Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci.
ABSTRACT: BACKGROUND:Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis. METHODS:All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21?693 controls; independent from discovery), and combined (9627 cases/30?845 controls; including additional individuals). RESULTS:The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation. CONCLUSIONS:Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.
Project description:Genome-wide association studies have identified several risk associations for ovarian carcinomas but not for mucinous ovarian carcinomas (MOCs). Our analysis of 1,644 MOC cases and 21,693 controls with imputation identified 3 new risk associations: rs752590 at 2q13 (P = 3.3 × 10(-8)), rs711830 at 2q31.1 (P = 7.5 × 10(-12)) and rs688187 at 19q13.2 (P = 6.8 × 10(-13)). We identified significant expression quantitative trait locus (eQTL) associations for HOXD9 at 2q31.1 in ovarian (P = 4.95 × 10(-4), false discovery rate (FDR) = 0.003) and colorectal (P = 0.01, FDR = 0.09) tumors and for PAX8 at 2q13 in colorectal tumors (P = 0.03, FDR = 0.09). Chromosome conformation capture analysis identified interactions between the HOXD9 promoter and risk-associated SNPs at 2q31.1. Overexpressing HOXD9 in MOC cells augmented the neoplastic phenotype. These findings provide the first evidence for MOC susceptibility variants and insights into the underlying biology of the disease.
Project description:Plasma exosomal miRNAs were evaluated for prognosis in an initial set of 44 metastatic renal cell cancer (mRCC) patients by RNA sequencing. Among ?3.49 million mappable reads per patient, miRNAs accounted for 93.1% of the mapped RNAs. 227 miRNAs with high abundance were selected for survival analysis. Cox regression analysis identified association of 6 miRNAs with overall survival (OS) (P<0.01, False discovery rate (FDR) < 0.3). Five of the associated miRNAs were quantified in an independent follow-up cohort of 65 mRCC patients by TaqMan-based miRNA assays. Kaplan-Meier analysis confirmed the significant OS association of three miRs; miR-let-7i-5p (P=0.018, HR=0.49, 95% CI=0.21-0.84), miR-26a-1-3p (P=0.025, HR=0.43, 95% CI=0.10-0.84) and miR-615-3p (P=0.0007, HR=0.36, 95% CI=0.11-0.54). A multivariate analysis of miR-let-7i-5p with the clinical factor-based Memorial Sloan-Kettering Cancer Center (MSKCC) score improved survival prediction from an area under the curve (AUC) of 0.58 for MSKCC score to an average AUC of 0.64 across 48-month follow-up time. The multivariate model was able to define a high-risk group with median survival of 14 months and low risk group of 39 months (P=0.0002, HR=3.43, 95%CI, 2.73-24.15). Further validation of miRNA-based prognostic biomarkers are needed to improve current clinic-pathologic based prognostic models in patients with mRCC.
Project description:Pathway-based analysis as an alternative and effective approach to identify disease-related genes or loci has been verified. To decipher the genetic background of plasma adiponectin levels, we performed genome wide pathway-based association studies in extremely obese individuals and normal-weight controls. The modified Gene Set Enrichment Algorithm (GSEA) was used to perform the pathway-based analyses (the GenGen Program) in 746 European American females, which were collected from our previous GWAS in extremely obese (BMI?>?35?kg/m(2)) and never-overweight (BMI<25?kg/m(2)) controls. Rac1 cell motility signaling pathway was associated with plasma adiponectin after false-discovery rate (FDR) correction (empirical P?<?0.001, FDR?=?0.008, family-wise error rate?=?0.008). Other several Rac1-centered pathways, such as cdc42racPathway (empirical P?<?0.001), hsa00603 (empirical P?=?0.003) were among the top associations. The RAC1 pathway association was replicated by the ICSNPathway method, yielded a FDR?=?0.002. Quantitative pathway analyses yielded similar results (empirical P?=?0.001) for the Rac1 pathway, although it failed to pass the multiple test correction (FDR?=?0.11). We further replicated our pathway associations in the ADIPOGen Consortium data by the GSA-SNP method. Our results suggest that Rac1 and related cell motility pathways might be associated with plasma adiponectin levels and biological functions of adiponectin.
