48 Works

Additional file 1 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 1: Mapping of differentially expressed ir-lncRNAs to protein coding genes. (A) Diagram representation of the random walk with restart global propagation network algorithm. (B) Walkscore distribution of protein-coding genes in TCGA-BRCA, with cutoff set at walkscore ≥ 0.01 to generate a ranked list of protein-coding genes in proximity of differentially expressed ir-lncRNAs.

Additional file 5 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 5: Spearman correlation coefficients of ir-lncRNAs with immune checkpoint expression in TCGA-BRCA.

Additional file 9 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 9: Correlation of 3 ir-lncRNA signature with immune subpopulations across tumor types. Pearson correlation heatmap between immune cell subpopulation enrichment scores and 3 ir-lncRNA scores.

Additional file 9 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 9: Table S9. Summary of univariate Cox proportional hazard analysis of the previously identified CpGs. Column variables represent Name; chr; chromosome number, pos; position, CpG name, relation_to_Island; where is CpG located in relationship to island, UCSC RefGene Name; UCSC gene name, UCSC RefGene Accession; UCSC gene accession, UCSC RefGene Group; where in respect to gene is CpG located, Beta; estimated coefficient beta from the model, StandardError; standard error, Z; z-score, LRT; likelihood ratio...

Additional file 11 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 11. Supplementary Figures.

Additional file 1 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 1:Table S1. Baseline characteristics of COVID-19 patients and controls.

Additional file 5 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 5: Table S5. Summary of differentially methylated CpGs in recovered and died COVID-19 patients. A. Immune cell comparison between baseline and recovered pairs, B. Significant CpGs between baseline and recovered pairs, C. CpG pathways between baseline and recovered pairs, D. Immune cell comparison between baseline and died pairs, E. Significant CpGs between baseline and died pairs, F. CpG pathways between baseline and died pairs.

Additional file 5 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 5: Table S5. Summary of differentially methylated CpGs in recovered and died COVID-19 patients. A. Immune cell comparison between baseline and recovered pairs, B. Significant CpGs between baseline and recovered pairs, C. CpG pathways between baseline and recovered pairs, D. Immune cell comparison between baseline and died pairs, E. Significant CpGs between baseline and died pairs, F. CpG pathways between baseline and died pairs.

Additional file 6 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
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 7 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
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 6 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
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...

Additional file 8 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 8: Survival curves of 3 ir-lncRNA signature. Overall survival Kaplan-Meier curves in which the 3 ir-lncRNAs signature did not show any significant prognostic value. Dichotomization cutoff of ‘high’ (red) and ‘low’ (cyan) subgroups was based on the optimal cut-off point as determined by a 5-fold cross-validation analysis. Censor points are indicated by vertical lines. P-values were determined by logrank test.

Additional file 8 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 8: Survival curves of 3 ir-lncRNA signature. Overall survival Kaplan-Meier curves in which the 3 ir-lncRNAs signature did not show any significant prognostic value. Dichotomization cutoff of ‘high’ (red) and ‘low’ (cyan) subgroups was based on the optimal cut-off point as determined by a 5-fold cross-validation analysis. Censor points are indicated by vertical lines. P-values were determined by logrank test.

Additional file 5 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 5: Spearman correlation coefficients of ir-lncRNAs with immune checkpoint expression in TCGA-BRCA.

Additional file 1 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 1: Mapping of differentially expressed ir-lncRNAs to protein coding genes. (A) Diagram representation of the random walk with restart global propagation network algorithm. (B) Walkscore distribution of protein-coding genes in TCGA-BRCA, with cutoff set at walkscore ≥ 0.01 to generate a ranked list of protein-coding genes in proximity of differentially expressed ir-lncRNAs.

Additional file 3 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 3: Propagation walkscores of 127 proxy protein-coding genes in the TCGA-BRCA cohort.

Additional file 7 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
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.

Assessing the genetic burden of familial hypercholesterolemia in a large middle eastern biobank

Geethanjali Devadoss Gandhi, Waleed Aamer, Navaneethakrishnan Krishnamoorthy, Najeeb Syed, Elbay Aliyev, Aljazi Al-Maraghi, Muhammad Kohailan, Jamil Alenbawi, Mohammed Elanbari, Borbala Mifsud, Younes Mokrab, Charbel Abi Khalil & Khalid A. Fakhro
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,...

Additional file 2 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
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 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
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 4 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 4: Table S4. Summary of differentially methylated pathways detected between COVID-19 patients and controls based on CpG sites.

Additional file 9 of DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
Additional file 9: Table S9. Summary of univariate Cox proportional hazard analysis of the previously identified CpGs. Column variables represent Name; chr; chromosome number, pos; position, CpG name, relation_to_Island; where is CpG located in relationship to island, UCSC RefGene Name; UCSC gene name, UCSC RefGene Accession; UCSC gene accession, UCSC RefGene Group; where in respect to gene is CpG located, Beta; estimated coefficient beta from the model, StandardError; standard error, Z; z-score, LRT; likelihood ratio...

DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome

Martina Bradic, Sarah Taleb, Binitha Thomas, Omar Chidiac, Amal Robay, Nessiya Hassan, Joel Malek, Ali Ait Hssain & Charbel Abi Khalil
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...

Additional file 3 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 3: Propagation walkscores of 127 proxy protein-coding genes in the TCGA-BRCA cohort.

Additional file 4 of Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Shimaa Sherif, Raghvendra Mall, Hossam Almeer, Adviti Naik, Abdulaziz Al Homaid, Remy Thomas, Jessica Roelands, Sathiya Narayanan, Mahmoud Gasim Mohamed, Shahinaz Bedri, Salha Bujassoum Al-Bader, Kulsoom Junejo, Davide Bedognetti, Wouter Hendrickx & Julie Decock
Additional file 4: Canonical pathways, diseases and functions associated with the 127 proxy protein-coding genes.

Registration Year

  • 2022
    48

Resource Types

  • Dataset
    22
  • Text
    20
  • Collection
    6

Affiliations

  • Hamad bin Khalifa University
    48
  • Weill Cornell Medical College in Qatar
    48
  • Hamad Medical Corporation
    44
  • Cornell University
    28
  • Centre Hospitalier Universitaire de Clermont-Ferrand
    24
  • Memorial Sloan Kettering Cancer Center
    24
  • Sidra Medical and Research Center
    24
  • University of Genoa
    20
  • Hamad General Hospital
    20
  • Qatar Foundation
    20