The top REACTOME pathways enriched in differentially expressed genes from SJS/TEN active lesions and ranked by FDR (FDR<0.25).

<p>The enrichment score is computed by DAVID on the 200 DEGs from Chung et al. Abbreviations: #GENES (number of DEGs in the pathway), PV (p-value, Fisher Exact test), FE (Fold Enrichment), FDR (false discovery rate). A (*) next to a pathway name indicates that the pathway was found to be enriched by both Pointer and DAVID.</p><p>The top REACTOME pathways enriched in differentially expressed genes from SJS/TEN active lesions and ranked by FDR (FDR<0.25).</p

QQ-plot (panel A) and GSEA plots (panel B) of the KEGG ABC transporter pathway.

<p>The QQ-plot is constructed using the genotyped SNPs whose snp-map contains at least one ABC transporter pathway gene. The GSEA plot shows the enrichment score of the ABC transporter pathway. The top portion of the plot shows the running enrichment score for the pathway genes as the analysis moves down the ranked list. The peak score is the enrichment score for the gene set. The bottom portion of the plot shows the value of the ranking metric as it moves down the list of ranked genes. The plots for the other two enriched pathways (Proteasome and Propanoate metabolism) look similar (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131038#pone.0131038.s002" target="_blank">S2 Fig</a>).</p

The top enriched KEGG pathways for low risk genetic variants ranked by normalized enrichment score (NES).

<p>Abbreviations: #GSS (gene set size for the pathway); #GLE (number of pathway genes in GSEA leading edge); ES (enrichment score), NES (normalized enrichment score), PV (p-value of ES), FDR (false discovery rate).</p><p>The top enriched KEGG pathways for low risk genetic variants ranked by normalized enrichment score (NES).</p

Comparison of False Discovery Rates from the pathway analysis performed with three SNP-to-gene mapping strategies.

<p>Abbreviations: FDR1 (FDR from the pathway analysis performed using physical distance only), FDR2 (FDR from the pathway analysis performed using LD-reconstruction), FDR3 (FDR from the pathway analysis performed with Pointer).</p><p>Comparison of False Discovery Rates from the pathway analysis performed with three SNP-to-gene mapping strategies.</p

The top KEGG pathways enriched in differentially expressed genes from SJS/TEN active lesions and ranked by FDR (FDR<0.25).

<p>The enrichment score is computed by DAVID on the 200 DEGs from Chung et al. Abbreviations: #GENES (number of DEGs in the pathway), PV (p-value, Fisher Exact test), FE (Fold Enrichment), FDR (false discovery rate). A (*) next to a pathway name indicates that the pathway was found to be enriched by both Pointer and DAVID.</p><p>The top KEGG pathways enriched in differentially expressed genes from SJS/TEN active lesions and ranked by FDR (FDR<0.25).</p

Enriched Reactome pathways ranked by FDR.

<p>In capital letters, pathways that contain KEGG proteasome genes.</p><p>Abbreviations: GSS (pathway gene set size); GLE (number of genes in leading edge); ES (Enrichment score), NES (Normalized Enrichment score), PV (p-value), FDR (false discovery rate), SD (p-value of shadow analysis against KEGG proteasome pathway)</p><p>Enriched Reactome pathways ranked by FDR.</p

Hierarchical clustering of expression profiles for genes in the KEGG proteasome pathway.

<p>The Hierarchical clustering analysis was performed on gene expression data from Chung et al., 2008. Individual gene-related signals are increased (red), unchanged (white), or decreased (blue). The analysis clearly separates cases (brown group) from controls (light blue group) and reveals the up-regulation of proteasome genes in the SJS/TEN lesions.</p

Key steps of the Pointer algorithm.

<p>Key steps of the Pointer algorithm.</p

Master transcription regulators and transcription factors regulate immune-associated differences between patients of African and European ancestry with colorectal cancer

BACKGROUND AND AIMS: Individuals of African (AFR) ancestry have a higher incidence of colorectal cancer (CRC) than those of European (EUR) ancestry and exhibit significant health disparities. Previous studies have noted differences in the tumor microenvironment between AFR and EUR patients with CRC. However, the molecular regulatory processes that underpin these immune differences remain largely unknown.  METHODS: Multiomics analysis was carried out for 55 AFR and 456 EUR patients with microsatellite-stable CRC using The Cancer Genome Atlas. We evaluated the tumor microenvironment by using gene expression and methylation data, transcription factor, and master transcriptional regulator analysis to identify the cell signaling pathways mediating the observed phenotypic differences. RESULTS: We demonstrate that downregulated genes in AFR patients with CRC showed enrichment for canonical pathways, including chemokine signaling. Moreover, evaluation of the tumor microenvironment showed that cytotoxic lymphocytes and neutrophil cell populations are significantly decreased in AFR compared with EUR patients, suggesting AFR patients have an attenuated immune response. We further demonstrate that molecules called “master transcriptional regulators” (MTRs) play a critical role in regulating the expression of genes impacting key immune processes through an intricate signal transduction network mediated by diseaseassociated transcription factors (TFs). Furthermore, a core set of these MTRs and TFs showed a positive correlation with levels of cytotoxic lymphocytes and neutrophils across both AFR and EUR patients with CRC, thus suggesting their role in driving the immune infiltrate differences between the two ancestral groups.  CONCLUSION: Our study provides an insight into the intricate regulatory landscape of MTRs and TFs that orchestrate the differences in the tumor microenvironment between patients with CRC of AFR and EUR ancestry.</p

Master transcription regulators and transcription factors regulate immune-associated differences between patients of African and European ancestry with colorectal cancer

BACKGROUND AND AIMS: Individuals of African (AFR) ancestry have a higher incidence of colorectal cancer (CRC) than those of European (EUR) ancestry and exhibit significant health disparities. Previous studies have noted differences in the tumor microenvironment between AFR and EUR patients with CRC. However, the molecular regulatory processes that underpin these immune differences remain largely unknown.  METHODS: Multiomics analysis was carried out for 55 AFR and 456 EUR patients with microsatellite-stable CRC using The Cancer Genome Atlas. We evaluated the tumor microenvironment by using gene expression and methylation data, transcription factor, and master transcriptional regulator analysis to identify the cell signaling pathways mediating the observed phenotypic differences. RESULTS: We demonstrate that downregulated genes in AFR patients with CRC showed enrichment for canonical pathways, including chemokine signaling. Moreover, evaluation of the tumor microenvironment showed that cytotoxic lymphocytes and neutrophil cell populations are significantly decreased in AFR compared with EUR patients, suggesting AFR patients have an attenuated immune response. We further demonstrate that molecules called “master transcriptional regulators” (MTRs) play a critical role in regulating the expression of genes impacting key immune processes through an intricate signal transduction network mediated by diseaseassociated transcription factors (TFs). Furthermore, a core set of these MTRs and TFs showed a positive correlation with levels of cytotoxic lymphocytes and neutrophils across both AFR and EUR patients with CRC, thus suggesting their role in driving the immune infiltrate differences between the two ancestral groups.  CONCLUSION: Our study provides an insight into the intricate regulatory landscape of MTRs and TFs that orchestrate the differences in the tumor microenvironment between patients with CRC of AFR and EUR ancestry.</p