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Objective: Few studies have evaluated the benefits of colorectal cancer (CRC) screening integrating both non-genetic and genetic risk factors. Here, we aimed to integrate an existing non-genetic risk model (QCancer-10) and a 139-variant polygenic risk score to evaluate the effectiveness of screening on CRC incidence and mortality. Methods: We applied the integrated model to calculate 10-year CRC risk for 430,908 participants in the UK Biobank, and divided the participants into low-, intermediate-, and high-risk groups. We calculated the screening-associated hazard ratios (HRs) and absolute risk reductions (ARRs) for CRC incidence and mortality according to risk stratification. Results: During a median follow-up of 11.03 years and 12.60 years, we observed 5,158 CRC cases and 1,487 CRC deaths, respectively. CRC incidence and mortality were significantly lower among screened than non-screened participants in both the intermediate- and high-risk groups [incidence: HR: 0.87, 95% confidence interval (CI): 0.81–0.94; 0.81, 0.73–0.90; mortality: 0.75, 0.64–0.87; 0.70, 0.58–0.85], which composed approximately 60% of the study population. The ARRs (95% CI) were 0.17 (0.11–0.24) and 0.43 (0.24–0.61), respectively, for CRC incidence, and 0.08 (0.05–0.11) and 0.24 (0.15–0.33), respectively, for mortality. Screening did not significantly reduce the relative or absolute risk of CRC incidence and mortality in the low-risk group. Further analysis revealed that screening was most effective for men and individuals with distal CRC among the intermediate to high-risk groups. Conclusions: After integrating both genetic and non-genetic factors, our findings provided priority evidence of risk-stratified CRC screening and valuable insights for the rational allocation of health resources.

Abstract Background Kidney cancer has become known as a metabolic disease. However, there is limited evidence linking metabolic syndrome (MetS) with kidney cancer risk. This study aimed to investigate the association between MetS and its components and the risk of kidney cancer. Methods UK Biobank data was used in this study. MetS was defined as having three or more metabolic abnormalities, while pre-MetS was defined as the presence of one or two metabolic abnormalities. Hazard ratios (HRs) and 95% confidence intervals (CIs) for kidney cancer risk by MetS category were calculated using multivariable Cox proportional hazards models. Subgroup analyses were conducted for age, sex, BMI, smoking status and drinking status. The joint effects of MetS and genetic factors on kidney cancer risk were also analyzed. Results This study included 355,678 participants without cancer at recruitment. During a median follow-up of 11 years, 1203 participants developed kidney cancer. Compared to the metabolically healthy group, participants with pre-MetS (HR= 1.36, 95% CI: 1.06-1.74) or MetS (HR= 1. 70, 95% CI: 1.30-2.23) had a significantly greater risk of kidney cancer. This risk increased with the increasing number of MetS components (P for trend < 0.001). The combination of hypertension, dyslipidemia and central obesity contributed to the highest risk of kidney cancer (HR= 3.03, 95% CI: 1.91-4.80). Compared with participants with non-MetS and low genetic risk, those with MetS and high genetic risk had the highest risk of kidney cancer (HR= 1. 74, 95% CI: 1.41-2.14). Conclusions Both pre-MetS and MetS status were positively associated with kidney cancer risk. The risk associated with kidney cancer varied by combinations of MetS components. These findings may offer novel perspectives on the aetiology of kidney cancer and assist in designing primary prevention strategies.

BackgroundAlthough the risk of prostate cancer (PCa) varies across different ages and genetic risks, it’s unclear about the effects of genetic-specific and age-specific prostate-specific antigen (PSA) screening for PCa.MethodsWeighed and unweighted polygenic risk scores (PRS) were constructed to classify the participants from the PLCO trial into low- or high-PRS groups. The age-specific and PRS-specific cut-off values of PSA for PCa screening were determined with time-dependent receiver-operating-characteristic curves and area-under-curves (tdAUCs). Improved screening strategies integrating PRS-specific and age-specific cut-off values of PSA were compared to traditional PSA screening on accuracy, detection rates of high-grade PCa (Gleason score ≥7), and false positive rate.ResultsWeighted PRS with 80 SNPs significantly associated with PCa was determined as the optimal PRS, with an AUC of 0.631. After stratifying by PRS, the tdAUCs of PSA with a 10-year risk of PCa were 0.818 and 0.816 for low- and high-PRS groups, whereas the cut-off values were 1.42 and 1.62 ng/mL, respectively. After further stratifying by age, the age-specific cut-off values of PSA were relatively lower for low PRS (1.42, 1.65, 1.60, and 2.24 ng/mL for aged

