Dataset Information

A different vision of translational research in biomarker discovery: a pilot study on circulatory mitochondrial proteins as Parkinson's disease potential biomarkers.

ABSTRACT: Background:The identification of circulating biomarkers that closely correlate with Parkinson's Disease (PD) has failed several times in the past. Nevertheless, in this pilot study, a translational approach was conducted, allowing the evaluation of the plasma levels of two mitochondrial-related proteins, whose combination leads to a robust model with potential diagnostic value to discriminate the PD patients from matched controls. Methods:The proposed translational approach was initiated by the analysis of secretomes from cells cultured under control or well-defined oxidative stress conditions, followed by the identification of proteins related to PD pathologic mechanisms that were altered between the two states. This pipeline was further translated into the analysis of undepleted plasma samples from 28 control and 31 PD patients. Results:From the secretome analysis, several mitochondria-related proteins were found to be differentially released between control and stress conditions and to be able to distinguish the two secretomes. Similarly, two mitochondrial-related proteins were found to be significantly changed in a PD cohort compared to matched controls. Moreover, a linear discriminant model with potential diagnostic value to discriminate PD patients was obtained using the combination of these two proteins. Both proteins are associated with apoptotic mitochondrial changes, which may correspond to potential indicators of cell death. Moreover, one of these proteins, the VPS35 protein, was reported in plasma for the first time, and its quantification was only possible due to its previous identification in the secretome analysis. Conclusions:In this work, an adaptation of a translational pipeline for biomarker selection was presented and transposed to neurological diseases, in the present case Parkinson's Disease. The novelty and success of this pilot study may arise from the combination of: i) a translational research pipeline, where plasma samples are interrogated using knowledge previously obtained from the evaluation of cells' secretome under oxidative stress; ii) the combined used of statistical analysis and an informed selection of candidates based on their link with relevant disease mechanisms, and iii) the use of SWATH-MS, an untargeted MS method that allows a complete record of the analyzed samples and a targeted data extraction of the quantitative values of proteins previously identified.

SUBMITTER: Anjo SI

PROVIDER: S-EPMC7118951 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start "speaking the same language" in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.

Dataset Information

A different vision of translational research in biomarker discovery: a pilot study on circulatory mitochondrial proteins as Parkinson's disease potential biomarkers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets