Transcriptomics

Dataset Information

0

Genomic and Proteomic Biomarkers of Cardiac Allograft Vasculopathy


ABSTRACT: Long-term survival of cardiac transplant recipients remains a significant hurdle largely due to cardiac allograft vasculopathy (CAV) as an expression of chronic rejection (CR). Currently, coronary angiography and intravascular ultrasound, invasive and expensive modalities, are considered the standard for diagnosis of CAV. In the current study, we combined assessment of blood mRNA and proteins to identify potential biomarkers for diagnosis of CAV. Whole blood Affymetrix microarrays and plasma iTRAQ-MALDI-TOF/TOF proteomic analyses were carried out on 25 cardiac transplant patient samples. CAV diagnosis was made based on blinded quantitative analysis of angiograms, intravascular ultrasound images and clinical chart review. The genomics and proteomics data were assessed by moderated t-tests and discriminant analysis. Two panels of 10 differentially expressed genes (FDR<5%), and 10 proteins with differential relative levels (p-values<0.025) between CAV and non-CAV samples were independently identified. Classification of new test samples revealed that the 10 genes together can discriminate between CAV and non-CAV samples with 83% sensitivity and specificity. Classification of the same samples using the 10 PGC’s dropped the sensitivity to 67%. A combinatorial panel derived from the genomic and proteomic panels provided improvement in performance, at 100% sensitivity and 83% specificity. Leave-one-out cross validation showed similar results. Genomic, proteomic and combinatorial blood-based biomarkers are promising as potential minimally-invasive, sensitive and specific modalities for the diagnosis of CAV. Classifier panels generated from current work are being evaluated in a prospective, multi-site observational trial.

ORGANISM(S): Homo sapiens

PROVIDER: GSE18656 | GEO | 2020/09/01

REPOSITORIES: GEO

Dataset's files

Source:
Action DRS
Other
Items per page:
1 - 1 of 1

Similar Datasets

2018-12-21 | GSE113511 | GEO
2020-01-21 | GSE132176 | GEO
| PRJNA608250 | ENA
2023-09-18 | GSE241024 | GEO
2024-07-23 | MODEL2407230001 | BioModels
2012-07-11 | E-GEOD-35490 | biostudies-arrayexpress
2023-04-23 | GSE223691 | GEO
2021-09-09 | PXD016483 | Pride
2016-08-08 | E-GEOD-62629 | biostudies-arrayexpress
2019-05-28 | GSE124120 | GEO