Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in XXXX with advanced @entity712 .

@entity1 @entity712 @entity409
BACKGROUND: @entity712 ( @entity712 ) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced @entity712 (AdHF) @entity1 . Perioperatively, @entity409 ( @entity409 ) is linked to the effects of device implantation, augmented by preexisting @entity712 . Early recognition of @entity409 allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from @entity1 ' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined. METHODS: We collected blood samples from 31 @entity712 @entity1 (57  15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color @entity1 BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of @entity409 into low ( <= 4 points), intermediate (5-11), and high (>=  12) risk categories correlating with GEP. RESULTS: Results indicate that the direction of change in GEP of individuals with @entity409 as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups. CONCLUSION: GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in @entity1 with @entity409 .