Background: Although poor oral health and several lifestyle factors have been found to be associated with cancer risk, their joint relationship has rarely been studied. Methods: We prospectively examined the associations of oral health and healthy lifestyle factors with cancer risk among 0.5 million rural and urban residents from the China Kadoorie Biobank (2004–2015). Oral health status was assessed from self-reported baseline questionnaires. A healthy lifestyle index comprising non-smoking, non-drinking, ideal body shape, physical activity and healthy diet was calculated for each participant, and categorized into favorable, intermediate and unfavorable lifestyle behavior. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) relating oral health and healthy lifestyle index to cancer risk using Cox proportional hazards models. We estimated the population attributable risk percent (PAR%) and 95% CIs using multivariate models. Results: During a median follow-up of 9 years, 23,805 new cancer cases were documented, with 52% from rural areas and 48% from urban areas. Compared with those with good oral health and favorable lifestyle, participants with poor oral health and unfavorable lifestyle had a higher risk of developing cancer in both rural (adjusted HR, 1.55 [95% CI, 1.39–1.74]; P for trend < 0.001) and urban areas (adjusted HR, 1.44 [95% CI, 1.24–1.67]; P for trend < 0.001). A significant multiplicative interaction between oral health and healthy lifestyle index on cancer risk was found in rural residents (P for interaction = 0.004) rather than in urban residents (P for interaction = 0.973). Assuming poor oral health as an additional risk factor, the PAR% of total cancer increased by 3.0% and 1.1% for participants with intermediate lifestyle and unfavorable lifestyle, respectively. Conclusions: These findings suggest a joint effect of oral health and common lifestyle factors on cancer risk. Promotion of healthy lifestyle by integration of good oral health would be beneficial to consider in cancer prevention strategies.

Abstract Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications.

Abstract 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

Objective: To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas. Methods: Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women. Results: A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45–65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results. Conclusions: The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.

The M protein of the novel coronavirus 2019 (SARS-CoV-2) is the major structural component of the viral envelope and is also the minimum requirement for virus particle budding. M proteins generally exist as dimers. In virus assembly, they are the main driving force for envelope formation through lateral interactions and interactions with other viral structural proteins that play a central role. We built 100 candidate models and finally analyzed the six most convincing structural features of the SARS-CoV-2 M protein dimer based on long-timescale molecular dynamics (MD) simulations, multiple free energy analyses (potential mean force (PMF) and molecular mechanics Poisson-Boltzmann surface area (MMPBSA)) and principal component analysis (PCA) to obtain the most reasonable structure. The dimer stability was found to depend on the Leu-Ile zipper motif and aromatic amino acids in the transmembrane domain (TMD). Furthermore, the C-terminal domain (CTD) effects were relatively small. These results highlight a model in which there is sufficient binding affinity between the TMDs of M proteins to form dimers through the residues at the interface of the three transmembrane helices (TMHs). This study aims to help find more effective inhibitors of SARS-CoV-2 M dimers and to develop vaccines based on structural information.

Abstract 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.

Abstract Background Diabetes mellitus and thyroid disease are two areas of broad interest in the field of endocrinology and metabolism. Variation of thyroid hormone concentration, even within the normal range, may portend the onset of type 2 diabetes mellitus (T2DM), especially among those with prediabetes. However, current evidence is mixed. Methods Prospective studies which assessed diabetes incidence were identified using a database search of MEDLINE and Embase through May 1, 2021. The Sidik-Jonkman random-effects model and cubic spline model were used to evaluate the associations and dose-response relationships between thyroid function/hormone levels and risk of T2DM and cardiovascular disease (CVD) risk among T2DM patients. Results A total of 12 prospective studies were included. We found that high baseline TSH levels were related to a 17% higher risk of T2DM (RR 1.17, 95% CI 1.01, 1.36; I 2=78%, P

