Quest for new genomic and proteomic biomarkers in neurology

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Kristina Gotovac ◽  
Nela Pivac ◽  
Sanja Hajnšek ◽  
Dorotea Mück-Šeler ◽  
Fran Borovečki
Keyword(s):  
Neurodegenerative Diseases ◽  
Biomarker Discovery ◽  
Sample Collection ◽  
Disease Etiology ◽  
Novel Technologies ◽  
Proteomic Data ◽  
Genomics And Proteomics ◽  
Novel Biomarkers ◽  
New Biomarkers ◽  
Patient Studies

AbstractThe possibility of identifying novel biomarkers for neurodegenerative diseases has been greatly enhanced with recent advances in genomics and proteomics. Novel technologies have the potential to hasten the development of new biomarkers useful as predictors of disease etiology and outcome, as well as responsiveness to therapy. Disease-modifying new therapies are very much needed in modern approaches to treatment of neurodegenerative diseases. Current progress in the field encounters a degree of skepticism about the reliability of genomic and proteomic data and its relevance for clinical applications. Standard operating procedures covering sample collection, methodology and statistical analysis need to be fully developed and strictly adhered to in order to assure reproducible and clinically relevant results. Previous studies involving patients with neurodegenerative diseases show promise in using genomic and proteomic approaches for development of new biomarkers. Confirmation of any novel biomarker in multiple independent patient cohorts and correlation of the improvement in biomarker endpoint with clinical improvement in longitudinal patient studies remains crucial for future successful application. We propose that a combination of approaches in biomarker discovery may in the end lead to identification of promising candidates at DNA, RNA, protein and small molecule level.

scholarly journals Proteomics as a Tool for Biomarker Discovery

2007 ◽  
Vol 23 (5-6) ◽  
pp. 411-417 ◽  
Author(s):  
Elise C. Kohn ◽  
Nilofer Azad ◽  
Christina Annunziata ◽  
Amit S. Dhamoon ◽  
Gordon Whiteley
Keyword(s):  
Quality Improvement ◽  
New Technologies ◽  
Biomarker Discovery ◽  
Early Stage ◽  
Life Quality ◽  
Early Stage Disease ◽  
Novel Technologies ◽  
True Value ◽  
Stage Disease ◽  
New Biomarkers

Novel technologies are now being advanced for the purpose of identification and validation of new disease biomarkers. A reliable and useful clinical biomarker must a) come from a readily attainable source, such as blood or urine, b) have sufficient sensitivity to correctly identify affected individuals, c) have sufficient specificity to avoid incorrect labeling of unaffected persons, and d) result in a notable benefit for the patient through intervention, such as survival or life quality improvement. Despite these critical descriptors, the few available FDA-approved biomarkers for cancer do not completely fit this definition and their benefits are limited to a small number of cancers. Ovarian cancer exemplifies the need for a diagnostic biomarker of early stage disease. Symptoms are present but not specific to the disease, delaying diagnosis until an advanced and generally incurable stage in over 70% of affected women. As such, diagnostic intervention in the form of oopherectomy can be performed in the appropriate at-risk population if identified such as with a new accurate, sensitive, and specific biomarker. If early stage disease is identified, the requirement for survival and life quality improvement will be met. One of the new technologies applied to biomarker discovery is tour-de-force analysis of serum peptides and proteins. Optimization of mass spectrometry techniques coupled with advanced bioinformatics approaches has yielded informative biomarker signatures discriminating presence of cancer from unaffected in multiple studies from different groups. Validation and randomized outcome studies are needed to determine the true value of these new biomarkers in early diagnosis, and improved survival and quality of life.

scholarly journals The Use of Proteomics in Biomarker Discovery in Neurodegenerative Diseases

