Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer

Ambient air pollution and the risk of cancer: Evidence from global cohort studies and epigenetic-related causal inference

The correlation between air pollution and cancer incidence has been a longstanding concern, understanding the need to elucidate the specifics of this relationship. Thus, this study aimed to assess the association between exposure to air pollution and cancer incidence, and to identify the possible biological links between the two. We examined global cohort studies investigating the association between air pollution and cancer and performed a univariate Mendelian randomization (MR) analysis. Our analysis revealed that the presence of particulate matter (PM)2.5, PM10, NO2, and NOx substantially impacted the risk of developing cancer. MR analysis identified 130 CpGs sites associated with three ambient air pollutants that have significant casual effects on the risk of 14 cancer sites (false discovery rate<0.05). Gene annotation was conducted using g-Profiler by screening for single nucleotide polymorphisms significantly associated with outcome, followed by analysis of the gene interaction network using GeneMANIA, and visualization using igraph. In conclusion, this study demonstrates that air pollution has a significant impact on cancer incidence, provides strong evidence for an epigenetic causal link between the two, and provides new insights into the molecular mechanisms by which air pollution affects cancer development.

Characterizing somatic mutations in ovarian cancer germline risk regions

Epithelial ovarian cancer (EOC) genetics research has been focused on germline or somatic mutations independently. Emerging evidence suggests that the somatic mutational landscape can be shaped by the germline genetic background. In this study, we aim to unravel the role of somatic alterations within EOC germline susceptibility regions by incorporating functional annotations. We investigate somatic events, including mutational signatures, point mutations, copy number alterations, and transcription factor binding disruptions, within 33 EOC germline susceptibility regions. Our analysis identifies significant associations between candidate germline susceptibility genes and somatic mutational signatures known to be key risk factors for EOC, such as mismatch repair deficiency, age-related mutagenesis, and homologous recombination deficiency. In addition, we find somatic point mutations and copy number alterations are significantly enriched in histotype-specific active enhancers and promoters within EOC risk loci. Furthermore, we examine the impact of germline variants and somatic mutations on transcription factor binding sites, identifying cancer developmental transcription factor motifs frequently affected by both types of mutations. Overall, our study highlights the importance of integrating germline and somatic mutations with regulatory and epigenomic data to gain insights into the genetic basis of EOC.

The role of sleep traits in prostate, endometrial, and epithelial ovarian cancers: An observational and Mendelian randomisation study

Background

Sleep traits may influence cancer risk; however, their associations with prostate (PCa), endometrial (ECa), and epithelial ovarian (EOCa) cancer remain unclear.

Methods

We conducted an observational analysis using the UK Biobank cohort and a two-sample Mendelian randomisation (MR) analysis to investigate the association of six sleep traits-duration, chronotype, insomnia, daytime napping, daytime sleepiness, and snoring-with PCa, ECa, and EOCa risk. Cox proportional hazards models were used for the observational analysis, while the inverse variance-weighted (IVW) method was applied in MR, with multiple sensitivity analyses. A Bonferroni correction accounted for multiple testing.

Results

Among 8,608 PCa, 1,079 ECa, and 680 EOCa incident diagnoses (median follow-up: 6.9 years), snoring was associated with reduced EOCa risk (HR=0.78, 95%CI: 0.62-0.98), while daytime sleepiness was associated with increased EOCa risk (HR=1.23, 95%CI: 1.03-1.47). However, these associations were not confirmed in MR. MR suggested higher odds of PCa (OR IVW =1.05, 95%CI: 1.01-1.11) and aggressive PCa (OR IVW =1.10, 95%CI: 1.02-1.19) for evening compared to morning chronotype. None of the findings survived multiple testing correction.

Conclusion

Sleep traits were not associated with PCa, ECa, or EOCa risk, but evening chronotype may increase PCa risk. Further research is needed to verify this association and investigate potential underlying mechanisms.

Impact

The proposed results have potential utility in reproductive cancer prevention.

What is already known on this topic

Sleep traits have been implicated in cancer risk, but their associations with prostate, endometrial, and epithelial ovarian cancer remain unclear.

What this study adds

This study found suggestive evidence that an evening chronotype may be associated with an increased risk of overall and aggressive prostate cancer.

How this study might affect research practice or policy

Further research is needed to confirm the potential association between chronotype and prostate cancer risk, which could inform personalised cancer prevention strategies.

Unraveling the genetic landscape of susceptibility to multiple primary cancers

With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (p < 5 × 10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when compared with single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

Gut microbiota, blood metabolites, & pan-cancer: a bidirectional Mendelian randomization & mediation analysis

We propose using Mendelian randomization analysis on GWAS data and MetaboAnalyst to model gut microbiota, metabolic pathways, blood metabolites, and cancer risk. We examined 473 gut microbiota, 205 pathways, 1400 metabolites, and 8 cancers. Results were validated through bidirectional two-sample Mendelian Randomization (MR), heterogeneity tests, and pathway enrichment, leading to a mediation pathway model. We identified 129 gut microbiota, 57 pathways, and 463 metabolites linked to cancer, and 34 significant plasma pathways. 15 microbiota, 8 pathways, and 58 metabolites implicated in multiple cancers. Eight plasma metabolic pathways are involved in the development of multiple types of cancer. Through Multivariate Mendelian Randomization (MVMR) and mediation analysis, we found 9 mediation pathways, offering novel targets and research directions for cancer pathogenesis and treatment.

Plasma proteomes and metabolism with genome-wide association data for causal effect identification in ovarian cancer

BACKGROUND

This study seeks to investigate the relationship between plasma metabolites or proteins and the risk of ovarian cancer through Mendelian randomization analysis and construct, while also developing a predictive model for resistance to chemotherapy.

METHODOLOGY/PRINCIPAL FINDINGS

Appropriate SNPs from GWAS data were selected as instrumental variables. Multiple methods, such as IVW, MR-Egger regression, and WME, were employed to investigate the causal relationship. A predictive model was established utilizing binary logistic regression based on the identified plasma protein genes. Four plasma metabolites and four plasma proteins were recognized as risk factors for ovarian cancer, whereas four plasma proteins were identified as protective factors. A predictive model for chemotherapy resistance was formulated with an AUC of 0.844 (p = 0.002).

CONCLUSIONS

Plasma metabolites and proteins may affect the risk of ovarian cancer and its resistance to chemotherapy. This study presents potential predictive factors and the underlying mechanisms influencing the onset, progression, and resistance of the disease.

Causal relationships of gut microbiota and blood metabolites with ovarian cancer and endometrial cancer: a Mendelian randomization study

OBJECTIVES

The study aimed to investigate the causal relationships of gut microbiota (GM), ovarian cancer (OC), endometrial cancer (EC), and potential metabolite mediators using Mendelian randomization (MR) analysis.

METHODS

Bidirectional two-sample MR analysis and reverse MR analysis of GM on OC/EC were employed to determine the causal effects of GM on OC/EC and the mediating role of blood metabolites in the relationship between GM and OC/EC, with results validated through sensitivity analysis.

RESULTS

We identified 6 pathogenic bacterial taxa associated with OC, including Euryarchaeota, Escherichia-Shigella, FamilyXIIIAD3011group, Prevotella9, and two unknown genera. Christensenellaceae R.7group, Tyzzerella3, and Victivallaceae were found to be protective against OC. The increase in EC risk was positively associated with Erysipelotrichia, Erysipelotrichaceae, Erysipelotrichales, and FamilyXI. Dorea, RuminococcaceaeUCG014, and Turicibacter exhibited a negative correlation with the EC risk. A total of 26 and 19 blood metabolites related to GM were identified, showing significant correlations with OC and EC, respectively. Cytosine was found to be an intermediate metabolite greatly associated with EC and FamilyXI. In reverse MR analysis, the FamilyXIIIAD3011group exhibited a significant bidirectional causal relationship with OC.

CONCLUSION

Our study revealed causal relationships of GM and intermediate metabolites with OC/EC, providing new avenues for understanding OC/EC and developing effective treatment strategies.

Curious Dichotomies of Apolipoprotein E Function in Alzheimer's Disease and Cancer-One Explanatory Mechanism of Inverse Disease Associations?

