Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women

ETS-1 in tumor immunology: implications for novel anti-cancer strategies

ETS-1, a key member of the Erythroblast Transformation-Specific (ETS) transcription factor family, plays an important role in cell biology and medical research due to its wide expression profile and strong transcriptional regulation ability. It regulates fundamental biological processes, including cell proliferation, differentiation, and apoptosis, and is involved in tumorigenesis and metastasis, promoting malignant behaviors such as angiogenesis, matrix degradation, and cell migration. Given the association between ETS-1 overexpression and the aggressive characteristics of multiple malignancies, it represents a promising therapeutic target in cancer treatment. This study aims to systematically analyze the role of ETS-1 within the tumor immune microenvironment, elucidating its mechanisms in cancer initiation, progression, and metastasis. It also investigates the differential expression of ETS-1 across tumor tissues and adjacent normal tissues, exploring its potential as a molecular marker for tumor diagnosis and prognosis.

Single nucleotide polymorphisms in ovarian cancer impacting lipid metabolism and prognosis: an integrated TCGA database analysis

Ovarian cancer (OC) stands as a formidable adversary among women, remaining a leading cause of cancer-related mortality owing to its aggressive and invasive nature. Investigating prognostic markers intricately linked to OC's molecular pathogenesis represents a critical avenue for enhancing patient outcomes and survival prospects. In this comprehensive study, we embarked on a bioinformatics journey, leveraging the vast repository of single nucleotide polymorphism (SNP) data from OC patients available within the TCGA database. Our overarching goal was to unearth the genetic underpinnings of OC, shedding light on potential prognostic markers that could significantly impact clinical decision-making and patient care. Our meticulous analysis led to the discovery of five mutated genes-APOB, BRCA1, COL6A3, LRP1, and LRP1B-engaged in the intricate world of lipid metabolism. These genes, previously unexplored in the context of OC, emerged as prominent figures in our investigation, showcasing their potential roles in OC progression. The intricate interplay between lipid metabolism and cancer development has garnered considerable attention in recent years, and our findings underscore the relevance of these genes in the context of OC. To fortify our discoveries, we delved into the realm of survival analysis, a pivotal component of our investigation. The results yielded compelling evidence of significant correlations between patient survival and the expression levels of the aforementioned genes. This critical insight underscores the potential utility of these genes as prognostic markers, illuminating a path toward more personalized and effective approaches to patient care. Our study represents a multifaceted approach to unraveling the complex molecular pathogenesis of OC. By harnessing the power of high-throughput data mining, we uncovered genetic insights that may reshape our understanding of this formidable disease. We complemented these findings with advanced techniques such as RT-qPCR and Western blot, further dissecting the intricacies of OC's molecular landscape. This holistic approach not only deepens our understanding but also provides essential bioinformatics information that holds promise in assessing patient prognosis. In summary, our study represents a significant stride in the quest to decode the molecular intricacies of ovarian cancer. Our findings spotlight the potential prognostic significance of APOB, BRCA1, COL6A3, LRP1, and LRP1B, inviting further exploration into their roles in OC progression. Ultimately, our research carries the potential to shape the future of OC management, offering a glimpse into a more personalized and effective approach to patient care.

SUGT1 regulates the progression of ovarian cancer through the AKT/PI3K/mTOR signaling pathway

This study investigates the expression and functional roles of SUGT1 in ovarian cancer, utilizing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects. Our analyses reveal that SUGT1 is significantly upregulated in ovarian cancer tissues compared to normal controls. We further explore the prognostic value of SUGT1, where elevated expression correlates with poorer patient outcomes, particularly in ovarian cancer. The functional implications of SUGT1 in cancer biology were assessed through in vitro and in vivo experiments. Gene Set Enrichment Analysis (GSEA) indicates a significant association between high SUGT1 expression and the activation of glycolytic pathways, suggesting a potential role in metabolic reprogramming. Inhibition of SUGT1 via siRNA in ovarian cancer cell lines results in decreased proliferation and increased apoptosis, along with reduced migration and invasion capabilities. Additionally, our study identifies the transcription factor ELF1 as a significant regulator of SUGT1 expression. Through promoter analysis and chromatin immunoprecipitation, we demonstrate that ELF1 directly binds to the SUGT1 promoter, enhancing its transcription. This regulatory mechanism underscores the importance of transcriptional control in cancer metabolism, providing insights into potential therapeutic targets. Our findings establish SUGT1 as a crucial player in the oncogenic processes of ovarian cancer, influencing both metabolic pathways and transcriptional regulation. This highlights its potential as a biomarker and therapeutic target in managing ovarian cancer.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

MotifbreakR v2: extended capability and database integration

MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.