Project description:Background:Genome-wide association studies (GWAS) have identified numerous loci associated with diseases and traits. However, the elucidation of disease mechanisms followed by drug development has remained a challenge owing to complex gene interactions. We performed pathway analysis with MAGENTA (Meta-Analysis Geneset Enrichment of variaNT Associations) to clarify the pathways in genetic background of AF. Methods:The existing GWAS data were analyzed using MAGENTA. A microarray analysis was then performed for the identified pathways with human atrial tissues, followed by Gene-Set Enrichment Analysis (GSEA). Results:MAGENTA identified two novel candidate pathways for AF pathogenesis, the CTCF (CCCTC-binding factor, p?=?1.00?×?10-4, FDR q?=?1.64?×?10-2) and mTOR pathways (mammalian target of rapamycin, p?=?3.00?×?10-4, FDR q?=?3.13?×?10-2). The microarray analysis with human atrial tissue using the GSEA indicated that the mTOR pathway was suppressed in AF cases compared with non-AF cases, validating the MAGENTA results, but not CTCF pathway. Conclusions:MAGENTA identified a novel pathway, mTOR, followed by GSEA with human atrial tissue samples. mTOR pathway is a key interface that adapts the change of environments by pressure overload and metabolic perturbation. Our results indicate that the MTOR pathway is involved in the pathogenesis of AF, although the details of the basic mechanism remain unknown and further analysis for causal-relationship of mTOR pathway to AF is required. CTCF pathway is essential for construction of chromatin structure and transcriptional process. The gene-set components of CTCF overlap with those of mTOR in Biocarta.
Project description:Several lines of evidence suggest that genome-wide association studies (GWASs) have the potential to explain more of the "missing heritability" of common complex phenotypes. However, reliable methods for identifying a larger proportion of SNPs are currently lacking. Here, we present a genetic-pleiotropy-informed method for improving gene discovery with the use of GWAS summary-statistics data. We applied this methodology to identify additional loci associated with schizophrenia (SCZ), a highly heritable disorder with significant missing heritability. Epidemiological and clinical studies suggest comorbidity between SCZ and cardiovascular-disease (CVD) risk factors, including systolic blood pressure, triglycerides, low- and high-density lipoprotein, body mass index, waist-to-hip ratio, and type 2 diabetes. Using stratified quantile-quantile plots, we show enrichment of SNPs associated with SCZ as a function of the association with several CVD risk factors and a corresponding reduction in false discovery rate (FDR). We validate this "pleiotropic enrichment" by demonstrating increased replication rate across independent SCZ substudies. Applying the stratified FDR method, we identified 25 loci associated with SCZ at a conditional FDR level of 0.01. Of these, ten loci are associated with both SCZ and CVD risk factors, mainly triglycerides and low- and high-density lipoproteins but also waist-to-hip ratio, systolic blood pressure, and body mass index. Together, these findings suggest the feasibility of using genetic-pleiotropy-informed methods for improving gene discovery in SCZ and identifying potential mechanistic relationships with various CVD risk factors.
Project description:BACKGROUND:SARS-CoV-2 coinfection with other viral and bacterial pathogens and their interactions are increasingly recognized in the literature as potential determinants of COVID-19 phenotypes. The aim of this study was to determine infection induced, host transcriptomic overlap between SARS-CoV-2 and other pathogens. MATERIALS AND METHODS:SARS-CoV-2 infection induced gene expression data were used for gene set enrichment analysis (GSEA) via the Enrichr platform. GSEA compared the extracted signature to VirusMINT, Virus and Microbe perturbations from Gene Expression Omnibus (GEO) in order to detect overlap with other pathogen induced host gene signatures. For all analyses, a false discovery rate (FDR) <0.05 was considered statistically significant. RESULTS:GSEA via Enrichr revealed several significantly enriched sub-signatures associated with HSV1, EBV, HIV1, IAV, RSV, P.Aeruginosa, Staph. Aureus and Strep. Pneumoniae infections, among other pathogens (FDR < 0.05). These signatures were detected in at least 6 infection-induced transcriptomic studies from GEO and involved both bronchial epithelial and peripheral blood immune cells. DISCUSSION:SARS-CoV-2 infection may function synergistically with other viral and bacterial pathogens at the transcriptomic level. Notably, several meta-analyses of COVID-19 cohorts have furthermore corroborated viral and bacterial pathogens reported herein as coinfections with SARS-CoV-2. The identification of common, perturbed gene networks outlines a common host targetome for these pathogens, and furthermore provides candidates for biomarker discovery and drug design.