Summary: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Summary: The comprehensive regulation effect of eRNA on tumor immune cell infiltration and the outcome remains obscure. We comprehensively identify the eRNA-mediated immune infiltration patterns of gastric cancer (GC) samples. We creatively proposed a random forest machine-learning (ML) algorithm to map eRNA to mRNA expression patterns. The eRNA score was constructed using principal component analysis algorithms and validated in an independent cohort. Three subtypes with distinct eRNA expression patterns were determined in GC. There were significant differences between the three subtypes in the overall survival rate, immune cell infiltration characteristics, and immunotherapy response indicators. The patients in the high eRNA score group have a higher overall survival rate and might benefit from immunotherapy. This work revealed that eRNA regulation might be a new prognostic index and might offer a potential biomarker in the response of immunotherapy. Evaluating the eRNA regulation manner of GC will contribute to guiding more effective immunotherapy strategies.

The majority of non-melanoma skin cancer (NMSC) is cutaneous basal cell carcinoma (BCC) or squamous cell carcinoma (SCC), which are also called keratinocyte carcinomas, as both of them originate from keratinocytes. The incidence of keratinocyte carcinomas is over 5 million per year in the US, three-fold higher than the total incidence of all other types of cancer combined. While there are several reports on gene expression profiling of BCC and SCC, there are significant variations in the reported gene expression changes in different studies. One reason is that tumor-adjacent normal skin specimens were not included in many studies as matched controls. Furthermore, while numerous studies of skin stem cells in mouse models have been reported, their relevance to human skin cancer remains unknown. In this report, we analyzed gene expression profiles of paired specimens of keratinocyte carcinomas with their matched normal skin tissues as the control. Among several novel findings, we discovered a significant number of zinc finger encoding genes up-regulated in human BCC. In BCC, a novel link was found between hedgehog signaling, Wnt signaling, and the cilium. While the SCC cancer-stem-cell gene signature is shared between human and mouse SCCs, the hair follicle stem-cell signature of mice was not highly represented in human SCC. Differential gene expression (DEG) in human BCC shares gene signature with both bulge and epidermal stem cells. We have also determined that human BCCs and SCCs have distinct gene expression patterns, and some of them are not fully reflected in current mouse models.

Summary: Background: There is a strong connection between oral health and overall wellness. We aim to examine the association between poor oral health and the risk of developing or dying of cancer, and whether the association differs by residential area. Methods: Between 2004 and 2008, a total of 510,148 adults free of cancer were included from the China Kadoorie Biobank study and thereafter followed up to 2015. Poor oral health was assessed from a self-reported baseline questionnaire and defined as a combination of rarely brushing teeth and always gum bleeding. We used Cox proportional hazards models to estimate the hazard ratio (HR) of cancer risk and its associated 95% confidence interval (CI) according to oral health status. Findings: Overall, 14.9% of participants (19.7% in rural areas and 8.8% in urban areas) reported poor oral health at baseline. After 4,602,743 person-years of follow-up, we identified 23,805 new cancer cases and 11,973 cancer deaths, respectively. Poor oral health was associated with higher risks of total cancer incidence (HR: 1.08, 95% CI: 1.04–1.12) and death (HR: 1.10, 95% CI: 1.05–1.16). For the site-specific cancers, poor oral health was significantly associated with higher risk of stomach cancer incidence (cases: 2964, HR: 1.10, 95% CI: 1.00–1.22), esophageal cancer incidence (cases: 2119, HR: 1.19, 95% CI: 1.07–1.33), esophageal cancer death (cases: 1238, HR: 1.29, 95% CI: 1.12–1.49), liver cancer incidence (cases: 2565, HR: 1.18, 95% CI: 1.06–1.32), and liver cancer death (cases: 1826, HR: 1.20, 95% CI: 1.05–1.36). This positive association was stronger among rural residents compared to urban residents (interaction test P < 0.01). Interpretation: Our findings indicate that poor oral health is associated with higher risk for cancers, especially digestive system cancers. Promotion of oral health in the general population, especially for rural residents, could have valuable public health significance in preventing major systemic diseases. Funding: Supported by grants (2021YFC2500400, 2016YFC0900500, 2016YFC0900501, 2016YFC0900504) from the National Key Research and Development Program of China, grants from the Kadoorie Charitable Foundation in Hong Kong and grants grants (088158/Z/09/Z, 104085/Z/14/Z, 202922/Z/16/Z) from Wellcome Trust in the UK. CKB is supported by the Kadoorie Charitable Foundation (KCF) in Hong Kong.

