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IntroductionAccurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images.MethodsOur approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process.ResultsWe evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation.ConclusionThe introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

Abstract Genetic and epigenetic alterations occur in many physiological and pathological processes. The existing knowledge regarding the association of PIWI‐interacting RNAs (piRNAs) and their genetic variants on risk and progression of prostate cancer (PCa) is limited. In this study, three genome‐wide association study datasets are combined, including 85,707 PCa cases and 166,247 controls, to uncover genetic variants in piRNAs. Functional investigations involved manipulating piRNA expression in cellular and mouse models to study its oncogenetic role in PCa. A specific genetic variant, rs17201241 is identified, associated with increased expression of PROPER (piRNA overexpressed in prostate cancer) in tumors and are located within the gene, conferring an increased risk and malignant progression of PCa. Mechanistically, PROPER coupled with YTHDF2 to recognize N6‐methyladenosine (m6A) and facilitated RNA‐binding protein interactions between EIF2S3 at 5′‐untranslated region (UTR) and YTHDF2/YBX3 at 3′‐UTR to promote DUSP1 circularization. This m6A‐dependent mRNA‐looping pattern enhanced DUSP1 degradation and inhibited DUSP1 translation, ultimately reducing DUSP1 expression and promoting PCa metastasis via the p38 mitogen‐activated protein kinase (MAPK) signaling pathway. Inhibition of PROPER expression using antagoPROPER effectively suppressed xenograft growth, suggesting its potential as a therapeutic target. Thus, targeting piRNA PROPER‐mediated genetic and epigenetic fine control is a promising strategy for the concurrent prevention and treatment of PCa.

Abstract The development of an effective survival prediction tool is key for reducing colorectal cancer mortality. Here, we apply a three-stage study to devise a polygenic prognostic score (PPS) for stratifying colorectal cancer overall survival. Leveraging two cohorts of 3703 patients, we first perform a genome-wide survival association analysis to develop eight candidate PPSs. Further using an independent cohort with 470 patients, we identify the 287 variants-derived PPS (i.e., PPS287) achieving an optimal prediction performance [hazard ratio (HR) per SD = 1.99, P = 1.76 × 10−8], accompanied by additional tests in two external cohorts, with HRs per SD of 1.90 (P = 3.21 × 10−14; 543 patients) and 1.80 (P = 1.11 × 10−9; 713 patients). Notably, the detrimental impact of pathologic characteristics and genetic risk could be attenuated by a healthy lifestyle, yielding a 7.62% improvement in the 5-year overall survival rate. Therefore, our findings demonstrate the integrated contribution of pathologic characteristics, germline variants, and lifestyle exposure to the prognosis of colorectal cancer patients.

Abstract Background Early-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification. Methods We included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes. Results The 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99–2.78] than LOPC (HR = 1.95, 95% CI 1.89–2.01; I 2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98–5.54) but not to LOPC (HR = 0.98, 95% CI 0.96–1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I 2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Pro state cancer A ge-based P heWAS; https://mulongdu.shinyapps.io/proap ), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures. Conclusions This study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.

Exposure to persistent organic pollutants (POPs) may contribute to colorectal cancer risk, but the underlying mechanisms of crucial POPs exposure remain unclear. Hence, we systematically investigated the associations among POPs exposure, genetics and epigenetics and their effects on colorectal cancer. A case-control study was conducted in the Chinese population for detecting POPs levels. We measured the concentrations of 24 POPs in the plasma using gas chromatography-tandem mass spectrometry (GC–MS/MS) and evaluated the clinical significance of POPs by calculating the area under the receiver operating characteristic curve (AUC). To assess the associations between candidate genetic variants and colorectal cancer risk, unconditional logistic regression was used. Compared with healthy control individuals, individuals with colorectal cancer exhibited higher concentrations of the majority of POPs. Exposure to PCB153 was positively associated with colorectal cancer risk, and PCB153 demonstrated superior accuracy (AUC=0.72) for predicting colorectal cancer compared to other analytes. On PCB153-related genes, the rs67734009 C allele was significantly associated with reduced colorectal cancer risk and lower plasma levels of PCB153. Moreover, rs67734009 exhibited an expression quantitative trait locus (eQTL) effect on ESR1, of which the expression level was negatively related to PCB153 concentration. Mechanistically, the risk allele of rs67734009 increased ESR1 expression via miR-3492 binding and m6A modification. Collectively, this study sheds light on potential genetic and epigenetic mechanisms linking PCB153 exposure and colorectal cancer risk, thereby providing insight into the accurate protection against POPs exposure.

