Dataset Information

Panel of Genetic Variations as a Potential Non-invasive Biomarker for Early Diagnosis of Alzheimer's Disease.

ABSTRACT: Alzheimer's disease (AD) is the most prevalent form of dementia. Biomarkers such as levels of amyloid beta (A?) in cerebrospinal fluid and ApoE genotyping were suggested for the diagnosis of AD, however, the result is either non-conclusive or with invasive procedure. Genome-wide association studies (GWASs) for AD suggested single nucleotide polymorphisms (SNPs) in many genes are associated with the risk of AD, but each only contributed with small effect to the disease. By incorporating a panel of established genetic susceptibility factors, the risk of an individual in getting AD could be better estimated. Further research will be required to reveal if adding to the current well-developed clinical diagnosis protocol, the accuracy and specificity of diagnosis of AD would be greatly improved and if this might also be beneficial in identifying pre-symptomatic AD patients for early diagnosis and intervention of the disease.

SUBMITTER: Ma SL

PROVIDER: S-EPMC3569084 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Panel of Genetic Variations as a Potential Non-invasive Biomarker for Early Diagnosis of Alzheimer's Disease.

Ma Suk Ling SL Lam Linda Chiu Wa LC

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology 20110831 2

Alzheimer's disease (AD) is the most prevalent form of dementia. Biomarkers such as levels of amyloid beta (Aβ) in cerebrospinal fluid and ApoE genotyping were suggested for the diagnosis of AD, however, the result is either non-conclusive or with invasive procedure. Genome-wide association studies (GWASs) for AD suggested single nucleotide polymorphisms (SNPs) in many genes are associated with the risk of AD, but each only contributed with small effect to the disease. By incorporating a panel o ...[more]

PMID: 23429712

Similar Datasets

Project description:Background:One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods:A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results:Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion:MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Dataset Information

Panel of Genetic Variations as a Potential Non-invasive Biomarker for Early Diagnosis of Alzheimer's Disease.

Publications

Panel of Genetic Variations as a Potential Non-invasive Biomarker for Early Diagnosis of Alzheimer's Disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure