Additional file 1 of Multiplexed MRM-based proteomics for identification of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus
Creators: name: Piarulli, Francesco
name: Banfi, Cristina
name: Ragazzi, Eugenio
name: Gianazza, Erica
name: Munno, Marco
name: Carollo, Massimo
name: Traldi, Pietro
name: Lapolla, Annunziata
name: Sartore, Giovanni
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Physiology
subject: Chemical Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified

description: Supplementary Material 1: Table S1. Clinical and metabolic parameters of subjects in study. Table S2. Peptides and transitions of the multimarker panel. Table S3. The pool of plasma sample analyzed for quality controls throughout the run. Table S4. List of the 81 proteins of the final biomarker panel, with UniProt Accession Number ( https://www.uniprot.org/help/accession_numbers ) and abbreviated names used for graphical display. Table S5. Gene Ontology (GO) analysis related to cellular component. Table S6. GO analysis related to the biological process revealing the enrichment of GO terms related to chylomicron remodeling and assembly. Figure S1. Validation of the 13 proteins selected with ANOVA. Panel A: Plot showing Important features identified by PLS-DA. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group under study. Variable Importance in Projection (VIP) is a weighted sum of squares of the PLS loadings taking into account the amount of explained variation in each dimension. Panel B: Plot showing the variables selected by the sPLS-DA model for a given component. The variables are ranked by the absolute values of their loadings. Panel C: Significant features identified by Random Forest. The features are ranked by the mean decrease in classification accuracy when they are permuted. Table S7. Summary of the features of the 13 proteins identified by ANOVA, according to the different non-parametric employed statistical procedures. The relevance of the proteins is indicated with semi-quantitative approximate scoring. Figure S2. A) Clustering result shown as heatmap (distance measure using Euclidean, and clustering algorithm using Ward method), obtained on the 13 selected proteins significant at ANOVA; differently from Figure 5, no ordering of groups was here applied, to show the overall pattern of protein distribution among groups. Heatmap color intensity is proportional to the parameter’s value (blue to red), as in the reported scale on the right of the figure. B) Clustering result including also factors such as age, gender, lipid profile, and glucose parameters. Figure S3. Clustering result shown as heatmap (distance measure using Euclidean, and clustering algorithm using Ward method), obtained on the overall panel of 81 proteins. Table S8. Effect summary of nominal logistic plot for three paired comparisons between groups of subjects by using the 13 selected proteins. Logworth and p value for each item are presented. Whole model test was successfully accomplished in any cases with overall significance of p&lt;0.0001.