Project description:Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the "missing heritability" of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q-Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders.
Project description:A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori to have similar biologic function, associated with the outcome.In this paper, we propose a general permutation-based framework for gene set testing that controls the false discovery rate (FDR) while accounting for the dependency among the genes within and across each gene set. The application of the proposed method is demonstrated using three public microarray data sets. The performance of our proposed method is contrasted to two other existing Gene Set Enrichment Analysis (GSEA) and Gene Set Analysis (GSA) methods.Our simulations show that the proposed method controls the FDR at the desired level. Through simulations and case studies, we observe that our method performs better than GSEA and GSA, especially when the number of prognostic gene sets is large.
Project description:Inherited BRCA1 mutations confer elevated cancer risk. Recent studies have identified genes that encode proteins that interact with BRCA1 as modifiers of BRCA1-associated breast cancer. We evaluated a comprehensive set of genes that encode most known BRCA1 interactors to evaluate the role of these genes as modifiers of cancer risk. A cohort of 2,825 BRCA1 mutation carriers was used to evaluate the association of haplotypes at ATM, BRCC36, BRCC45 (BRE), BRIP1 (BACH1/FANCJ), CTIP, ABRA1 (FAM175A), MERIT40, MRE11A, NBS1, PALB2 (FANCN), RAD50, RAD51, RAP80, and TOPBP1, and was associated with time to breast and ovarian cancer diagnosis. Statistically significant false discovery rate (FDR) adjusted P values for overall association of haplotypes (P(FDR)) with breast cancer were identified at ATM (P(FDR) = 0.029), BRCC45 (P(FDR) = 0.019), BRIP1 (P(FDR) = 0.008), CTIP (P(FDR) = 0.017), MERIT40 (P(FDR) = 0.019), NBS1 (P(FDR) = 0.003), RAD50 (P(FDR) = 0.014), and TOPBP1 (P(FDR) = 0.011). Haplotypes at ABRA1 (P(FDR) = 0.007), BRCC45 (P(FDR) = 0.016 and P(FDR) = 0.005 in two haplotype blocks), and RAP80 (P(FDR) < 0.001) were associated with ovarian cancer risk. Overall, the data suggest that genomic variation at multiple loci that encode proteins that interact biologically with BRCA1 are associated with modified breast cancer and ovarian cancer risk in women who carry BRCA1 mutations.
Project description:Ovarian cancer is a fatal gynaecological malignancy in women worldwide, and serous ovarian cancer (SOC) is considered the most common histological subtype of this malignancy. Thus, the present study aimed to identify the core genes for SOC via bioinformatics analysis. The GSE18520 and GSE14407 datasets were downloaded from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) and perform gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify the core genes, while The Cancer Genome Atlas (TCGA) database was used to screen for prognosis-associated DEGs. Furthermore, clinical samples were collected for further validation of kinesin family member 11 (KIF11) gene. In the GEO analysis, a total of 198 DEGs were identified, including 81 upregulated and 117 downregulated genes compared SOC to normal tissue. GSEA across the two datasets demonstrated that 16 gene sets, including those involved in the cell cycle and DNA replication, were notably associated with SOC. A PPI network of the DEGs was constructed with 130 nodes and 387 edges. Subsequently, 20 core genes involved in the same top-ranked module were filtered out by submodule analysis. Survival analysis identified three predictive genes for SOC prognosis, including KIF11, CLDN3 and FGF13. KIF11 was identified as a core and predictive gene and thus was further validated using clinical samples. The results demonstrated that KIF11 was upregulated in tumour tissues compared with adjacent normal tissues and was associated with aggressive factors, including tumour grade, TNM stage and lymph node invasion. In conclusions, the present study identified the core genes and gene sets for SOC, thus extending the understanding of SOC occurrence and progression. Furthermore, KIF11 was identified as a promising tumour-promoting gene and a potential target for the diagnosis and treatment of SOC.