Advances in deep learning have revolutionized medical image segmentation, facilitating the precise delineation of complex anatomical structures. The scarcity of annotated training samples remains a significant bottleneck. To tackle the data limitation, federated learning (FL) offers the promise of pooling data from multiple healthcare institutions. However, as models grow larger, the increase in communication costs restricts FL to fewer nodes, which constrains the volume of data. This situation necessitates the simultaneous achievement of model lightweighting. To address this problem, this study proposes FKD-Med, a novel framework that integrates FL for privacy-sensitive data amalgamation across multiple healthcare institutions, and uses knowledge distillation (KD) to enhance communication efficiency. The “Med” in FKD-Med refers to medical application computational problems. Our principal contributions encompass the design of an open-source framework that seamlessly blends FL and KD, rendering it applicable to a broad spectrum of medical informatics tasks. Our approach substantially augments the computational data volume, thereby boosting both communication efficiency and training throughput. Tested on two datasets of medical image segmentation using TransUNet and ResUNet as teacher models, FKD-Med achieves data privacy, lowers communication costs, and increases accuracy. The parameters of the student models were reduced to 1/127 and 1/1027 of those in the teacher models. Additionally, the models subjected to KD exhibited accuracy improvements of 0.25%, 0.43%, 1.35%, and 1.46% respectively, given the same parameter volume. This positions FKD-Med not only as a pivotal tool for multi-institutional medical research but also as a versatile platform adaptable to a wide array of real-world medical engineering applications. The code is publicly available at https://github.com/SUN-1024/FKD-Med.

Abstract In rapid development, Selective Laser Sintering (SLS) creates prototypes by processing industrial materials, for example, polymers. Such materials are usually in powder form and fused by a laser beam. The manufacturing quality depends on the interaction between a high‐energy laser beam and the powdered material. However, in‐homogeneous temperature distribution, unstable laser powder, and inconsistent powder densities can cause defects in the final product, for example, Powder Bed Defects. Such factors can lead to irregularities, for example, warping, distortion, and inadequate powder bed fusion. These irregularities may affect the profitable SLS production. Consequently, detecting powder bed defects requires automation. An ensemble learning‐based approach is proposed for detecting defects in SLS powder bed images from this perceptive. The proposed approach first pre‐processes the images to reduce the computational complexity. Then, the Convolutional Neural Network (CNN) based ensembled models (off‐the‐shelf CNN, bagged CNN, and boosted CNN) are implemented and compared. The ensemble learning CNN (bagged and boosted CNN) is good for powder bed detection. The evaluation results indicate that the performance of bagged CNN is significant. It also indicates that preprocessing of the images, mainly cropping to the region of interest, improves the performance of the proposed approach. The training and testing accuracy of the bagged CNN is 96.1% and 95.1%, respectively.

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet.

BackgroundHypertension significantly impacts the well-being and health of individuals globally. Hypertension management apps (HMAs) have been shown to assist patients in controlling blood pressure (BP), with their efficacy validated in clinical trials. However, the utilization of HMAs continues to be suboptimal. Presently, there is a dearth of real-world research based on big data and exploratory mining that compares Chinese and American HMAs. ObjectiveThis study aims to systematically gather HMAs and their user reviews from both China and the United States. Subsequently, using data mining techniques, the study aims to compare the user experience, satisfaction levels, influencing factors, and asymmetry between Chinese and American users of HMAs. In addition, the study seeks to assess the disparities in satisfaction and its determinants while delving into the asymmetry of these factors. MethodsThe study sourced HMAs and user reviews from 10 prominent Chinese and American app stores globally. Using the latent Dirichlet allocation (LDA) topic model, the research identified various topics within user reviews. Subsequently, the Tobit model was used to investigate the impact and distinctions of each topic on user satisfaction. The Wald test was applied to analyze differences in effects across various factors. ResultsWe examined a total of 261 HMAs along with their associated user reviews, amounting to 116,686 reviews in total. In terms of quantity and overall satisfaction levels, Chinese HMAs (n=91) and corresponding reviews (n=16,561) were notably fewer compared with their American counterparts (n=220 HMAs and n=100,125 reviews). The overall satisfaction rate among HMA users was 75.22% (87,773/116,686), with Chinese HMAs demonstrating a higher satisfaction rate (13,866/16,561, 83.73%) compared with that for American HMAs (73,907/100,125, 73.81%). Chinese users primarily focus on reliability (2165/16,561, 13.07%) and measurement accuracy (2091/16,561, 12.63%) when considering HMAs, whereas American users prioritize BP tracking (17,285/100,125, 17.26%) and data synchronization (12,837/100,125, 12.82%). Seven factors (easy to use: P