Abstract Background Both coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) are caused by coronaviruses and have infected people in China and worldwide. We aimed to investigate whether COVID-19 and SARS exhibited similar spatial and temporal features at provincial level in mainland China. Methods The number of people infected by COVID-19 and SARS were extracted from daily briefings on newly confirmed cases during the epidemics, as of Mar. 4, 2020 and Aug. 3, 2003, respectively. We depicted spatiotemporal patterns of the COVID-19 and SARS epidemics using spatial statistics such as Moran’s I and the local indicators of spatial association (LISA). Results Compared to SARS, COVID-19 had a higher overall incidence. We identified 3 clusters (predominantly located in south-central China; the highest RR = 135.08, 95% CI: 128.36–142.08) for COVID-19 and 4 clusters (mainly in Northern China; the highest RR = 423.51, 95% CI: 240.96–722.32) for SARS. Fewer secondary clusters were identified after the “Wuhan lockdown”. The LISA cluster map detected a significantly high-low (Hubei) and low-high spatial clustering (Anhui, Hunan, and Jiangxi, in Central China) for COVID-19. Two significant high-high (Beijing and Tianjin) and low-high (Hebei) clusters were detected for SARS. Conclusions COVID-19 and SARS outbreaks exhibited distinct spatiotemporal clustering patterns at the provincial levels in mainland China, which may be attributable to changes in social and demographic factors, local government containment strategies or differences in transmission mechanisms.

Background: Prior studies on the relationship between bowel health and mortality have generally focused on the individual association of stool frequency or consistency with mortality but did not present a joint association. Therefore, we aimed to systematically evaluate the individual and joint associations of stool frequency and consistency with all-cause and cause-specific mortality in this study. Methods: A total of 14,574 participants from the National Health and Nutrition Examination Survey 2005–2010 were incorporated in this analysis. Survey sample-weighted Cox proportional hazards models adjusted for potential confounders were used to estimate hazard ratios (HRs) between bowel health measures and mortality risks. Results: During a median of 7.6 years of follow-up, 1502 deaths occurred, including 357 cancer deaths and 284 cardiovascular disease (CVD) deaths. The bowel habit of the most participants was 7 times/week (50.7%), and the most common type was “Like a sausage or snake, smooth and soft” (51.8%). Stool frequency displayed a parabolic relationship with all-cause mortality, and less than 7 times/week is a significant risk factor for mortality (HR for 1 time/week: 1.43, p-values = 0.04. HR for 6 times/week: 1.05, p-value = 0.03). Analyzing the joint association of stool frequency and consistency on mortality clarified the limitations of only inspecting the effects of either individual factor. Compared with 7 times/week of normal stool, infrequent soft stools at 4 times/week were associated with 1.78-, 2.42-, and 2.27-times higher risks of all-cause, cancer, and CVD mortality, respectively. Conclusion: Analyses of bowel health should consider the joint effects of stool frequency and stool consistency. Self-appraisal of stool frequency and consistency may be a simple but useful tool for informing about major chronic illnesses.