Association of ESR1 Germline Variants with TP53 Somatic Variants in Breast Tumors in a Genome-wide Study

In breast tumors, somatic mutation frequencies in TP53 and PIK3CA vary by tumor subtype and ancestry. Emerging data suggest tumor mutation status is associated with germline variants and genetic ancestry. We aimed to identify germline variants that are associated with somatic TP53 or PIK3CA mutation status in breast tumors. A genome-wide association study was conducted in 2,850 women of European ancestry with breast cancer using TP53 and PIK3CA mutation status (positive or negative) as well as specific functional categories [e.g., TP53 gain-of-function (GOF) and loss-of-function, PIK3CA activating] as phenotypes. Germline variants showing evidence of association were selected for validation analyses and tested in multiple independent datasets. Discovery association analyses found five variants associated with TP53 mutation status with P values <1 × 10-6 and 33 variants with P values <1 × 10-5. Forty-four variants were associated with PIK3CA mutation status with P values <1 × 10-5. In validation analyses, only variants at the ESR1 locus were associated with TP53 mutation status after multiple comparisons corrections. Combined analyses in European and Malaysian populations found ESR1 locus variants rs9383938 and rs9479090 associated with the presence of TP53 mutations overall (P values 2 × 10-11 and 4.6 × 10-10, respectively). rs9383938 also showed association with TP53 GOF mutations (P value 6.1 × 10-7). rs9479090 showed suggestive evidence (P value 0.02) for association with TP53 mutation status in African ancestry populations. No other variants were significantly associated with TP53 or PIK3CA mutation status. Larger studies are needed to confirm these findings and determine if additional variants contribute to ancestry-specific differences in mutation frequency.

SIGNIFICANCE

Emerging data show ancestry-specific differences in TP53 and PIK3CA mutation frequency in breast tumors suggesting that germline variants may influence somatic mutational processes. This study identified variants near ESR1 associated with TP53 mutation status and identified additional loci with suggestive association which may provide biological insight into observed differences.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Fine-scale mapping of chromosome 9q22.33 identifies candidate causal variant in ovarian cancer

Ovarian cancer is a complex polygenic disease in which genetic factors play a significant role in disease etiology. A genome-wide association study (GWAS) identified a novel variant on chromosome 9q22.33 as a susceptibility locus for epithelial ovarian cancer (EOC) in the Han Chinese population. However, the underlying mechanism of this genomic region remained unknown. In this study, we conducted a fine-mapping analysis of 130 kb regions, including 1,039 variants in 200 healthy women. Ten variants were selected to evaluate the association with EOC risk in 1,099 EOC cases and 1,591 controls. We identified two variants that were significantly associated with ovarian cancer risk (rs7027650, P = 1.91 × 10-7; rs1889268, P = 3.71 × 10-2). Expression quantitative trait locus (eQTL) analysis found that rs7027650 was significantly correlated with COL15A1 gene expression (P = 0.009). The Luciferase reporter gene assay confirmed that rs7027650 could interact with the promoter region of COL15A1, reducing its activity. An electrophoretic mobility shift assay (EMSA) showed the allele-specific binding capacity of rs7027650. These findings revealed that rs7027650 could be a potential causal variant at 9q22.33 region and may regulate the expression level of COL15A1. This study offered insight into the molecular mechanism behind a potential causal variant that affects the risk of ovarian cancer.

Association of ESR1 germline variants with TP53 somatic variants in breast tumors in a genome-wide study

Background

In breast tumors, somatic mutation frequencies in TP53 and PIK3CA vary by tumor subtype and ancestry. HER2 positive and triple negative breast cancers (TNBC) have a higher frequency of TP53 somatic mutations than other subtypes. PIK3CA mutations are more frequently observed in hormone receptor positive tumors. Emerging data suggest tumor mutation status is associated with germline variants and genetic ancestry. We aimed to identify germline variants that are associated with somatic TP53 or PIK3CA mutation status in breast tumors.

