Project description:Four male patients were enrolled for this study in collaboration with the Cardiology Unit of Policlinico Tor Vergata-Fondazione PTV (Rome). The first group of patients has chronic coronary artery disease (CAD) confirmed by coronary angiography, the second group are subjects with clinically proven healthy coronary arteries (CTR). On our case study we have performed a genome-wide methylation study on genomic DNA bisulfite-converted and a miRNA-sequencing study using NextSeq 500 ILLUMINA platform. The methylation study showed different methylated regions (DMRs) and single CpG sites (DMCs) in patients sharing the same clinical and pathological features, allowing detecting distinctly different methylation patterns between CTR subjects and CAD patients. Moreover, miRNA-sequencing results displayed a differential expression of several significant miRNAs (p-value<0.05), defining a peculiar miRNAs profile in patients featuring the same clinical data. miRNA-sequencing and genome-wide methylation integreted results, showed hsa-miR-200c-3p down-regulated in CAD patients compared to control subjects (FC CAD=2.97 and p≤0.05) and with two hypermethylated sites (genomic coordinates: chr12:7073122-7073122 and chr12:7072599-7072599) in its promoter region (p-value=0.009). We extended the validation of these results on all case study (n=96; 24 CTR and 72 CAD).
Project description:The human LncRNA microarray analysis of the 6 plasma samples from Coronary Artery Disease patients and non Coronary Artery Disease Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization was performed using Expander6 and subsequent data processing was performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). After low intensity filtering, LncRNAs and mRNAs that at least 2 out of 12 samples have flags in Present or Marginal (“All Targets Value”) were chosen for quantile normalization and further data analysis. Differentially expressed LncRNAs and mRNAs with statistical significance were identified through Volcano Plot filtering. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed mRNAs played in these biological pathways or GO terms. Finally, Hierarchical Clustering was performed to show the distinguishable LncRNAs expression pattern among samples.