An apparent "inverse" relationship exists between two seemingly unconnected conditions, Alzheimer's disease (AD) and cancer, despite sharing similar risk factors, like increased age and obesity. AD is associated with amyloid beta (Aβ) plaques and neurofibrillary tau tangles that cause neural degeneration; cancer, in contrast, is characterized by enhanced cell survival and proliferation. Apolipoprotein E (ApoE) is the main lipoprotein found in the central nervous system and via its high affinity with lipoprotein receptors plays a critical role in cholesterol transport and uptake. ApoE has 3 protein isoforms, ApoE E2, ApoE E3, and ApoE E4, respectively encoded for by 3 allelic variants of APOE (ε2, ε3, and ε4). This review examines the characteristics and function of ApoE described in both AD and cancer to assimilate evidence for its potential contribution to mechanisms that may underly the reported inverse association between the two conditions. Of the genetic risk factors relevant to most cases of AD, the most well-known with the strongest contribution to risk is APOE, specifically the ε4 variant, whereas for cancer risk, APOE has not featured as a significant genetic contributor to risk. However, at the protein level in both conditions, ApoE contributes to disease pathology via affecting lipid physiology and transport. In AD, Aβ-dependent and -independent interactions have been suggested, whereas in cancer, ApoE plays a role in immunoregulation. Understanding the mechanism of action of ApoE in these diametrically opposed diseases may enable differential targeting of therapeutics to provide a beneficial outcome for both.

Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses

The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women's health better. As specific examples, we describe genetic risk factors for ovarian cysts that elucidate the genetic determinants of folliculogenesis and, by leveraging population-specific variants, uncover new candidate genes for uterine fibroids. We find that most female reproductive health diagnoses have a heritable component, with varying degrees of polygenicity and discoverability. Finally, we identify pleiotropic loci and genes that function in genital tract development (WNT4, PAX8, WT1, SALL1), hormonal regulation (FSHB, GREB1, BMPR1B, SYNE1/ESR1) and folliculogenesis (CHEK2), underlining their integral roles in female reproductive health.

Cathepsins and their role in gynecological cancers: Evidence from two-sample Mendelian randomization analysis

Prior studies have reported connections between cathepsins (CTS) and gynecological cancers; however, the exact causal links are yet to be fully understood. Leveraging publicly accessible genome-wide association study summary datasets, we performed a two-sample bidirectional Mendelian randomization (MR) and multivariate MR (MVMR) analysis, with the inverse variance weighted (IVW) method as the primary approach. MR analysis demonstrated inverse associations between CTSB and cervical cancer (IVW: odds ratio [OR] = 0.9995, 95% confidence interval [CI] = 0.9991-0.9999, P = .0418), CTSE and ovarian cancer (IVW: OR = 0.9197, 95% CI = 0.8505-0.9944, P = .0358), CTSZ and ovarian cancer (IVW: OR = 0.9449, 95% CI = 0.8938-0.9990, P = .0459), CTSE and high grade serous ovarian cancer (IVW: OR = 0.8939, 95% CI = 0.8248-0.9689, P = .0063), and CTSZ and high grade serous ovarian cancer (IVW: OR = 0.9269, 95% CI = 0.8667-0.9913, P = .0268). A positive correlation was identified between CTSH and clear cell ovarian cancer (IVW: OR = 1.1496, 95% CI = 1.0368-1.2745, P = .0081). Nevertheless, subsequent adjustment for the false discovery rate revealed that none of the P-values retained statistical significance (PFDR > 0.05). MVMR analysis results elucidated that CTSZ was inversely associated with cervical cancer (IVW: OR = 0.9988, 95% CI = 0.9981-0.9996, P = .0022). Moreover, a positive association was noted between CTSF and cervical cancer (IVW: OR = 1.0007, 95% CI = 1.0000-1.0014, P = .0364), and similarly, between CTSS and cervical cancer (IVW: OR = 1.0005, 95% CI = 1.0000-1.0011, P = .0490). CTSO exhibited a positive association with non-endometrioid endometrial cancer (IVW: OR = 1.4405, 95% CI = 1.1864-1.7490, P < .001), and CTSH was positively associated with clear cell ovarian cancer (IVW: OR = 1.1167, 95% CI = 1.0131-1.2310, P = .0263). The MVMR analysis findings reveal that CTSZ emerges as a protective element against cervical cancer, whereas CTSF and CTSS represent risk factors for this disease. CTSO stands out as a risk factor for non-endometrioid endometrial cancer, and CTSH acts as a risk factor for clear cell ovarian cancer. This study elucidates causative connections between CTS and gynecological cancers, providing innovative insights for diagnostic and therapeutic optimization.

The length of night shift work is closely associated with cancer risk: A pan-cancer study of Mendelian randomization study

Night shift work (NSW) has been associated with cancer risk in animal studies, but epidemiological evidence remains insufficient and contradictory. This study sought to investigate the causal association between NSW and 13 common cancers using a two-sample Mendelian randomization (MR) study. Genetic variants associated with NSW were extracted from the UK Biobank and selected as instrumental variables (IVs). Genome-wide association study (GWAS) data for 13 cancers were obtained from relevant consortia and biobanks. Causality was estimated using inverse-variance weighted (IVW), MR-Egger, and weighted median (WM). Sensitivity analyses, including MR-Egger intercept tests, MR-PRESSO, leave-one-out analyses, and funnel plots, were conducted to detect pleiotropy and heterogeneity. A suggestive causal association was found between NSW duration and risks of cervical (IVW: p = 0.028) and gastric cancer (IVW: p = 0.011). No significant associations were observed for other cancers (p > 0.05). These findings suggest the need to reduce NSW duration and limit nocturnal light exposure to maintain circadian rhythms and mitigate cancer risks.

Exome sequencing identifies HELB as a novel susceptibility gene for non-mucinous, non-high-grade-serous epithelial ovarian cancer

Rare, germline loss-of-function variants in a handful of DNA repair genes are associated with epithelial ovarian cancer. The aim of this study was to evaluate the role of rare, coding, loss-of-function variants across the genome in epithelial ovarian cancer. We carried out a gene-by-gene burden test with various histotypes using data from 2573 non-mucinous cases and 13,923 controls. Twelve genes were associated at a False Discovery Rate of less than 0.1 of which seven were the known ovarian cancer susceptibility genes BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, MSH6 and PALB2. The other five genes were OR2T35, HELB, MYO1A and GABRP which were associated with non-high-grade serous ovarian cancer and MIGA1 which was associated with high-grade serous ovarian cancer. Further support for the association of HELB association comes from the observation that loss-of-function variants in HELB are associated with age at natural menopause and Mendelian randomisation analysis shows an association between genetically predicted age at natural menopause and endometrioid ovarian cancer, but not high-grade serous ovarian cancer.

Potential drug targets for ovarian cancer identified through Mendelian randomization and colocalization analysis

BACKGROUND

The existing drugs for ovarian cancer (OC) are unsatisfactory and thus new drug targets are urgently required. We conducted proteome-wide Mendelian randomization (MR) and colocalization analysis to pinpoint potential targets for OC.

METHODS

Data on protein quantitative trait loci (pQTL) for 734 plasma proteins were obtained from large genome-proteome-wide association studies. Genetic associations with OC were derived from the Ovarian Cancer Association Consortium, which included 25,509 cases and 40,941 controls. MR analysis was performed to evaluate the association between the proteins and the OC risk. Colocalization analysis was conducted to check whether the identified proteins and OC shared causal variants. In addition, the phenome-wide MR analysis was performed to clarify protein associations across the phenotype, and drug target databases were examined for target validation.