Methods

A genome-wide association study was conducted using breast cancer mutation status of TP53 and PIK3CA and functional mutation categories including TP53 gain of function (GOF) and loss of function mutations and PIK3CA activating/hotspot mutations. The discovery analysis consisted of 2850 European ancestry women from three datasets. Germline variants showing evidence of association with somatic mutations were selected for validation analyses based on predicted function, allele frequency, and proximity to known cancer genes or risk loci. Candidate variants were assessed for association with mutation status in a multi-ancestry validation study, a Malaysian study, and a study of African American/Black women with TNBC.

Results

The discovery Germline x Mutation (GxM) association study found five variants associated with one or more TP53 phenotypes with P values <1×10-6, 33 variants associated with one or more TP53 phenotypes with P values <1×10-5, and 44 variants associated with one or more PIK3CA phenotypes with P values <1×10-5. In the multi-ancestry and Malaysian validation studies, germline ESR1 locus variant, rs9383938, was associated with the presence of TP53 mutations overall (P values 6.8×10-5 and 9.8×10-8, respectively) and TP53 GOF mutations (P value 8.4×10-6). Multiple variants showed suggestive evidence of association with PIK3CA mutation status in the validation studies, but none were significant after correction for multiple comparisons.

Conclusions

We found evidence that germline variants were associated with TP53 and PIK3CA mutation status in breast cancers. Variants near the estrogen receptor alpha gene, ESR1, were significantly associated with overall TP53 mutations and GOF mutations. Larger multi-ancestry studies are needed to confirm these findings and determine if these variants contribute to ancestry-specific differences in mutation frequency.

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

Design and quality control of large-scale two-sample Mendelian randomization studies

Background

Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors.

Methods

We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set.

Results

We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls.

Conclusions

In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Effect of CX3CL1/CX3CR1 gene polymorphisms on the clinical efficacy of carboplatin therapy in Han patients with ovarian cancer

Gene polymorphisms have a close relationship with the clinical effects of carboplatin for ovarian cancer. Here, we investigated the relationship between CX3CL1 and CX3CR1 genotypes and the clinical efficacy of carboplatin in ovarian cancer, thereby clarifying the unidentified genetic factors that influence the efficacy of carboplatin in ovarian cancer. Based on the above purposes, we used Sequenom Mass ARRAY technology to detect CX3CL1 and CX3CR1 gene polymorphisms in 127 patients with carboplatin-treated ovarian cancer. We performed various statistical analyses to evaluate the effects of CX3CL1 and CX3CR1 genetic variants, demographic data, and clinical characteristics on the effect of carboplatin therapy. The results show that the CX3CL1 genotypes rs223815 (G>C) and rs682082 (G>A) will significantly affect the clinical efficacy of carboplatin for ovarian cancer (p < 0.05), while the other six genotypes and all CX3CR1 genotypes have no significant effect (p > 0.05). In addition, only one population factor, age, had a significant effect on the clinical efficacy of carboplatin-treated ovarian cancer (p < 0.05). Based on the above research results, we concluded that the clinical efficacy of carboplatin in ovarian cancer patients was significantly correlated with age and CX3CL1 polymorphism factors; however, more in-depth effects and mechanisms need to be explored by large-scale, multicenter studies.

ABO blood groups and expression of blood group antigens of epithelial ovarian cancer in Chinese women

BACKGROUND

ABO blood groups has been associated with risk of several cancers; however, the results for an association with ovarian cancer are inconsistent and little is known about the expression of histo-blood group (ABH) antigens and ABO gene in ovarian tumor tissues.

METHODS

To assess the impact of genotype-derived ABO blood types on the risk of EOC, we conducted a case-control study in 1,870 EOC and 4,829 controls. Expression of A and B antigen in 70 pairs of ovarian tumor tissues and adjacent normal tissues were detected by immunohistochemistry. Gene expression and DNA methylation profiling was conducted in ovarian tumor tissues.