Despite robust evidence for the role of m6A in cancer development and progression, its association with immune infiltration and survival outcomes in melanoma remains obscure. Here, we aimed to develop an m6A-related risk signature to improve prognostic and immunotherapy responder prediction performance in the context of melanoma. We comprehensively analyzed the m6A cluster and immune infiltration phenotypes of public datasets. The TCGA (n = 457) and eleven independent melanoma cohorts (n = 758) were used as the training and validation datasets, respectively. We identified two m6A clusters (m6A-clusterA and m6A-clusterB) based on the expression pattern of m6A regulators via unsupervised consensus clustering. IGF2BP1 (7.49%), KIAA1429 (7.06%), and YTHDC1 (4.28%) were the three most frequently mutated genes. There was a correlation between driver genes mutation statuses and the expression of m6A regulators. A significant difference in tumor-associated immune infiltration between two m6A clusters was detected. Compared with m6A-clusterA, the m6A-clusterB was characterized by a lower immune score and immune cell infiltration but higher mRNA expression-based stemness index (mRNAsi). An m6A-related risk signature consisting of 12 genes was determined via Cox regression analysis and divided the patients into low- and high-risk groups (IL6ST, MBNL1, NXT2, EIF2A, CSGALNACT1, C11orf58, CD14, SPI1, NCCRP1, BOK, CD74, PAEP). A nomogram was developed for the prediction of the survival rate. Compared with the high-risk group, the low-risk group was characterized by high expression of immune checkpoints and immunophenoscore (IPS), activation of immune-related pathways, and more enriched in immune cell infiltrations. The low-risk group had a favorable prognosis and contained the potential beneficiaries of the immune checkpoint blockade therapy and verified by the IMvigor210 cohort (n = 298). The m6A-related signature we have determined in melanoma highlights the relationships between m6A regulators and immune cell infiltration. The established risk signature was identified as a promising clinical biomarker of melanoma.

Observational studies suggested inconsistent associations between lipid-lowering drugs, such as statins, and renal cell carcinoma (RCC) risk. In a two-sample Mendelian randomization (MR) framework, we assessed the causal influence of lipid-lowering agents and circulating lipid traits on overall and sex-specific RCC risk. Genetic variants of six drug-target genes were selected to proxy the effects of low-density lipoprotein cholesterol (LDL-C) lowering therapies. Instrumental variables for circulating lipid traits were constructed from two large genome-wide association studies. We used endpoints for RCC from summary statistics of two studies [International Agency for Research on Cancer [IARC], N = 13,230; National Cancer Institute [NCI], N = 4,735]. The robustness of results was assessed through conventional MR sensitivity analyses. Overall, there was no significant association between genetically proxied HMG-CoA reductase (HMGCR) inhibition and RCC risk [Odds ratio [OR] = 1.42, 95% CI, 0.29–6.99]. In the sex-stratified analysis, we observed a positive association for genetically proxied drug targets with RCC risk. Specifically, genetically proxied proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition was associated with a higher risk of RCC in men [OR = 2.20 [95% CI, 1.24–3.89]], and the difference by sex was moderate. This study suggested genetically proxied inhibition of HMGCR was not associated with RCC risk, while genetically proxied PCSK9 inhibition might be associated with a higher risk of RCC in male.

Abstract Age at menarche (AAM) was found to be associated with ovarian cancer risk in previous observational studies. However, the causality of this association remains unclear. Here, after systematic meta‐analyses, we performed two‐sample Mendelian randomization (MR) analyses to evaluate the causal effect of AAM in epithelial ovarian cancer (EOC) etiology. We performed meta‐analyses including 11 410 cases and 1 163 117 noncases to quantitatively evaluate the association between AAM and ovarian cancer risk. In MR analyses, we used 25 single nucleotide polymorphisms (SNPs) associated with AAM for Chinese and 390 SNPs for Europeans as instrumental variables. MR estimates were calculated using inverse‐variance weighted methods from 1044 cases and 1172 controls in a Chinese genome‐wide association study and validated by the Ovarian Cancer Association Consortium and Consortium of Investigators of Modifiers of BRCA1/2 studies with 29 396 cases and 68 502 controls of European ancestry. In meta‐analyses, we observed an inverse association (odds ratio [OR] = 0.96, 95% confidence interval [CI] = 0.93 to 1.00, P = 0.036) between per year older AAM and ovarian cancer risk in case–control studies, but no association was observed in cohort studies. In MR analyses, the OR of EOC risk per year increase in AAM was 0.81 (95% CI = 0.67 to 0.97, P = 0.026) in Chinese and 0.94 (95% CI = 0.90 to 0.98, P = 0.003) in Europeans, respectively. Our study supports a causal association between AAM and EOC risk.