RESULTS

Genetically predicted circulating levels of 44 proteins were associated with OC risk at Benjamini-Hochberg correction. Genetically predicted 17 proteins had evidence of the increased risk of OC (CLEC11A, MFAP2, TYMP, PDIA3, IL1R1, SPINK1, PLAU, DKK2, IL6ST, DLK1, LRRC15, CDON, ANGPTL1, SEMA4D, AKR1A1, TNFAIP6, and FCGR2B); 27 proteins decreased the risk of OC(SIGLEC9, RARRES1, SPINT3, TMEM132A, HAVCR2, CNTN2, TGFBI, GSTA1, HGFAC, TREML2, GRAMD1C, ASAH2, CPNE1, CCL25, MAPKAPK2, POFUT1, PREP, NTNG1, CA10, CACNA2D3, CA8, MAN1C1, MRC2, IL10RB, RBP4, GP5 and CALCOCO2). Bayesian colocalization demonstrated that GRAMD1C, RBP4, PLAU, PDIA3, MFAP2, POFUT1, MAN1C1 and DKK2 shared the same variant with OC. The phe-MR analyses assessed the side effects of these 44 identified proteins, and the drug target database offered information on both approved and investigational indications.

CONCLUSION

This study provides proof of a causal relationship between genetically predicted 44 proteins associated with OC risk, which could serve as promising drug targets for OC.

Genetic regulation of TERT splicing affects cancer risk by altering cellular longevity and replicative potential

The chromosome 5p15.33 region, which encodes telomerase reverse transcriptase (TERT), harbors multiple germline variants identified by genome-wide association studies (GWAS) as risk for some cancers but protective for others. Here, we characterize a variable number tandem repeat within TERT intron 6, VNTR6-1 (38-bp repeat unit), and detect a strong link between VNTR6-1 alleles (Short: 24-27 repeats, Long: 40.5-66.5 repeats) and GWAS signals rs2242652 and rs10069690 within TERT intron 4. Bioinformatics analyses reveal that rs10069690-T allele increases intron 4 retention while VNTR6-1-Long allele expands a polymorphic G-quadruplex (G4, 35-113 copies) within intron 6, with both variants contributing to variable TERT expression through alternative splicing and nonsense-mediated decay. In two cell lines, CRISPR/Cas9 deletion of VNTR6-1 increases the ratio of TERT-full-length (FL) to the alternative TERT-β isoform, promoting apoptosis and reducing cell proliferation. In contrast, treatment with G4-stabilizing ligands shifts splicing from TERT-FL to TERT-β isoform, implicating VNTR6-1 as a splicing switch. We associate the functional variants VNTR6-1, rs10069690, and their haplotypes with multi-cancer risk and age-related telomere shortening. By regulating TERT splicing, these variants may contribute to fine-tuning cellular longevity and replicative potential in the context of stress due to tissue-specific endogenous and exogenous exposures, thereby influencing the cancer risk conferred by this locus.

Exploring the causal associations of the gut microbiota and plasma metabolites with ovarian cancer: an approach of mendelian randomization analysis combined with network pharmacology and molecular docking

BACKGROUND

While increasing evidence suggests that alterations in the gut microbiota and metabolites are associated with ovarian cancer (OC) risk, whether these associations imply causation remains to be identified.

METHODS

We conducted a two-sample Mendelian randomization (MR) study utilizing a large-scale genome-wide association study (GWAS) to explore the causal effects of the gut microbiota of 196/220 individuals and 1,400 plasma metabolites on OC and epithelial ovarian cancer (EOC) subtypes. Data on the gut microbiota were obtained from the MiBioGen consortium of 18,340 subjects and the Dutch Microbiome Project of 7,738 volunteers. Data on plasma metabolites were derived from a GWAS of plasma metabolites in 8,299 participants. Ovarian cancer (n = 25,509) and EOC subtypes were obtained from the Ovarian Cancer Association Consortium (OCAC). Metabolites and associated targets were analyzed via network pharmacology and molecular docking.

RESULTS

At the genus and species levels, we identified seven risk factors for the gut microbiota: the genus Dialister (P = 0.024), genus Ruminiclostridium5 (P = 0.0004), genus Phascolarctobacterium (P = 0.0217), species Bacteroides massiliensis (P = 0.011), species Phascolarctobacterium succinatutens (P = 0.0212), species Paraprevotella clara (P = 0.0247) and species Bacteroides dorei (P = 0.0054). In addition, five gut microbes at the genus and species levels were found to be protective: genus Family XIII AD3011 group (P = 0.006), genus Butyrivibrio (P = 0.0095), genus Oscillibacter (P = 0.0206), species Roseburia hominis (P = 0.0241), and species Bifidobacterium bifidum (P = 0.0224). For plasma metabolites, we revealed five positive and four negative correlations with OC. Among these, caffeic acid and caffeine metabolites and sphingomyelin and ceramide metabolites were identified as risk factors, whereas phenylalanine metabolites, butyric acid metabolites, and some lipid metabolites were recognized as protective factors. A series of sensitivity analyses revealed no abnormalities, including pleiotropy and heterogeneity analyses.

CONCLUSION

Our MR analysis demonstrated that the gut microbiota and metabolites are causally associated with OC, which has significant potential for the early detection and diagnosis of OC and EOC subtypes, providing valuable insights into this area of research.

Exploring estrogen-related mechanisms in ovarian carcinogenesis: association between bone mineral density and ovarian cancer risk in a multivariable Mendelian randomization study

BACKGROUND

Estrogen may play a role in epithelial ovarian cancer (EOC) carcinogenesis, with effects varying by EOC histotype. Measuring women's long-term exposure to estrogen is difficult, but bone mineral density (BMD) may be a reasonable proxy of longer-term exposure. We examined this relationship by assessing the association between genetic predisposition for higher BMD and risk of EOC by histotype.

METHODS

We used Mendelian randomization (MR) to assess associations between genetic markers for femoral neck and lumbar spine BMD and each EOC histotype. We used multivariable MR (MVMR) to adjust for probable pleiotropic traits, including body mass index, height, menarcheal age, menopausal age, smoking, alcohol intake, and vitamin D.

RESULTS

Univariable analyses suggested greater BMD was associated with increased risk of endometrioid EOC (per standard deviation increase; lumbar spine OR = 1.21; 95% CI 0.93,1.57, femoral neck: OR = 1.25; 0.99,1.57), but sensitivity analyses indicated that pleiotropy was likely. Adjustment using MVMR reduced the magnitude of estimates slightly (lumbar spine: OR = 1.13; 95% CI 1.00,1.28, femoral neck: OR = 1.18; 1.03,1.36). Results for lumbar spine BMD and high-grade serous EOC were also suggestive of an association (univariable MR: OR = 1.16; 95% CI 1.03,1.30; MVMR: OR = 1.06; 0.99,1.14).

CONCLUSION

Our study found associations between genetic predisposition to higher BMD, a proxy for long-term estrogen exposure, and risk of developing endometroid and high-grade serous EOC cancers. These findings add to existing evidence of the relationship between estrogen and increased risk of EOC for certain histotypes.

Molecular and genetic insights into human ovarian aging from single-nuclei multi-omics analyses

The ovary is the first organ to age in the human body, affecting both fertility and overall health. However, the biological mechanisms underlying human ovarian aging remain poorly understood. Here we present a comprehensive single-nuclei multi-omics atlas of four young (ages 23-29 years) and four reproductively aged (ages 49-54 years) human ovaries. Our analyses reveal coordinated changes in transcriptomes and chromatin accessibilities across cell types in the ovary during aging, notably mTOR signaling being a prominent ovary-specific aging pathway. Cell-type-specific regulatory networks reveal enhanced activity of the transcription factor CEBPD across cell types in the aged ovary. Integration of our multi-omics data with genetic variants associated with age at natural menopause demonstrates a global impact of functional variants on gene regulatory networks across ovarian cell types. We nominate functional non-coding regulatory variants, their target genes and ovarian cell types and regulatory mechanisms. This atlas provides a valuable resource for understanding the cellular, molecular and genetic basis of human ovarian aging.

Large-scale analysis to identify risk factors for ovarian cancer

OBJECTIVE

Ovarian cancer is characterized by late-stage diagnoses and poor prognosis. We aimed to identify factors that can inform prevention and early detection of ovarian cancer.

METHODS

We used a data-driven machine learning approach to identify predictors of epithelial ovarian cancer from 2920 input features measured 12.6 years (IQR 11.9 to 13.3 years) before diagnoses. Analyses included 221 732 female participants in the UK Biobank without a history of cancer. During the follow-up 1441 women developed ovarian cancer. For factors that contributed to model prediction, we used multivariate logistic regression to evaluate the association with ovarian cancer, with evidence for causality tested by Mendelian randomization (MR) analyses in the Ovarian Cancer Genetics Consortium (25 509 cases).

