for multiple sclerosis disease by analyzing transcriptomic microRNA data. By transforming this phenotype classification problem into a text mining problem, multiple sclerosis disease biomarkers can be obtained. To our knowledge, text mining methods have not been introduced previously in transcriptomic data analysis of multiple sclerosis disease. Hence, this work presents a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction. Predictive machine learning methods can then be obtained accordingly. This study describes experimental work on a transcriptomic dataset of noncoding microRNA sequences denoted from relapsing-remitting multiple sclerosis patients before fingolimod treatment and after six consecutive months of treatment. The experimental results of the predictive methods with the proposed model report sensitivity, specificity, F1-score, and average accuracy scores of 96.4, 96.47, 95.6, and 97% with random forest, 92.89, 92.78, 93.2, and 94% with support vector machine and 91.95, 92.2, 93.1, and 94% with logistic regression, respectively. These promising results support the introduced model and the proposed KmerFIDF method in transcriptomic data analysis. Moreover, comparative experiments are conducted with two referenced studies. The obtained results show that the average reported accuracy scores of the proposed model outperform the referenced literature work.