2005 ◽  
Vol 21 (2) ◽  
pp. 81-92 ◽  
Author(s):  
Pia Davidsson ◽  
Magnus Sjögren
Keyword(s):  
Neurodegenerative Diseases ◽  
Biomarker Discovery ◽  
Neurological Diseases ◽  
Synaptic Proteins ◽  
Clinical Proteomics ◽  
Protein Patterns ◽  
Differential Protein ◽  
Csf Proteins ◽  
New Biomarkers ◽  
The Brain

Biomarkers for neurodegenerative diseases should reflect the central pathogenic processes of the diseases. The field of clinical proteomics is especially well suited for discovery of biomarkers in cerebrospinal fluid (CSF), which reflects the proteins in the brain under healthy conditions as well as in several neurodegenerative diseases. Known proteins involved in the pathology of neurodegenerative diseases are, respectively, normal tau protein,β-amyloid (1-42), synaptic proteins, amyloid precursor protein (APP), apolipoprotein E (apoE), which previously have been studied by protein immunoassays. The objective of this paper was to summarize results from proteomic studies of differential protein patterns in neurodegenerative diseases with focus on Alzheimer's disease (AD). Today, discrimination of AD from controls and from other neurological diseases has been improved by simultaneous analysis of bothβ-amyloid (1-42), total-tau, and phosphorylated tau, where a combination of low levels of CSF-β-amyloid 1-42 and high levels of CSF-tau and CSF-phospho-tau is associated with an AD diagnosis. Detection of new biomarkers will further strengthen diagnosis and provide useful information in drug trials. The combination of immunoassays and proteomic methods show that the CSF proteins express differential protein patterns in AD, FTD, and PD patients, which reflect divergent underlying pathophysiological mechanisms and neuropathological changes in these diseases.

scholarly journals Biomarkers of doping: uses, discovery and validation

Bioanalysis ◽  
2020 ◽  
Author(s):  
Reid Aikin ◽  
Norbert Baume ◽  
Tristan Equey ◽  
Olivier Rabin
Keyword(s):  
Biomarker Discovery ◽  
Biological Response ◽  
High Potential ◽  
Routine Analysis ◽  
Novel Biomarkers ◽  
New Biomarkers ◽  
Study Designs ◽  
Analytical Approaches

A biomarker of doping indicates the biological response to the use of a prohibited substance or method. Uncovering novel biomarkers of doping is a key objective in order to improve antidoping outcomes such as the detection of doping and changing athlete behavior toward doping practices. While the antidoping field has been successful in validating novel metabolites of prohibited substances, there has been less success in developing new biomarkers of doping. Employing the most suitable study designs and analytical approaches is critical to successfully uncovering novel biomarkers of doping with a high potential for translation into routine analysis. Here we argue that the antidoping field is well positioned for biomarker discovery and outline considerations for the development of novel biomarkers of doping.

scholarly journals Different miRNA Profiles in Plasma Derived Small and Large Extracellular Vesicles from Patients with Neurodegenerative Diseases

2021 ◽  
Vol 22 (5) ◽  
pp. 2737
Author(s):  
Daisy Sproviero ◽  
Stella Gagliardi ◽  
Susanna Zucca ◽  
Maddalena Arigoni ◽  
Marta Giannini ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Extracellular Vesicles ◽  
Small Rnas ◽  
Biomarker Discovery ◽  
Toll Like Receptor ◽  
Neurotrophin Signaling ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Lateral Sclerosis ◽  
Glycosphingolipid Biosynthesis

Identifying biomarkers is essential for early diagnosis of neurodegenerative diseases (NDs). Large (LEVs) and small extracellular vesicles (SEVs) are extracellular vesicles (EVs) of different sizes and biological functions transported in blood and they may be valid biomarkers for NDs. The aim of our study was to investigate common and different miRNA signatures in plasma derived LEVs and SEVs of Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Fronto-Temporal Dementia (FTD) patients. LEVs and SEVs were isolated from plasma of patients and healthy volunteers (CTR) by filtration and differential centrifugation and RNA was extracted. Small RNAs libraries were carried out by Next Generation Sequencing (NGS). MiRNAs discriminate all NDs diseases from CTRs and they can provide a signature for each NDs. Common enriched pathways for SEVs were instead linked to ubiquitin mediated proteolysis and Toll-like receptor signaling pathways and for LEVs to neurotrophin signaling and Glycosphingolipid biosynthesis pathway. LEVs and SEVs are involved in different pathways and this might give a specificity to their role in the spreading of the disease. The study of common and different miRNAs transported by LEVs and SEVs can be of great interest for biomarker discovery and for pathogenesis studies in neurodegeneration.