RESULTS

Greater parity and ever-use of oral contraception were associated with lower ovarian cancer risk (ever vs never OR 0.74, 95% CI 0.66 to 0.84). After adjustment for established risk factors, greater height, weight, and greater red blood cell distribution width were associated with increased ovarian cancer risk, while higher aspartate aminotransferase levels and mean corpuscular volume were associated with lower risk. MR analyses confirmed observational associations with anthropometric/adiposity traits (eg, body fat percentage per standard deviation (SD); OR inverse-variance weighted (ORIVW) 1.28, 95% CI 1.13 to 1.46) and aspartate aminotransferase (ORIVW 0.87, 95% CI 0.78 to 0.98). MR also provided genetic evidence for a protective association of higher total serum protein on ovarian cancer, higher lymphocyte count on serous and endometrioid ovarian cancer, and greater forced expiratory volume in 1 s on serous ovarian cancer among other findings.

CONCLUSIONS

This study shows that certain risk factors for ovarian cancer are modifiable, suggesting that weight reduction and interventions to reduce the number of ovulations may provide potential for future prevention. We also identified blood biomarkers associated with ovarian cancer years before diagnoses, warranting further investigation.

ATEC23 Challenge: Automated prediction of treatment effectiveness in ovarian cancer using histopathological images

Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan-Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis-based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.

Percentage of fat in milk consumption and risk of six cancers: a Mendelian randomization study

Background

The causal relationship between percentage of fat in milk consumption and cancer risk lacks sufficient investigation. The purpose of this study was to explore whether the percentage of fat in milk consumption is a factor that affects the risk variation of several common types of cancer.

Methods

Mendelian randomization (MR) was performed to estimate the unconfounded causal relationship between the percentage of fat in milk consumption and the risk of six cancers related to milk intake, as well as to assess the associations between body fat percentage and these cancers. Data corresponding to the percentage of fat in milk consumption (n=411,503), body fat percentage (n=401,772), breast cancer (n=139,274), ovarian cancer (n=66,450), endometrial cancer (n=121,885), colorectal cancer (n=32,072), prostate cancer (n=140,254), and bladder cancer (n=373,295) were obtained from the Integrative Epidemiology Unit (IEU) or the genome-wide association study (GWAS) Catalog databases. The primary analytical strategy employed the inverse-variance weighted (IVW) method. Sensitivity analysis, including assessments of heterogeneity and pleiotropy, was conducted to assess the robustness of the findings.

Results

The percentage of fat in milk consumption only exhibited a causal relationship with breast cancer (β=2.993, P=0.01). The study identified significant causal effects of body fat percentage on the risk of several cancers, including ovarian cancer (β=0.225, P=0.002), endometrial cancer (β=0.669, P<0.001), and colorectal cancer (β=0.344, P<0.001), as well as a protective effect on prostate cancer (β=-0.104, P=0.046). Sensitivity analysis demonstrated that the findings were robust.

Conclusions

Our study findings indicated that a higher percentage of fat in milk consumption was associated with an increased risk of breast cancer, providing valuable insights for cancer prevention strategies among the European population.

Proteome-Wide Mendelian Randomization Analysis to Identify Potential Plasma Biomarkers and Therapeutic Targets for Epithelial Ovarian Cancer Subtypes

Background

Epithelial ovarian cancer (EOC) remains an unmet medical challenge due to its insidious onset, atypical symptoms, and increasing resistance to conventional chemotherapeutic agents. It is imperative to explore novel biomarkers and generate innovative target drugs.

Methods

To identify potential proteins with causal association to EOC subtypes, we conducted a Mendelian Randomization (MR) analysis using 15,419 protein quantitative trait loci (pQTLs) associated with 2015 proteins. Bayesian colocalization analysis, Summary-data-based MR, and Heterogeneity in Dependent Instruments tests were employed for validation. Enrichment and druggability analyses were performed to assess the biological significance and therapeutic potential of identified proteins.

Results

Our analysis identified 455 unique proteins associated with at least one EOC subtype, with 14 protein-cancer associations confirmed by further validation. Ten proteins were prioritized as potential therapeutic targets, including α1B-glycoprotein (A1BG) and ephrin-A1 (EFNA1), which interact with the known drug targets human epidermal growth factor receptor 2 (HER2) and vascular endothelial growth factor receptor (VEGFR).

Conclusion

This study elucidated the plasma proteins causally associated with EOC subtypes, potentially offering easily detectable biomarkers and promising therapeutic targets. A1BG and EFNA1 were identified as druggable targets and confirmed to correspond with current pharmacological targets. Targeting these proteins in drug development potentially offers an avenue for innovative treatment strategies.

Genetic Causes of Sudden Cardiac Arrest in the Community

Background

Annually 300,000 Americans experience sudden cardiac arrest (SCA). Studies in referral SCA cohorts have observed rare variants in genes associated with arrhythmia and cardiomyopathy. We sought to: (1) establish the population prevalence of rare disease-causing variants in a set of candidate genes and (2) confirm the association of disease-causing variants in these genes with SCA in two prospective population-based studies.

Methods

SCA patients (n=3264) were accrued from the Oregon Sudden Unexpected Death Study and the PREdiction of Sudden death in mulTi-ethnic cOmmunities (PRESTO) study and compared to control patients (n=13713) from the Atherosclerosis Risk in Communities (ARIC) study. Whole genome sequencing was performed. Disease-causing (likely pathogenic or pathogenic) variants in candidate genes associated with arrhythmia/cardiomyopathy were identified using updated American College of Medical Genetics and Genomics criteria. Gene- collapsing case-control analysis was performed using the conditional logistic regression-sequence kernel association test.

Results

We identified 300 disease-causing variants, the majority of which were in cardiomyopathy genes (71%). There were 136 patients (4.2%) in the SCA group and 351 patients (2.6%) in the control group with one or more disease-causing variants (OR 1.66, 95% confidence interval 1.33-2.07, p<0.001). We identified 13 genes associated with an increased risk of SCA, nine associated with cardiomyopathy ( BAG3, DSC2, DSG2, FLNC, LMNA, MYBPC3, TNNI3, TNNT2, TTN ) and four with arrhythmia ( CACNA1C, CASQ2, KCNH2, KCNQ1 ).

Conclusions

Disease-causing variants in cardiomyopathy genes were the predominant genetic cause of SCA. These findings inform which genes to include in genetic screening for SCA.

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

BACKGROUND

Genomic instability and evading apoptosis are two fundamental hallmarks of cancer and closely linked to DNA damage response (DDR). By analyzing expression quantitative trait loci (eQTL) upon cell stimulation (called exposure eQTL (e2QTL)) it is possible to identify context specific gene regulatory variants and connect them to oncological diseases based on genome-wide association studies (GWAS).

RESULTS

We isolate CD8+ T cells from 461 healthy donors and stimulate them with high doses of 5 different carcinogens to identify regulatory mechanisms of DNA damage-induced apoptosis. Across all stimuli, we find 5,373 genes to be differentially expressed, with 85% to 99% of these genes being suppressed. While upregulated genes are specific to distinct stimuli, downregulated genes are shared across conditions but exhibit enrichment in biological processes depending on the DNA damage type. Analysis of eQTL reveals 654 regulated genes across conditions. Among them, 47 genes are significant e2QTL, representing a fraction of 4% to 5% per stimulus. To unveil disease relevant genetic variants, we compare eQTL and e2QTL with GWAS risk variants. We identify gene regulatory variants for KLF2, PIP4K2A, GPR160, RPS18, ARL17B and XBP1 that represent risk variants for oncological diseases.

CONCLUSION

Our study highlights the relevance of gene regulatory variants influencing DNA damage-induced apoptosis in cancer. The results provide new insights in cellular mechanisms and corresponding genes contributing to inter-individual effects in cancer development.