RESULTS

We identified that blood group A was associated with increased risk for EOC compared to blood group O (OR = 1.18, 95% CI = 1.03-1.36, p = 0.019). Increased frequency of aberrant expression of histo-blood group antigens was observed in patients with blood group A (76.5%) compared to patients with blood group O (21.1%) and B (5.0%) by immunohistochemistry (p < 0.001). ABO gene expression was down-regulated in ovarian tumor tissues compared with paired adjacent normal tissues (p = 0.027). In addition, ABO gene expression was positively correlated with NFYB (r = 0.38, p < 0.001) and inversely correlated with DNA methylation level of four CpG sites on ABO gene (cg11879188, r = - 0.3, p = 0.002; cg22535403, r = - 0.30, p = 0.002; cg13506600, r = - 0.22, p = 0.025; cg07241568, r = - 0.21, p = 0.049) in ovarian tumor tissues.

CONCLUSION

We identified blood group A was associated with increased EOC risk in Chinese women and provided the clues of the possible molecular mechanisms of blood group A related to ovarian cancer risk.

Understanding How Pregnancy Protects Against Ovarian and Endometrial Cancer Development: Fetal Antigens May Be Involved

It is well known that many factors, including infertility, obesity, type 2 diabetes, and family history of cancer, increase the risk of developing endometrial and ovarian cancer. However, multiparous women are known to have a lower risk of developing either ovarian or endometrial cancer than nonparous women. The lack of ovulation and shifting of sex hormonal balance, with decreased estrogen levels and increased progesterone levels during pregnancy, has traditionally been thought to be the major contributor to this decreased risk. However, in reality, the mechanisms underlying this phenomenon are relatively unknown. Increasing evidence suggests that endocrine factors are unlikely to completely explain the protective effect of pregnancies, and that multiple other nonendocrine mechanisms including fetal antigens and the newly proposed dormant cells hypothesis may also be involved. In this review, we summarize recent evidence and describe the potential underlying mechanisms that may explain how pregnancy protects against the development of ovarian and endometrial cancers in women's later life.

Genome-Wide Association Study of Fluorescent Oxidation Products Accounting for Tobacco Smoking Status in Adults from the French EGEA Study

Oxidative stress (OS) is the main pathophysiological mechanism involved in several chronic diseases, including asthma. Fluorescent oxidation products (FlOPs), a global biomarker of damage due to OS, is of growing interest in epidemiological studies. We conducted a genome-wide association study (GWAS) of the FlOPs level in 1216 adults from the case-control and family-based EGEA study (mean age 43 years old, 51% women, and 23% current smokers) to identify genetic variants associated with FlOPs. The GWAS was first conducted in the whole sample and then stratified according to smoking status, the main exogenous source of reactive oxygen species. Among the top genetic variants identified by the three GWAS, those located in BMP6 (p = 3 × 10⁻⁶), near BMPER (p = 9 × 10⁻⁶), in GABRG3 (p = 4 × 10⁻⁷), and near ATG5 (p = 2 × 10⁻⁹) are the most relevant because of both their link to biological pathways related to OS and their association with several chronic diseases for which the role of OS in their pathophysiology has been pointed out. BMP6 and BMPER are of particular interest due to their involvement in the same biological pathways related to OS and their functional interaction. To conclude, this study, which is the first GWAS of FlOPs, provides new insights into the pathophysiology of chronic OS-related diseases.

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Genome-wide association and functional interrogation identified a variant at 3p26.1 modulating ovarian cancer survival among Chinese women

Ovarian cancer survival varies considerably among patients, to which germline variation may also contribute in addition to mutational signatures. To identify genetic markers modulating ovarian cancer outcome, we performed a genome-wide association study in 2130 Chinese ovarian cancer patients and found a hitherto unrecognized locus at 3p26.1 to be associated with the overall survival (P_combined = 8.90 × 10⁻¹⁰). Subsequent statistical fine-mapping, functional annotation, and eQTL mapping prioritized a likely casual SNP rs9311399 in the non-coding regulatory region. Mechanistically, rs9311399 altered its enhancer activity through an allele-specific transcription factor binding and a long-range interaction with the promoter of a lncRNA BHLHE40-AS1. Deletion of the rs9311399-associated enhancer resulted in expression changes in several oncogenic signaling pathway genes and a decrease in tumor growth. Thus, we have identified a novel genetic locus that is associated with ovarian cancer survival possibly through a long-range gene regulation of oncogenic pathways.

ETV5-mediated upregulation of lncRNA CTBP1-DT as a ceRNA facilitates HGSOC progression by regulating miR-188-5p/MAP3K3 axis

High-grade serous ovarian cancer (HGSOC) is a common and lethal cancer of the female reproductive system. Long non-coding RNAs (lncRNAs) are aberrantly expressed in various cancers and play crucial roles in tumour progression. However, their function and molecular mechanism in HGSOC remain largely unknown. Based on public databases and bioinformatics analyses, the overexpression of lncRNA CTBP1-DT in HGSOC tissues was detected and validated in a cohort of HGSOC tissues. High expression of lncRNA CTBP1-DT was associated with poor prognosis and was an independent risk factor for survival. Overexpression of lncRNA CTBP1-DT promoted malignant biological behaviour of HGSOC cells, whereas its depletion induced growth arrest of HGSOC cells by vitro and in vivo assays. Mechanistically, lncRNA CTBP1-DT could competitively bind to miR-188-5p to protect MAP3K3 from degradation. Moreover, our results revealed that ETV5 could specifically interact with the promoter of lncRNA CTBP1-DT and activate its transcription. Collectively, these results reveal a novel ETV5/lncRNA CTBP1-DT/miR-188-5p/MAP3K3 pathway for HGSOC progression and suggest that lncRNA CTBP1-DT might be a potential biomarker and therapeutic target for HGSOC.

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

Genetically predicted circulating protein biomarkers and ovarian cancer risk

OBJECTIVE

Most women with epithelial ovarian cancer (EOC) are diagnosed after the disease has metastasized and survival in this group remains poor. Circulating proteins associated with the risk of developing EOC have the potential to serve as biomarkers for early detection and diagnosis. We integrated large-scale genomic and proteomic data to identify novel plasma proteins associated with EOC risk.

METHODS

We used the germline genetic variants most strongly associated (P <1.5 × 10-11) with plasma levels of 1329 proteins in 3301 healthy individuals from the INTERVAL study to predict circulating levels of these proteins in 22,406 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). Association testing was performed by weighting the beta coefficients and standard errors for EOC risk from the OCAC study by the inverse of the beta coefficients from INTERVAL.

RESULTS

We identified 26 proteins whose genetically predicted circulating levels were associated with EOC risk at false discovery rate < 0.05. The 26 proteins included MFAP2, SEMG2, DLK1, and NTNG1 and a group of 22 proteins whose plasma levels were predicted by variants at chromosome 9q34.2. All 26 protein association signals identified were driven by association with the high-grade serous histotype that comprised 58% of the EOC cases in OCAC. Regional genomic plots confirmed overlap of the genetic association signal underlying both plasma protein level and EOC risk for the 26 proteins. Pathway analysis identified enrichment of seven biological pathways among the 26 proteins (Padjusted <0.05), highlighting roles for Focal Adhesion-PI3K-Akt-mTOR and Notch signaling.

CONCLUSION

The identified proteins further illuminate the etiology of EOC and represent promising new EOC biomarkers for targeted validation by studies involving direct measurement of plasma proteins in EOC patient cohorts.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Clinical outcomes and safety of apatinib monotherapy in the treatment of patients with advanced epithelial ovarian carcinoma who progressed after standard regimens and the analysis of the VEGFR2 polymorphism

The aims of the present study were to investigate the clinical outcomes and safety of apatinib monotherapy in the treatment of patients with advanced epithelial ovarian carcinoma (EOC) who have progressed after standard regimens, and to analyze the vascular endothelial growth factor receptor 2 (VEGFR2) rs2071559 polymorphism. A total of 118 patients with advanced EOC who received apatinib treatment were included in the study. Tumor response was evaluated using progression-free survival (PFS) and overall survival (OS) time, and safety data were documented. Additionally, peripheral blood and peripheral blood mononuclear cell (PBMC) specimens from the patients with EOC were collected to perform the genotyping of genetic polymorphism and assess the mRNA expression of VEGFR2, respectively. The objective response rate across the 118 patients with advanced EOC was 38.98%, the disease control rate was 63.56%, the median PFS time was 4.65 months and the median OS time was 15.10 months. Regarding the polymorphism analysis, the prevalence of rs2071559 in VEGFR2 among the 118 patients with advanced EOC was recorded as the TT genotype in 72 cases (61.02%), TC genotype in 41 cases (34.75%) and CC genotype in 5 cases (4.23%), and the minor allele frequency of rs2071559 was 0.22. The distribution of the three genotypes was in accordance with the Hardy-Weinberg equilibrium (P=0.781). TC and CC genotypes were merged in the subsequent analysis. The prognosis analyses suggested that the median PFS time of patients with the TC/CC genotype and the TT genotype was 3.10 and 5.40 months, respectively (P=0.015). Moreover, the median OS time of the two genotypes was 12.60 and 17.50 months, respectively (P=0.009). However, no association was noted between genotype status of the polymorphism and adverse reactions. Additionally, the mRNA expression analysis indicated that the mRNA expression levels of VEGFR2 in PBMC specimens were significantly different between TT and TC/CC genotypes (P<0.001). The present study suggested that the clinical outcomes of patients with advanced EOC, who progressed after standard regimens and received apatinib treatment, might be influenced by the VEGFR2 rs2071559 polymorphism.

Identification of novel epithelial ovarian cancer loci in women of African ancestry

Women of African ancestry have lower incidence of epithelial ovarian cancer (EOC) yet worse survival compared to women of European ancestry. We conducted a genome-wide association study in African ancestry women with 755 EOC cases, including 537 high-grade serous ovarian carcinomas (HGSOC) and 1,235 controls. We identified four novel loci with suggestive evidence of association with EOC (p < 1 × 10-6 ), including rs4525119 (intronic to AKR1C3), rs7643459 (intronic to LOC101927394), rs4286604 (12 kb 3' of UGT2A2) and rs142091544 (5 kb 5' of WWC1). For HGSOC, we identified six loci with suggestive evidence of association including rs37792 (132 kb 5' of follistatin [FST]), rs57403204 (81 kb 3' of MAGEC1), rs79079890 (LOC105376360 intronic), rs66459581 (5 kb 5' of PRPSAP1), rs116046250 (GABRG3 intronic) and rs192876988 (32 kb 3' of GK2). Among the identified variants, two are near genes known to regulate hormones and diseases of the ovary (AKR1C3 and FST), and two are linked to cancer (AKR1C3 and MAGEC1). In follow-up studies of the 10 identified variants, the GK2 region SNP, rs192876988, showed an inverse association with EOC in European ancestry women (p = 0.002), increased risk of ER positive breast cancer in African ancestry women (p = 0.027) and decreased expression of GK2 in HGSOC tissue from African ancestry women (p = 0.004). A European ancestry-derived polygenic risk score showed positive associations with EOC and HGSOC in women of African ancestry suggesting shared genetic architecture. Our investigation presents evidence of variants for EOC shared among European and African ancestry women and identifies novel EOC risk loci in women of African ancestry.

Identification of Candidate Genes Associated with Susceptibility to Ovarian Clear Cell Adenocarcinoma Using cis-eQTL Analysis

Ovarian clear cell adenocarcinoma (Ov-CCA) has a higher prevalence in the Japanese ancestry than other populations. The ancestral disparities in Ov-CCA prevalence suggests the presence of Ov-CCA-specific genetic alterations and may provide an opportunity to identify the novel genes associated with Ov-CCA tumorigenesis. Using 94 previously reported genes as the phenotypic trait, we conducted multistep expression quantitative trait loci (eQTL) analysis with the HapMap3 project datasets. Four single-nucleotide polymorphisms (SNPs) (rs4873815, rs12976454, rs11136002, and rs13259097) that had different allele frequencies in the Japanese ancestry and seven genes associated in cis (APBA3, C8orf58, KIAA1967, NAPRT1, RHOBTB2, TNFRSF10B, and ZNF707) were identified. In silico functional annotation analysis and in vitro promoter assay validated the regulatory effect of rs4873815-TT on ZNF707 and rs11136002-TT on TNFRSF10B. Furthermore, ZNF707 was highly expressed in Ov-CCA and had a negative prognostic value in disease recurrence in our sample cohort. This prognostic power was consistently observed in The Cancer Genome Atlas (TCGA) clear cell renal cell carcinoma dataset, suggesting that ZNF707 may have prognostic value in clear cell histology regardless of tissue origin. In conclusion, rs4873815-TT/ZNF707 may have clinical significance in the prognosis and tumorigenesis of Ov-CCA, which may be more relevant to clear cell histology. Besides, this study may underpin the evidence that cis-eQTL analysis based on ancestral disparities can facilitate the discovery of causal genetic alterations in complex diseases, such as cancer.

Functional polymorphisms in FOXC2 gene and Epithelial ovarian Cancer susceptibility in Chinese population

Background

Epithelial ovarian cancer (EOC) is highly lethal gynecological cancer. Forkhead Box Protein C2 (FOXC2) promotes occurrence and development of various malignant tumors. The present study is aimed at exploring the correlation between the polymorphism of FOXC2 and epithelial ovarian cancer susceptibility in Chinese Han population.

Methods

A case-control design was used to verify the association between FOXC2 polymorphisms and epithelial ovarian cancer. The genotyping was performed using Taqman® SNP Genotyping kit by qRT-PCR. The genetic variants including rs3751794 C > T, rs1035550 A > G, rs4843163 C > G and rs4843396 C > T in FOXC2 gene were analyzed. The strength of the associations was detected using odds ratios and 95% confidence intervals. Stratification analyses showed the association between the FOXC2 gene polymorphisms rs3751794 C > T, rs4843163 C > G and rs4843396 C > T with epithelial ovarian cancer susceptibility in terms of age, metastasis status, clinical stage, pathological grade, pregnant times, pausimenia, and the expression of ER, PR, wild p53 and mutant p53.

Results

Rs3751794 C > T (P = 0.0016), rs4843163 C > G (P < 0.0001) and rs4843396 C > T (P < 0.0001) were significantly associated with increased epithelial ovarian cancer risk. In stratification analyses,rs3751794 C > T, was identified to be dominant in no metastasis patients, clinical stage 4 group, middle grade pathological stage, pregnant time over 3 patients, post-menopause women, strong wild type p53 expression; rs4843163 C > G was dominant in high grade clinical stage, high grade pathological stage, post-menopause women, strong ER expression group and no mutant p53 expression group; rs4843396 C > T was dominant in high grade clinical stage, high grade pathological stage, strong ER expression group. The rs1035550 A > G was not related to epithelial ovarian cancer susceptibility.

Conclusions

The results of the current study verified that FOXC2 gene polymorphisms were associated with increased epithelial ovarian cancer risk and suggested that FOXC2 gene polymorphisms might be a potential biomarker for epithelial ovarian cancer susceptibility.

MiR-212-3p suppresses high-grade serous ovarian cancer progression by directly targeting MAP3K3

MicroRNAs (miRNAs) are small regulatory non-coding RNAs that have been reported to play an important role in the tumorigenesis of many cancers. In addition, miRNAs might serve as new promising biomarkers for diagnosis and prognosis and as effective therapeutic targets for patients with such malignancies. Accordingly, the dysregulation of miR-212-3p has been reported in a variety of human cancers. However, its biological functions and molecular mechanisms high-grade serous ovarian cancer (HGSOG) remain unknown. In this study, we demonstrated that miR-212-3p interacts with MAP3K3 based on bioinformatics-based predictions. Further, MAP3K3 was identified as a direct target gene of miR-212-3p in HGSOC. In addition, overexpression of miR-212-3p in HGSOC inhibited cell proliferation, colony formation, invasion, and migration. In contrast MAP3K3 mitigated the suppressive effects of miR-212-3p on HGSOC cell proliferation, invasion, and migration. Furthermore, miR-212-3p was significantly downregulated in HGSOC tissues compared to expression in normal fallopian tube tissues and was inversely associated with MAP3K3 levels. Accordingly, low miR-212-3p expression was also correlated with poor prognosis for HGSOC patients. In conclusion, miR-212-3p might act as a suppressor of HGSOC carcinogenesis by directly targeting MAP3K3. Therefore, this miRNA could be a novel and effective target for the treatment of patients with HGSOC.