Environmental pollutants known as polycyclic aromatic hydrocarbons (PAHs) are produced through the incomplete combustion of organic material. While PAHs have been investigated as genotoxicants, they can also operate through nongenotoxic pathways in estrogen-dependent malignancies, such as breast, cervical and ovarian cancer. However, whether PAHs induce colorectal cancer (CRC) risk through estrogenic effects is still illusive. Here, we systematically investigated the abnormal expression and activation of estrogen receptor beta (ERβ) regulated by PAHs in CRC as well as the underlying mechanisms of ERβ-mediated CRC risk. Based on the 300 plasma samples from CRC patients and healthy controls detected by GC–MS/MS, we found that the plasma concentrations of benzo[a]pyrene (BaP) were significantly higher in CRC cases than in healthy controls, with significant estrogenic effects. Moreover, histone deacetylase 2 (HDAC2)-induced deacetylation of the promoter decreases ERβ expression, which is associated with poor overall survival and advanced tumor stage. The study also revealed that BaP and estradiol (E2) had different carcinogenic effects, with BaP promoting cell proliferation and inhibiting apoptosis, while E2 had the opposite effects. Additionally, this study mapped ERβ genomic binding regions by performing ChIP-seq and ATAC-seq and identified genetic variants of rs1411680 and its high linkage disequilibrium SNP rs6477937, which were significantly associated with CRC risk through meta-analysis of two independent Chinese population genome-wide association studies comprising 2,248 cases and 3,173 controls and then validation in a large-scale European population. By integrating data from functional genomics, we validated the regulatory effect of rs6477937 as an ERβ binding-disrupting SNP that mediated allele-specific expression of LINC02977 in a long-range chromosomal interaction manner, which was found to be highly expressed in CRC tissues. Overall, this study suggests that the different active effects on ERβ by PAHs and endogenous E2 may play a crucial role in the development and progression of CRC and highlights the potential of targeting ERβ and its downstream targets for CRC prevention and treatment.

Abstract Background The genetic architectures of colorectal cancer are distinct across different populations. To date, the majority of polygenic risk scores (PRSs) are derived from European (EUR) populations, which limits their accurate extrapolation to other populations. Here, we aimed to generate a PRS by incorporating East Asian (EAS) and EUR ancestry groups and validate its utility for colorectal cancer risk assessment among different populations. Methods A large-scale colorectal cancer genome-wide association study (GWAS), harboring 35,145 cases and 288,934 controls from EAS and EUR populations, was used for the EAS-EUR GWAS meta-analysis and the construction of candidate EAS-EUR PRSs via different approaches. The performance of each PRS was then validated in external GWAS datasets of EAS (727 cases and 1452 controls) and EUR (1289 cases and 1284 controls) ancestries, respectively. The optimal PRS was further tested using the UK Biobank longitudinal cohort of 355,543 individuals and ultimately applied to stratify individual risk attached by healthy lifestyle. Results In the meta-analysis across EAS and EUR populations, we identified 48 independent variants beyond genome-wide significance (P < 5 × 10−8) at previously reported loci. Among 26 candidate EAS-EUR PRSs, the PRS-CSx approach-derived PRS (defined as PRSCSx) that harbored genome-wide variants achieved the optimal discriminatory ability in both validation datasets, as well as better performance in the EAS population compared to the PRS derived from known variants. Using the UK Biobank cohort, we further validated a significant dose-response effect of PRSCSx on incident colorectal cancer, in which the risk was 2.11- and 3.88-fold higher in individuals with intermediate and high PRSCSx than in the low score subgroup (P trend = 8.15 × 10−53). Notably, the detrimental effect of being at a high genetic risk could be largely attenuated by adherence to a favorable lifestyle, with a 0.53% reduction in 5-year absolute risk. Conclusions In summary, we systemically constructed an EAS-EUR PRS to effectively stratify colorectal cancer risk, which highlighted its clinical implication among diverse ancestries. Importantly, these findings also supported that a healthy lifestyle could reduce the genetic impact on incident colorectal cancer.