Deciphering the causal landscape: genetic insights into sporadic vestibular schwannoma risk factors through Mendelian Randomization

BACKGROUND

Sporadic vestibular schwannoma, a benign tumor affecting the vestibulocochlear nerve, poses significant health challenges due to its impact on hearing, balance, and facial nerve function. Despite known associations with genetic mutations and environmental factors, the causality between potential risk factors and sporadic vestibular schwannoma remains underexplored.

OBJECTIVE

This study aims to investigate the causal effects of various genetically predicted risk factors on sporadic vestibular schwannoma utilizing a Two-Sample Mendelian Randomization (MR) approach to enhance understanding of its etiology and inform prevention strategies.

METHODS

Leveraging data from genome-wide association studies (GWAS), we analyzed 29 risk factors across five categories: related diseases, lifestyle habits, nutritional status, learning ability, and laboratory indicators. The MR analysis employed instrumental variables (IVs) derived from single nucleotide polymorphisms (SNPs) to assess causal relationships, overcoming traditional observational study limitations.

RESULTS

Our findings highlight significant associations between sporadic vestibular schwannoma and factors such as ovarian cancer, uterine fibroids and lifestyle habits including dietary intake and alcohol consumption. Notably, higher educational attainment and specific laboratory indicators like high-density lipoprotein (HDL) cholesterol levels were linked to altered disease risk. These results suggest a multifaceted etiology involving hormonal, cardiovascular, gastrointestinal, immune, and metabolic pathways.

CONCLUSION

This comprehensive MR study provides novel insights into the diverse risk factors contributing to sporadic vestibular schwannoma, emphasizing the role of genetic predispositions, hormonal influences, and lifestyle choices in its development. The associations identified underscore the need for a multidisciplinary research approach and targeted public health strategies to mitigate sporadic vestibular schwannoma risk. Further research into the underlying mechanisms of these associations is crucial for developing effective interventions and improving patient outcomes.

Causal effect between breast cancer and ovarian cancer: a two-sample mendelian randomization study

OBJECTIVES

Improved breast cancer (BC) outcomes highlight the importance of secondary primary cancers (SPCs) on survivor prognosis. The objective of this study was to investigate the potential genetic association between primary BC and ovarian cancer (OC), laying the groundwork for the development of preventive strategies for SPC-OC following BC surgery.

METHODS

This study aimed to assess the connection between BC and OC using a two sample Mendelian randomization (MR) approach, exclusively employing aggregate level data from publicly available genome wide association studies (GWASs). Finally, the Genetic Risk Scores (GRS) method was used for secondary analysis to verify the results robustness further.

RESULTS

The IVW method revealed a genetic correlation between Overall BC and ER + BC with Serous borderline tumors, while ER-BC exhibited genetic correlation with Mucinous borderline tumors and high-grade serous ovarian cancer. The findings from the GRS method aligned with those of the primary analysis, reinforcing the study's robustness.

CONCLUSION

Our MR Study identifies an association between BC and OC, highlighting the importance of increased vigilance in clinical practice for individuals with a history of BC. Timely intervention and treatment measures should be taken when necessary.

Genetic Evidence for the Causal Link Between Coagulation Factors and the Risk of Ovarian Cancer: A Two-Sample Mendelian Randomization Study

Background

Prior investigations have suggested a significant association between coagulation factors and ovarian cancer; however, the precise nature of the causal relationship remains elusive. Our objective is to thoroughly investigate this causal link and delineate the influence of coagulation factors on the risk of ovarian cancer through a rigorous two-sample Mendelian randomization (MR) analysis.

Methods

Genetic instrumental variables representing coagulation factors were sourced from four distinct data repositories. Summary statistics pertaining to ovarian cancer were obtained from two extensive Genome-Wide Association Studies (GWAS) for primary and replication analyses, respectively. The primary Mendelian randomization (MR) analysis utilized the inverse-variance weighted (IVW) method. To fortify the reliability of our findings, additional analyses were conducted, including the weighted-median method, MR-Egger regression, MR pleiotropy residual sum and outlier test, Cochran's Q statistic test, MR-Egger intercept analysis, and leave-one-out method, among others.

Results

We identified four coagulation factors that were associated with the risk of ovarian cancer in the primary analysis, [odds ratio (OR): 1.365, 95% confidence interval (CI): 1.209-1.542, P <0.001 for von Willebrand factor measurement(vWF); OR: 1.060, 95% CI: 1.018-1.104, P = 0.005 for A disintegrin and metalloproteinase with thrombospondin motifs 13 (ADATMS13); OR: 1.317, 95% CI: 1.002-1.730, P = 0.048 for activated partial thromboplastin time (aPTT); OR: 1.139, 95% CI: 1.063-1.221, P <0.001 for coagulation Factor VIII (FVIII)]. In the meta-analysis, we found that higher levels of coagulation factor VII measurement(FVII) (OR=1.0007, 95% CI: 1.0001-1.0013, P=1.0007) was associated with increased ovarian cancer risk. The results of sensitivity analyses for these coagulation factors were consistent (P<0.05).

Conclusion

Our systematic analyses have furnished evidence suggesting a plausible causal association between FVII and the susceptibility to ovarian cancer. Further investigations are warranted to delineate the mechanistic pathways through which coagulation factors influence the progression of ovarian cancer.

Causal relationship between cancer and immune cell traits: A two-sample mendelian randomization study

Background

Observational studies provide evidence of correlations between cancer and the immune system. Previous research has established associations between immune traits and the propensity for developing certain cancers. However, a systematic exploration of these connections remains largely uncharted. Therefore, further investigation is needed to examine the causal association between cancer and immune cell traits using Mendelian randomization (MR) approach.

Methods

We identified genetic instruments for breast cancer (BC), lung cancer (LC), endometrial cancer (EC), ovarian cancer (OC), prostate cancer (PC), and their subtype cancers to investigate their potential causal impact on immune traits. Data on cancer and immune cell traits were obtained from the IEU Open GWAS project. To assess whether these five cancer types and subtype cancers have a causal association with immune cell traits, we conducted two-sample MR analyses. Additionally, we conducted bidirectional MR analyses to examine the direction of causal relationships and adjusted for potentially related pleiotropy through multivariable MR analysis.

Results

We have identified several causal relationships between different types of cancer and immune traits. We found that breast cancer may influence 49 immune cell traits, endometrial cancer may influence 38, lung cancer may influence 25, ovarian cancer may influence 19, and prostate cancer may influence 28. Among these, breast cancer and lung cancer were associated with four common immune traits: CD25 on IgD- CD38dim, CD25 on sw mem, CD24 on IgD- CD38-, and CD25 on IgD- CD38-. Lung cancer and prostate cancer shared four immune traits: CD25 on IgD+ CD24+, CD25 on IgD+ CD38-, CD66b on CD66b++ myeloid cell, DN (CD4-CD8-) AC. Endometrial cancer and ovarian cancer shared two immune traits: TD DN (CD4-CD8-) %DN, EM DN (CD4-CD8-) %DN. Breast cancer and endometrial cancer shared one immune trait: CD20 on IgD- CD38dim. Endometrial cancer and prostate cancer shared one immune trait: CCR2 on myeloid DC. Lastly, breast cancer, lung cancer, and prostate cancer shared one immune trait: CD25 on CD24+ CD27+. Additionally, we identified specific immune traits that may serve as protective or risk factors for cancers. We found 14 immune traits may influence breast cancer, 9 immune traits may influence endometrial cancer, 22 immune traits may influence lung cancer, 9 immune traits may influence ovarian cancer, and 14 immune traits may influence prostate cancer. Among these, breast cancer and prostate cancer shared three immune traits: HLA DR++ monocyte %monocyte, HLA DR on plasmacytoid DC, and HLA DR on DC. Lung cancer and ovarian cancer shared one immune trait: CD62L- monocyte %monocyte. Prostate cancer and endometrial cancer shared one immune trait: HLA DR on CD33dim HLA DR + CD11b+. Lastly, ovarian cancer and prostate cancer shared one immune trait: CD3 on resting Treg.

Conclusions

Our MR study suggests a potential relationship between immune traits and cancers, particularly highlighting 14 immune traits that are simultaneously influenced by two or three of five cancer types, while also indicating that 6 immune traits may simultaneously contribute to the development of two of the cancers. This elucidation enables us to reveal a significant involvement of immune traits in cancer progression, providing critical insights into how immune traits affect cancer susceptibility.

Genetic regulation of TERT splicing contributes to reduced or elevated cancer risk by altering cellular longevity and replicative potential

The chromosome 5p15.33 region, which encodes telomerase reverse transcriptase (TERT), harbors multiple germline variants identified by genome-wide association studies (GWAS) as risk for some cancers but protective for others. We characterized a variable number tandem repeat within TERT intron 6 (VNTR6-1, 38-bp repeat unit) and observed a strong association between VNTR6-1 alleles (Short: 24-27 repeats, Long: 40.5-66.5 repeats) and GWAS signals within TERT intron 4. Specifically, VNTR6-1 fully explained the GWAS signals for rs2242652 and partially for rs10069690. VNTR6-1, rs10069690 and their haplotypes were associated with multi-cancer risk and age-related telomere shortening. Both variants reduce TERT expression through alternative splicing and nonsense-mediated decay: rs10069690-T increases intron 4 retention and VNTR6-1-Long expands a polymorphic G quadruplex (G4, 35-113 copies) within intron 6. Treatment with G4-stabilizing ligands decreased the fraction of the functional telomerase-encoding TERT full-length isoform, whereas CRISPR/Cas9 deletion of VNTR6-1 increased this fraction and apoptosis while reducing cell proliferation. Thus, VNTR6-1 and rs10069690 regulate the expression and splicing of TERT transcripts encoding both functional and nonfunctional telomerase. Altered TERT isoform ratios might modulate cellular longevity and replicative potential at homeostasis and in response to environmental factors, thus selectively contributing to the reduced or elevated cancer risk conferred by this locus.

GWAS and 3D chromatin mapping identifies multicancer risk genes associated with hormone-dependent cancers

Hormone-dependent cancers (HDCs) share several risk factors, suggesting a common aetiology. Using data from genome-wide association studies, we showed spatial clustering of risk variants across four HDCs (breast, endometrial, ovarian and prostate cancers), contrasting with genetically uncorrelated traits. We identified 44 multi-HDC risk regions across the genome, defined as overlapping risk regions for at least two HDCs: two regions contained risk variants for all four HDCs, 13 for three HDCs and 28 for two HDCs. Integrating GWAS data, epigenomic profiling and promoter capture HiC maps from diverse cell line models, we annotated 53 candidate risk genes at 22 multi-HDC risk regions. These targets were enriched for established genes from the COSMIC Cancer Gene Census, but many had no previously reported pleiotropic roles. Additionally, we pinpointed lncRNAs as potential HDC targets and identified risk alleles in several regions that altered transcription factors motifs, suggesting regulatory mechanisms. Known drug targets were over-represented among the candidate multi-HDC risk genes, implying that some may serve as targets for therapeutic development or facilitate the repurposing of existing treatments for HDC. Our approach provides a framework for identifying common target genes driving complex traits and enhances understanding of HDC susceptibility.

Association between cholelithiasis, cholecystectomy, and risk of breast and gynecological cancers: Evidence from meta-analysis and Mendelian randomization study

BACKGROUND

Observational studies have shown that cholelithiasis and cholecystectomy are associated with the risk of breast cancer (BC) and gynecological cancers, but whether these relationships are causal has not been established and remains controversial.

METHODS

Our study began with a meta-analysis that synthesized data from prior observational studies to examine the association between cholelithiasis, cholecystectomy, and the risk of BC and gynecological cancers. Subsequently, a two-sample Mendelian randomization (MR) analysis was conducted utilizing genetic variant data to investigate the potential causal relationship between cholelithiasis, cholecystectomy, and the aforementioned cancers.

RESULTS

The results of the meta-analysis demonstrated a significant association between cholecystectomy and the risk of BC (risk ratio [RR] = 1.04, 95% confidence interval [CI]: 1.01-1.06, p = 0.002) and endometrial cancer (EC) (RR = 1.26, 95% CI: 1.02-1.56, p = 0.031). Conversely, no significant association was observed between cholelithiasis and the risk of BC, EC, and ovarian cancer. The MR analysis revealed no discernible causal connection between cholelithiasis and overall BC (p = 0.053), as well as BC subtypes (including estrogen receptor-positive/negative). Similarly, there was no causal effect of cholecystectomy on BC risk (p = 0.399) and its subtypes. Furthermore, no causal associations were identified between cholelithiasis, cholecystectomy, and the risk of gynecological cancers (ovarian, endometrial, and cervical cancer [CC]) (all p > 0.05).

CONCLUSION

This study does not support a causal link between cholelithiasis and cholecystectomy and an increased risk of female cancers such as breast, endometrial, ovarian, and CC.

Unraveling the genetic landscape of susceptibility to multiple primary cancers

With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (P<5×10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when comparing to single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level

Integrating genome-wide association study (GWAS) and transcriptomic datasets can help identify potential mediators for germline genetic risk of cancer. However, traditional methods have been largely unsuccessful because of an overreliance on total gene expression. These approaches overlook alternative splicing, which can produce multiple isoforms from the same gene, each with potentially different effects on cancer risk. Here, we integrate genetic and multi-tissue isoform-level gene expression data from the Genotype Tissue-Expression Project (GTEx, N = 108-574) with publicly available European-ancestry GWAS summary statistics (all N > 20,000 cases) to identify both isoform- and gene-level risk associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total). Compared to traditional methods leveraging total gene expression, directly modeling isoform expression through transcriptome-wide association studies (isoTWAS) substantially increases discovery of transcriptomic mechanisms underlying genetic associations. Using the same RNA-seq datasets, isoTWAS identified 164% more significant unique gene associations compared to TWAS (6,163 and 2,336, respectively), with isoTWAS-prioritized genes enriched 4-fold for evolutionarily-constrained genes (P = 6.1 × 10-13). isoTWAS tags transcriptomic associations at 52% more independent GWAS loci compared to TWAS across the six cancers. Additionally, isoform expression mediates an estimated 63% greater proportion of cancer risk SNP heritability compared to gene expression when evaluating cis-genetic influence on isoform expression. We highlight several notable isoTWAS associations that demonstrate GWAS colocalization at the isoform level but not at the gene level, including, CLPTM1L (lung cancer), LAMC1 (colorectal), and BABAM1 (breast). These results underscore the critical importance of modeling isoform-level expression to maximize discovery of genetic risk mechanisms for cancers.

Using Genetics to Assess the Role of Acetate in Ischemic Heart Disease, Diabetes, and Sex-Hormone-Related Cancers: A Mendelian Randomization Study

BACKGROUND

Acetate, a short-chain fatty acid, has gained attention for its contrasting roles, with evidence suggesting it may offer cardiovascular protection but also promote cancer, particularly those involving sex hormones. However, these influences have been scarcely assessed in epidemiological research.

OBJECTIVE

To investigate the relationship between acetate and ischemic heart disease (IHD), diabetes, and cancers related to sex hormones.

METHODS

Mendelian randomization (MR) was used to assess potential causal effects, selecting genetic variants without linkage disequilibrium (r2 < 0.001) and with genome-wide significance for acetate (p < 5 × 10-8). These variants were applied to large genome-wide association studies (GWAS) for ischemic heart disease (IHD; up to 154,373 cases), diabetes (109,731 cases), and five sex-hormone-related cancers (breast, colorectal, prostate, ovarian, and endometrial cancers, ranging from 8679 to 122,977 cases). We employed various methods for analysis, including penalized inverse variance weighting (pIVW), inverse variance weighting, weighted mode, and weighted median.

RESULTS

This study indicates that acetate may be associated with a lower risk of ischemic heart disease (IHD), with an odds ratio (OR) of 0.62 per standard deviation (SD) increase in acetate and a 95% confidence interval (CI) of 0.39 to 0.98. Additionally, acetate was linked to a higher breast cancer risk, with an OR of 1.26 and a 95% CI ranging from 1.08 to 1.46. This association remained robust across multiple sensitivity analyses.

CONCLUSIONS

Acetate, along with factors that influence its activity, may serve as possible targets for breast cancer treatment and possibly IHD, offering opportunities for new drug development.

Circulating Phylloquinone and the Risk of Four Female-Specific Cancers: A Mendelian Randomization Study

BACKGROUND

Observational studies have linked vitamin K and cancer, but the causality of this association remains unknown. This Mendelian randomization (MR) study aims to investigate the association between circulating phylloquinone (vitamin K1) levels and four female-specific cancers.

METHODS

We used four single-nucleotide polymorphisms (SNPs) to instrument phylloquinone, with the reported F-statistic 16.00-28.44 for all variants. SNP-outcome associations were obtained from consortia meta-analyses, UK Biobank, and the FinnGen database (up to 145,257/419,675, 27,446/362,324, 15,181/591,477, and 2211/320,454 cases/controls for breast, ovarian, endometrial, and cervical cancer, respectively). Analyses were conducted using five complementary MR methods including pleiotropy robust approaches. The MR Egger intercept test, MR PRESSO global test and leave-one-out analyses were used to test for and identify pleiotropic variants.

RESULTS

The relevance of the instrument was validated by positive control analyses on coagulation factor IX (p = 0.01). However, the main MR analysis and all sensitivity analyses were consistently supportive of a null association between phylloquinone and all four cancers (p > 0.05 for all analyses, across all methods). MR-PRESSO did not detect outlying variants, and there was no evidence of horizontal pleiotropy relating to any cancer outcome (pintercept > 0.26 for all).

CONCLUSIONS

We found no evidence for an association between genetically predicted circulating phylloquinone levels and the risk of four female-specific cancers.

Causal relationship between amino acids and ovarian cancer in the European population: A bidirectional Mendelian randomization study and meta-analysis

In recent years, an increasing number of observational studies have reported the impact of amino acids on ovarian cancer. However, Mendelian randomization studies have not yet been conducted to explore the causal relationship between them in the context of ovarian cancer. This study conducted Mendelian randomization (MR) analysis of 20 amino acids in relation to ovarian cancer data from 2 different sources within the European population, using a two-sample MR approach. The primary results from the inverse variance weighting analysis were then subjected to a meta-analysis, followed by multiple testing correction for the meta-analysis thresholds. Finally, reverse causality testing was performed on the positively associated amino acids and ovarian cancer. MR analyses were conducted for 20 amino acids with ovarian cancer data from both the Finngen R10 and Open genome-wide association study databases. The inverse variance weighted results from these 2 analyses were then combined through meta-analysis, with multiple corrections applied to the significance thresholds of the meta-analysis results. The findings showed that only cysteine had a significant association with ovarian cancer, with an (odds ratio) odds ratio value of 0.507 (95% confidence interval: 0.335-0.767, P = .025). The P-value of the combined MR and meta-analysis, after multiple testing correction, was 0.025, indicating statistical significance (P < .05). Additionally, cysteine did not show a reverse causal relationship with ovarian cancer in either data source. Cysteine is a protective factor for ovarian cancer, potentially reducing the risk of ovarian cancer and slowing the progression of the disease.

Two-sample Mendelian Randomization to evaluate the causal relationship between inflammatory arthritis and female-specific cancers

BACKGROUND

There is evidence that inflammatory arthritis in the form of ankylosing spondylitis (AS), psoriatic arthritis (PsA), and rheumatoid arthritis are both positively and negatively associated with certain female-specific cancers. However, the study results are very heterogeneous.

METHODS

Based on up to 375,814 European women, we performed an iterative two-sample Mendelian randomization to assess causal effects of the occurrence of the inflammatory arthritis on the risk of female-specific cancer in form of breast, endometrial, and ovarian cancer sites as well as their subtypes. Evidence was strengthened by using similar exposures for plausibility or by replication with a subsequent meta-analysis. P-values were Bonferroni adjusted.

RESULTS

Genetic liability to AS was associated with ovarian cancer (OR = 1.03; 95% CI: [1.01; 1.04]; [Formula: see text]=0.029) and liability to PsA with breast cancer (OR = 1.02; CI: [1.01; 1.04]; [Formula: see text]=0.002). Subgroup analyses revealed that the high-grade serous ovarian cancer (OR = 1.04; CI: [1.02; 1.06]; [Formula: see text]=0.015) and the ER- breast cancer (OR = 1.04; CI: [1.01; 1.07]; [Formula: see text]=0.118) appeared to drive the observed associations, respectively. No further associations were found between the remaining inflammatory arthritis phenotypes and female-specific cancers.

CONCLUSIONS

This study suggests that AS is a risk factor for ovarian cancer, while PsA is linked to an increased breast cancer risk. These results are important for physicians caring women with inflammatory arthritis to advise their patients on cancer screening and preventive measures.

Appraisal of the causal effect of Chlamydia trachomatis infection on epithelial ovarian cancer risk: a two-sample Mendelian randomisation study

Background

History of Chlamydia trachomatis infection has previously been associated with epithelial ovarian cancer (EOC) in observational studies. We conducted a two-sample univariable Mendelian randomisation (MR) study to examine whether genetically predicted seropositivity to the C. trachomatis major outer membrane protein (momp) D is causally associated with EOC.

Methods

MR analyses employed genetic associations derived from UK Biobank as proxies for momp D seropositivity in 25 509 EOC cases and 40 941 controls that participated in the Ovarian Cancer Association Consortium. Findings were replicated using a GWAS meta-analyses of global biobanks including the UK Biobank, FinnGen and BioBank Japan.

Results

Genetically predicted momp D seropositivity was associated with overall and high-grade serous EOC risk in inverse-variance weighted (IVW) and MR-Egger univariable MR analysis (odds ratio (OR) 1.06; 95% confidence interval (CI) 1.02-1.10, and OR 1.08; 95%CI 1.01-1.16, respectively). Replication yielded similar results for overall EOC (OR 1.11; 95%CI 1.01-1.22).

Conclusion

This MR study supports a causative link between C. trachomatis infection and overall and high-grade serous EOC.

Associations between serum metabolites and female cancers: A bidirectional two-sample mendelian randomization study

Female cancers, especially breast, ovarian, cervical, and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers-breast, ovarian, cervical, and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo's studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane's Q, Egger's intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate, and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

Identifying genetic variants that influence the abundance of cell states in single-cell data

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Causal Relationship Between Mood Swing and Gynecological Disorders: A Mendelian Randomization Study

Background

Gynecological disorders are a wide range of health problems affecting the female reproductive system, which poses substantial health challenges worldwide. Increasing number of observational studies have associated mood instability to common female diseases, but the underlying causal relationship remains unclear. In this work, Mendelian randomization (MR) analysis was applied to explore the genetically predicted causal relationship of mood swings and several prevalent gynecological disorders.

Methods

Instrumental variables (IVs) of mood swings were selected from UK Biobank (UKB), with 204,412 cases and 247,207 controls being incorporated. The genetic variants for female disorders were obtained from genome-wide association studies (GWASs) and FinnGen consortium. To avoid biases caused by racial difference, only European population was included here. Five strong analytical methodologies were used to increase the validity of the results, the most substantial of which was the inverse variance weighting (IVW) method. Pleiotropy, sensitivity, and heterogeneity were assessed to strengthen the findings.

Results

We found mood swings was significantly positively associated with risk of endometrial cancer (OR= 2.60 [95% CI= 1.36, 4.95], P= 0.0037), cervical cancer (OR= 1.01[95% CI= 1.00,1.02], P= 0.0213) and endometriosis (OR= 2.58 [95% CI= 1.18, 5.60], P= 0.0170) by IVW method. However, there was no causal relationship between mood swing and ovarian cancer. No pleiotropy and heterogeneity existed and sensitivity tests were passed.

Conclusion

This study reveals that mood swing may serve as a genetically predicted causal risk factor for endometrial cancer, cervical cancer, and endometriosis in the European population, while no such association was observed for ovarian cancer. These findings make up for observational research's inherent limitations and may improve patient outcomes in the field of gynecological health. However, the study's focus on European populations may limit the applicability of these results globally.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2

BACKGROUND

As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.