scholarly journals Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Frederick S. Vizeacoumar ◽  
Hongyu Guo ◽  
Lynn Dwernychuk ◽  
Adnan Zaidi ◽  
Andrew Freywald ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Cancer Recurrence ◽  
The Cancer Genome Atlas ◽  
Svm Classifier ◽  
Plasma Proteome ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers ◽  
Evaluation Metric

AbstractGastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

scholarly journals Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rosa Alba Sola Martínez ◽  
José María Pastor Hernández ◽  
Gema Lozano Terol ◽  
Julia Gallego-Jara ◽  
Luis García-Marcos ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Data Preprocessing ◽  
Exhaled Breath ◽  
Sample Collection ◽  
Clinical Tool ◽  
Chronic Respiratory Diseases ◽  
Current State ◽  
Exhaled Breath Analysis ◽  
High Prevalence ◽  
Volatile Organic

AbstractThe noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS.

scholarly journals Searching for New Biomarkers of Renal Diseases through Proteomics

2012 ◽  
Vol 58 (2) ◽  
pp. 353-365 ◽  
Author(s):  
Ana Konvalinka ◽  
James W Scholey ◽  
Eleftherios P Diamandis
Keyword(s):  
Mass Spectrometry ◽  
Prognostic Markers ◽  
Renal Diseases ◽  
Sample Collection ◽  
Technological Advances ◽  
Regulatory Bodies ◽  
New Biomarkers ◽  
Study Designs ◽  
Treatment Responsiveness ◽  
Proteomic Methods

Abstract BACKGROUND Technological advances have resulted in a renaissance of proteomic studies directed at finding markers of disease progression, diagnosis, or responsiveness to therapy. Renal diseases are ideally suited for such research, given that urine is an easily accessible biofluid and its protein content is derived mainly from the kidney. Current renal prognostic markers have limited value, and renal biopsy remains the sole method for establishing a diagnosis. Mass spectrometry instruments, which can detect thousands of proteins at nanomolar (or even femtomolar) concentrations, may be expected to allow the discovery of improved markers of progression, diagnosis, or treatment responsiveness. CONTENT In this review we describe the strengths and limitations of proteomic methods and the drawbacks of existing biomarkers, and provide an overview of opportunities in the field. We also highlight several proteomic studies of biomarkers of renal diseases selected from the plethora of studies performed. SUMMARY It is clear that the field of proteomics has not yet fulfilled its promise. However, ongoing efforts to standardize sample collection and preparation, improve study designs, perform multicenter validations, and create joint industry–regulatory bodies offer promise for the recognition of novel molecules that could change clinical nephrology forever.

scholarly journals Whole Blood Holding Time Prior to Plasma Processing Alters microRNA Expression Profile

2022 ◽  
Vol 12 ◽  
Author(s):  
Sung Hye Kim ◽  
David A. MacIntyre ◽  
Lynne Sykes ◽  
Maria Arianoglou ◽  
Phillip R. Bennett ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Whole Blood ◽  
Biomarker Discovery ◽  
Expression Profiles ◽  
Holding Time ◽  
Circulating Mirnas ◽  
Sample Collection ◽  
Aberrant Expression ◽  
Discovery Research ◽  
Mirna Expression Profiles

