Additional file 2: Differentially expressed lncRNAs in TCGA-BRCA

Additional file 2: Differentially expressed lncRNAs in TCGA-BRCA

Additional file 4: Canonical pathways, diseases and functions associated with the 127 proxy protein-coding genes.

Additional file 10: Table S10. Description of the eight genes that are predictors of mortality. Data were collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19 related articles to highlight the role of each gene in relation to COVID-19.

Additional file 4: Table S4. Summary of differentially methylated pathways detected between COVID-19 patients and controls based on CpG sites.

Additional file 2: Table S2. Summary of different models tested for estimating differences between controls and COVID-19 patients for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p.value for each tested model for each cell type are shown. The following models were tested, mod1; Age and ethnicity as covariates, mod2; age as a covariate, mod3; ethnicity as a covariate, mod4; no covariates.

Additional file 3: Table S3. Summary of identified differentially methylated CpGs between COVID-19 patients and controls. A. All significant CpGs B. Variable description from Table S2A. C. Significant CpGs from genes previously described as COVID-19 important [1]. D. Functional annotation of genes from Supplemental table 3C.

Additional file 6: Table S6. Analysis of dead and recovered+A3 COVID-19 patients for immune cell proportions. A. Summary of different models tested for immune cell proportions. Adjusted R2, residual standard error (sigma), AIC, and p-value for each tested model for each cell type are shown. The following models were tested, mod1; Age + MV days + Gender + ICU LoS + ECMO + Nosocomial infections, mod2; Age + Gender + ICU LoS + ECMO +...

Additional file 8: Table S8. Description of 27 genes from 49 differentially methylated CpGs between survived and dead patients over four time points. A summarized description of the 27 genes obtained from Supplementary Table 7B, collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19-related articles to highlight the role of each gene in relation to COVID-19.

Additional file 8: Table S8. Description of 27 genes from 49 differentially methylated CpGs between survived and dead patients over four time points. A summarized description of the 27 genes obtained from Supplementary Table 7B, collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19-related articles to highlight the role of each gene in relation to COVID-19.

Supplementary Material 1

Additional file 3: Propagation walkscores of 127 proxy protein-coding genes in the TCGA-BRCA cohort.

Additional file 4: Canonical pathways, diseases and functions associated with the 127 proxy protein-coding genes.

Additional file 6: Prognostic value of ICR classifier across solid cancers. Forest plot showing HRs for death (overall survival) and corresponding 95%-confidence interval for the continuous ICR score and number of patients for each TCGA cancer cohort and RAQA breast cancer cohort. Significant positive HRs are visualized in blue and significant negative HRs are visualized in red. ICR enabled (HR < 1, p-value < 0.05) cancer types are indicated with orange asterisks and ICR disabled...

Abstract Background Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly...

Additional file 7: Akaike information criterion (AIC), delta AIC, and HRs for death (overall survival) and corresponding 95%-confidence interval for ICR and 3 ir-lncRNA signature in 18 solid cancer TCGA datasets and RAQA cohort.

Additional file 10: Table S10. Description of the eight genes that are predictors of mortality. Data were collected from Gene Ontology (GO) to identify the functional annotation of each gene and recently published COVID-19 related articles to highlight the role of each gene in relation to COVID-19.

Additional file 11. Supplementary Figures.

Additional file 7: Table S7. Summary of immune cell changes and differentially methylated CpGs between recovered and dead patients over four time points 7A. Immune cell changes between recovered and dead patients over four time points, 7B. Differential methylation of CpGs between recovered and died patients over four time points. The b_0, b_1, b_2, and b_3 coefficients correspond to the reference model parameters, where survival phenotype is used as a reference. The d_0, d_1, d_2,...

Additional file 1:Table S1. Baseline characteristics of COVID-19 patients and controls.

Abstract Background COVID-19 infections could be complicated by acute respiratory distress syndrome (ARDS), increasing mortality risk. We sought to assess the methylome of peripheral blood mononuclear cells in COVID-19 with ARDS. Methods We recruited 100 COVID-19 patients with ARDS under mechanical ventilation and 33 non-COVID-19 controls between April and July 2020. COVID-19 patients were followed at four time points for 60 days. DNA methylation and immune cell populations were measured at each time point. A...

Supplementary Material 1

Abstract Background The genetic architecture underlying Familial Hypercholesterolemia (FH) in Middle Eastern Arabs is yet to be fully described, and approaches to assess this from population-wide biobanks are important for public health planning and personalized medicine. Methods We evaluate the pilot phase cohort (n = 6,140 adults) of the Qatar Biobank (QBB) for FH using the Dutch Lipid Clinic Network (DLCN) criteria, followed by an in-depth characterization of all genetic alleles in known dominant (LDLR,...