RESULTS

The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.

CONCLUSIONS

Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction

BACKGROUND

The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population.

METHODS

In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS.

RESULTS

The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66-5.89; p = 1.76 × 10-4). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49-1.81; p < 2.0 × 10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%).

CONCLUSIONS

Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.

Association of telomerase reverse transcriptase gene rs10069690 variant with cancer risk: an updated meta-analysis

OBJECTIVE

Existing evidence suggests telomerase activation is a crucial step in tumorigenesis. The telomerase reverse transcriptase (TERT), encoded by the human TERT gene, is critical for telomerase expression. The TERT rs10069690 (C > T) variant was identified to be associated with the risk of cancer, however, there have been inconsistent results. Therefore, we performed a comprehensive meta-analysis aiming to clarify the association between this variant and cancer susceptibility.

METHODS

We conducted literature search in PubMed, EMbase, MEDLINE and Cochrane Library up to April 30, 2024. Overall, there are 55 studies involving 334,196 patients with cancer and 741,187 controls included in the present study. All statistical analyses were performed by STATA software (version 11.0).

RESULTS

The pooled results showed a significant association between rs10069690 and an increased risk of cancer under allele model (OR = 1.10, 95% CI: 1.07-1.13, P < 0.001), especially in European and Asian populations. When stratified by cancer types, this variant was associated with elevated risk of breast cancer (OR = 1.11, 95% CI: 1.07-1.15, P < 0.001), ovarian cancer (OR = 1.14, 95% CI: 1.10-1.19, P < 0.001), lung cancer (OR = 1.20, 95% CI: 1.07-1.35, P = 0.003), thyroid cancer (OR = 1.23, 95% CI: 1.15-1.32, P < 0.001), gastric cancer (OR = 1.31, 95% CI: 1.19-1.45, P < 0.001), and renal cell carcinoma (OR = 1.29, 95% CI: 1.07-1.55, P = 0.007), while decreased risk was found for hepatocellular carcinoma, prostate cancer and pancreatic cancer. Our results also indicated that this variant was significantly associated with solid cancer (OR = 1.11, 95% CI: 1.07-1.14, P < 0.001), but not with hematological tumor.

CONCLUSION

This systematic meta-analysis demonstrated that the TERT rs10069690 variant was a risk factor for cancer. However, the effects of this variant may vary in different types of cancer and differ across ethnic populations.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

Association of glucagon-like peptide-1 receptor agonists with risk of cancers-evidence from a drug target Mendelian randomization and clinical trials

BACKGROUND

Glucagon-like peptide-1 receptor (GLP1R) agonists have been approved by Food and Drug Administration for management of obesity. However, the causal relationship of GLP1R agonists (GLP1RA) with cancers still unclear.

METHODS

The available cis-eQTLs for drugs target genes (GLP1R) were used as proxies for exposure to GLP1RA. Mendelian randomizations (MR) were performed to reveal the association of genetically-proxied GLP1RA with 14 common types cancer from large-scale consortia. Type 2 diabetes was used as positive control, and the GWASs data including 80 154 cases and 853 816 controls. Replicating the findings in the FinnGen study and then pooled with meta-analysis. Finally, all the related randomized controlled trails (RCTs) on GLP1RA were systematically searched from PubMed, Embase, and the Cochrane Library to comprehensively synthesize the evidence to validate any possible association with cancers.

RESULT

A total of 22 significant cis-eQTL single-nucleotide polymorphisms were included as genetic instrument. The association of genetically-proxied GLP1RA with significantly decreased type 2 diabetes risk [OR (95%)=0.82 (0.79-0.86), P <0.001], which ensuring the effectiveness of identified genetic instruments. The authors found favorable evidence to support the association of GLP1RA with reduced breast cancer and basal cell carcinoma risk [0.92 (0.88-0.96), P <0.001, 0.92 (0.85-0.99), P =0.029, respectively], and with increased colorectal cancer risk [1.12 (1.07-1.18), P <0.001]. In addition, there was no suggestive evidence to support the association of GLP1RA with ovarian cancer [0.99 (0.90-1.09), P =0.827], lung cancer [1.01 (0.93-1.10), P =0760], and thyroid cancer [0.83 (0.63-1.10), P =0.187]. Our findings were consistent with the meta-analysis. Finally, 80 RCTs were included in the systematic review, with a low incidence of different kinds of cancer.

CONCLUSIONS

Our study suggests that GLP1RA may decrease the risk of breast cancer and basal cell carcinoma, but increase the risk of colorectal cancer. However, according to the systematic review of RCTs, the incidence of cancer in patients treated with GLP1RA is low. Larger sample sizes of RCTs with long-term follow-up are necessary to establish the incidence of cancers and evaluate the risk-benefit ratios.

Causal association of dietary factors with five common cancers: univariate and multivariate Mendelian randomization studies

Background

Daily dietary habits are closely related to human health, and long-term unhealthy dietary intake, such as excessive consumption of alcohol and pickled foods, may promote the development of cancers. However, comprehensive research on the causal relationship between dietary habits and cancer is lacking. Therefore, this study aimed to reveal the potential causal link between dietary risk factors and the prognosis of cancer-related to genetic susceptibility.

Methods

GWAS (Genome-Wide Association Studies) summary data on dietary habits and five common types of cancer and their pathological subtypes were obtained from the UK Biobank and various cancer association consortia. A univariable two-sample Mendelian randomization (UVMR) and FDR correction analysis was conducted to explore the causal relationships between 45 dietary habits and five common types of cancer and their histopathological subtypes. In addition, multivariable Mendelian randomization analysis (MVMR) was performed to adjust for traditional risk factors for dietary habits, and the direct or indirect effects of diet on cancer were evaluated. Finally, the prognostic impact of selected instrumental variables on cancer was analyzed using an online data platform.

Results

In the UVMR analysis, four dietary habits were identified as risk factors for cancer, while five dietary habits were identified as protective factors. Among the latter, one dietary habit showed a significant association with cancer even after FDR correction, indicating a potential causal relationship. The MVMR analysis revealed that weekly beer and cider intake, may act as an independent risk factor for cancer development. Other causal associations between dietary habits and cancer risk may be mediated by intermediate factors. In the prognostic analysis, the SNPs (Single Nucleotide Polymorphisms) of average weekly beer and cider intake were set as independent risk factors and were found to significantly impact overall survival (OS) and cancer-specific survival (CSS) in lung cancer.

Conclusion

This causal relationship study supports the notion that adjusting daily dietary habits and specific dietary interventions may decrease the risk of 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.

A comprehensive analysis of germline predisposition to early-onset ovarian cancer

The subset of ovarian cancer (OC) diagnosed ≤ 30yo represents a distinct subgroup exhibiting disparities from late-onset OC in many aspects, including indefinite germline cancer predisposition. We performed DNA/RNA-WES with HLA-typing, PRS assessment and survival analysis in 123 early-onset OC-patients compared to histology/stage-matched late-onset and unselected OC-patients, and population-matched controls. Only 6/123(4.9%) early-onset OC-patients carried a germline pathogenic variant (GPV) in high-penetrance OC-predisposition genes. Nevertheless, our comprehensive germline analysis of early-onset OC-patients revealed two divergent trajectories of potential germline susceptibility. Firstly, overrepresentation analysis highlighted a connection to breast cancer (BC) that was supported by the CHEK2 GPV enrichment in early-onset OC(p = 1.2 × 10-4), and the presumably BC-specific PRS313, which successfully stratified early-onset OC-patients from controls(p = 0.03). The second avenue pointed towards the impaired immune response, indicated by LY75-CD302 GPV(p = 8.3 × 10-4) and diminished HLA diversity compared with controls(p = 3 × 10-7). Furthermore, we found a significantly higher overall GPV burden in early-onset OC-patients compared to controls(p = 3.8 × 10-4). The genetic predisposition to early-onset OC appears to be a heterogeneous and complex process that goes beyond the traditional Mendelian monogenic understanding of hereditary cancer predisposition, with a significant role of the immune system. We speculate that rather a cumulative overall GPV burden than specific GPV may potentially increase OC risk, concomitantly with reduced HLA diversity.