Background: Nutritional, environmental, and metabolic status may play a role in affecting the progression and prognosis of type 2 diabetes. However, results in identifying prognostic biomarkers among diabetic patients have been inconsistent and inconclusive. We aimed to evaluate the associations of nutritional, environmental, and metabolic status with disease progression and prognosis among diabetic patients. Methods: In a nationally representative sample in the NHANES III (The Third National Health and Nutrition Examination Survey, 1988–1994), we analyzed available data on 44 biomarkers among 2113 diabetic patients aged 20 to 90 years (mean age: 58.2 years) with mortality data followed up through 2016. A panel of 44 biomarkers from blood and urine specimens available from NHANES III were included in this study and the main outcomes as well as the measures are mortalities from all-causes. We performed weighted logistic regression analyses after controlling potential confounders. To assess incremental prognostic values of promising biomarkers beyond traditional risk factors, we compared c-statistics of the adjusted models with and without biomarkers, separately. Results: In total, 1387 (65.2%) deaths were documented between 1988 and 2016. We observed an increased risk of all-cause mortality associated with higher levels of serum C-reactive protein (p for trend = 0.0004), thyroid stimulating hormone (p for trend = 0.04), lactate dehydrogenase (p for trend = 0.02), gamma glutamyl transferase (p for trend = 0.02), and plasma fibrinogen (p for trend = 0.03), and urine albumin (p for trend < 0.0001). In contrast, higher levels of serum sodium (p for trend = 0.005), alpha carotene (p for trend = 0.006), and albumin (p for trend = 0.005) were associated with a decreased risk of all-cause mortality. In addition, these significant associations were not modified by age, sex, or race. Inclusion of thyroid stimulating hormone (p = 0.03), fibrinogen (p = 0.01), and urine albumin (p < 0.0001), separately, modestly improved the discriminatory ability for predicting all-cause mortality among diabetic patients. Conclusions: Our nationwide study findings provide strong evidence that some nutritional, environmental, and metabolic biomarkers were significant predictors of all-cause mortality among diabetic patients and may have potential clinical value for improving stratification of mortality risk.

Identifying causal regulatory variants and their target genes from the majority of non-coding disease-associated genetic loci is the main challenge in post-Genome-Wide Association Studies (GWAS) functional studies. Although chromosome conformation capture (3C) and its derivative technologies have been successfully applied to nominate putative causal genes for non-coding variants, many GWAS target genes have not been identified yet. This study generated a high-resolution contact map from epithelial ovarian cancer (EOC) cells with two H3K27ac-HiChIP libraries and analyzed the underlying gene networks for 15 risk loci identified from the largest EOC GWAS. By combinatory analysis of 4,021 fine-mapped credible variants of EOC GWAS and high-resolution contact map, we obtained 162 target genes that mainly enriched in cancer related pathways. Compared with GTEx eQTL genes in ovarian tissue and annotated proximal genes, 132 HiChIP targets were first identified for EOC causal variants. More than half of the credible variants (CVs) involved interactions that were over 185 kb in distance, indicating that long-range transcriptional regulation is an important mechanism for the function of GWAS variants in EOC. We also found that many HiChIP gene targets showed significantly differential expressions between normal ovarian and EOC tumor samples. We validated one of these targets by manipulating the rs9303542 located region with CRISPR-Cas9 deletion and dCas9-VP64 activation experiments and found altered expression of HOXB7 and HOXB8 at 17q21.32. This study presents a systematic analysis to identify putative target genes for causal variants of EOC, providing an in-depth investigation of the mechanisms of non-coding regulatory variants in the etiology and pathogenesis of ovarian cancer.

Abstract Background The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). Methods Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. Results Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76–1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91–1.06), 1.07 (95% CI 0.66–1.74) and 2.29 (95% CI 1.95–2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31–2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53–0.56) and 0.75 (95% CI 0.63–0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59–0.63), 0.55 (95% CI 0.52–0.58) and 0.58 (95% CI 0.55–0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27–0.89), 0.91 (95% CI 0.87–0.94) and 17.38 (95% CI 2.66–113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17–0.59), 0.86 (95% CI 0.76–0.92) and 3.38 (95% CI 1.40–8.17), respectively. Conclusions The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. Trial registration PROSPERO CRD42016047215.