MicroRNAs (miRNAs) can exhibit aberrant expression under different physiological and pathological conditions. Therefore, differentially expressed circulating miRNAs have been a focus of biomarker discovery research. However, the use of circulating miRNAs comes with challenges which may hinder the reliability for their clinical application. These include varied sample collection protocols, storage times/conditions, sample processing and analysis methods. This study focused on examining the effect of whole blood holding time on the stability of plasma miRNA expression profiles. Whole blood samples were collected from healthy pregnant women and were held at 4°C for 30 min, 2 h, 6 h or 24 h prior to processing for plasma isolation. Plasma RNA was extracted and the expression of 179 miRNAs were analyzed. Unsupervised principal component analysis demonstrated that whole blood holding time was a major source of variation in miRNA expression profiles with 53 of 179 miRNAs showing significant changes in expression. Levels of specific miRNAs previously reported to be associated with pregnancy-associated complications such as hsa-miR-150-5p, hsa-miR-191-5p, and hsa-miR-29a-3p, as well as commonly used endogenous miRNA controls, hsa-miR-16-5p, hsa-miR-25-3p, and hsa-miR-223-3p were significantly altered with increase in blood holding time. Current protocols for plasma-based miRNA profiling for diagnostics describe major differences in whole blood holding periods ranging from immediately after collection to 26 h after. Our results demonstrate holding time can have dramatic effects on analytical reliability and reproducibility. This highlights the importance of standardization of blood holding time prior to processing for plasma in order to minimize introduction of non-biological variance in miRNA profiles.

scholarly journals A statistical framework for assessing pharmacological responses and biomarkers using uncertainty estimates

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dennis Wang ◽  
James Hensman ◽  
Ginte Kutkaite ◽  
Tzen S Toh ◽  
Ana Galhoz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Dose Response ◽  
Biomarker Discovery ◽  
Statistical Framework ◽  
Curve Fit ◽  
Uncertainty Estimates ◽  
Drug Response Biomarkers ◽  
Novel Biomarkers ◽  
Response Biomarkers

High-throughput testing of drugs across molecular-characterised cell lines can identify candidate treatments and discover biomarkers. However, the cells’ response to a drug is typically quantified by a summary statistic from a best-fit dose-response curve, whilst neglecting the uncertainty of the curve fit and the potential variability in the raw readouts. Here, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertainty estimates to identify associated biomarkers with a new Bayesian framework. Applied to in vitro screening data on 265 compounds across 1074 cancer cell lines, our models identified 24 clinically established drug-response biomarkers, and provided evidence for six novel biomarkers by accounting for association with low uncertainty. We validated our uncertainty estimates with an additional drug screen of 26 drugs, 10 cell lines with 8 to 9 replicates. Our method is applicable to any dose-response data without replicates, and improves biomarker discovery for precision medicine.

CircRNAs in lung adenocarcinoma: diagnosis and therapy

2021 ◽  
Vol 21 ◽  
Author(s):  
Lijia Su ◽  
Jinying Zhao ◽  
Huahua Su ◽  
Yanhua Wang ◽  
Wenfeng Huang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Biomedical Application ◽  
Small Cell Lung Carcinoma ◽  
Disease Diagnosis ◽  
Circular Rnas ◽  
Cell Lung Carcinoma ◽  
Diagnosis And Therapy ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers ◽  
New Biomarkers

: Lung adenocarcinoma (LUAD) is the common histological subtype of non-small-cell lung carcinoma (NSCLC). Circular RNAs (circRNAs) represent a new class of non-coding RNAs (ncRNAs) involved in the development of cancer. Accumulating evidence indicated that a large number of circular RNAs were found to be involved in many biological processes, including tumor initiation, proliferation and progression. These circRNAs present great potentials as new biomarkers and vital targets for disease diagnosis and prognosis. In this review, we mainly focus on the differentially expressed circRNAs and their functions in the pathogenesis of LUAD, which makes it possible for the utility of circRNAs as novel biomarkers for early diagnosis and therapy. Especially, it is helpful to develop circRNAs as crucial therapeutic targets, thus providing a promising biomedical application in the field of cancer gene therapy.

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