Background: Environmental pollutant measurement is essential for accurate health risk assessment. However, the detection of humans’ internal exposure to pollutants is cost-intensive and consumes time and energy. Polygenic risk scores (PRSs) have been widely applied in genetic studies of complex trait diseases. It is important to construct a genetically relevant environmental surrogate for pollutant exposure and to explore its utility for disease prediction and risk assessment. Objectives: This study enrolled 714 individuals with complete genomic data and exposomic data on 22 plasma-persistent organic pollutants (POPs). Methods: We first conducted 22 POP genome-wide association studies (GWAS) and constructed the corresponding environmental pollutant-based PRS (EpPRS) by clumping and P value thresholding (C + T), lassosum, and PRS-CS methods. The best-fit EpPRS was chosen by its regression R2. An adverse outcome pathway (AOP) framework was developed to assess the effects of contaminants on candidate diseases. Furthermore, Mendelian randomization (MR) analysis was performed to explore the causal association between POPs and cancer risk. Results: The C + T method produced the best-performing EpPRSs for 7 PCBs and 4 PBDEs. EpPRSs replicated the correlations of environmental exposure measurements based on consistent patterns. The diagnostic performance of type 2 diabetes mellitus (T2DM) PRS was improved by the combined model of T2DM-EpPRS of PCB126/BDE153. Finally, the AKT1-mediated AOP framework illustrated that PCB126 and BDE153 may increase the risk of T2DM by decreasing AKT1 phosphorylation through the cGMP-PKG pathway and promoting abnormal glucose homeostasis. MR analysis showed that digestive system tumors, such as colorectal cancer and biliary tract cancer, are more sensitive to POP exposure. Conclusions: EpPRSs can serve as a proxy for assessing pollutant internal exposure. The application of the EpPRS to disease risk assessment can reveal the toxic pathway and mode of action linking exposure and disease in detail, providing a basis for the development of environmental pollutant control strategies.

Understanding the racial specificities of diseases—such as adult diffuse glioma, the most common primary malignant tumor of the central nervous system—is a critical step toward precision medicine. Here, we comprehensively review studies of gliomas in East Asian populations and other ancestry groups to clarify the racial differences in terms of epidemiology and genomic characteristics. Overall, we observed a lower glioma incidence in East Asians than in Whites; notably, patients with glioblastoma had significantly younger ages of onset and longer overall survival than the Whites. Multiple genome-wide association studies of various cohorts have revealed single nucleotide polymorphisms associated with overall and subtype-specific glioma susceptibility. Notably, only 3 risk loci—5p15.33, 11q23.3, and 20q13.33—were shared between patients with East Asian and White ancestry, whereas other loci predominated only in particular populations. For instance, risk loci 12p11.23, 15q15-21.1, and 19p13.12 were reported in East Asians, whereas risk loci 8q24.21, 1p31.3, and 1q32.1 were reported in studies in White patients. Although the somatic mutational profiles of gliomas between East Asians and non-East Asians were broadly consistent, a lower incidence of EGFR amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma were observed in East Asian cohorts. By summarizing large-scale disease surveillance, germline, and somatic genomic studies, this review reveals the unique characteristics of adult diffuse glioma among East Asians, to guide clinical management and policy design focused on patients with East Asian ancestry.

Objective Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. Methods We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during “clock drawing” and “repetitive writing” tasks. Extracted fingertip interaction digital biomarkers were used to assess participants’ SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. Results We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Conclusion Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human–computer interaction and intelligent early warning method for home and community screening of MCI due to AD.

BackgroundCancers arising within the gastrointestinal tract are complex disorders involving genetic events that cause the conversion of normal tissue to premalignant lesions and malignancy. Shared genetic features are reported in epithelial-based gastrointestinal cancers which indicate common susceptibility among this group of malignancies. In addition, the contribution of rare variants may constitute parts of genetic susceptibility.MethodsA cross-cancer analysis of 38,171 shared rare genetic variants from genome-wide association assays was conducted, which included data from 3,194 cases and 1,455 controls across three cancer sites (esophageal, gastric and colorectal). The SNP-level association was performed by multivariate logistic regression analyses for single cancer, followed by association analysis for SubSETs (ASSET) to adjust the bias of overlapping controls. Gene-level analyses were conducted by SKAT-O, with multiple comparison adjustments by false discovery rate (FDR). Based on the significant genes indicated by SKATO analysis, pathways analysis was conducted using Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases.ResultsMeta-analysis in three gastrointestinal (GI) cancers identified 13 novel susceptibility loci that reached genome-wide significance (PASSET< 5×10-8). SKAT-O analysis revealed EXOC6, LRP5L and MIR1263/LINC01324 to be significant genes shared by GI cancers (Padj

Abstract Background The association between vitamin E and cancer risk has been widely investigated by observational studies, but the findings remain inconclusive. Here, we aimed to evaluate the causal effect of circulating vitamin E on the risk of ten common cancers, including bladder, breast, colorectal, esophagus, lung, oral and pharynx, ovarian, pancreatic, prostate, and kidney cancer. Methods A Mendelian randomization (MR) analytic framework was applied to data from a cancer-specific genome-wide association study (GWAS) comprising a total of 297,699 cancer cases and 304,736 controls of European ancestry. Three genetic instrumental variables associated with circulating vitamin E were selected. Summary statistic-based methods of inverse variance weighting (IVW) and likelihood-based approach, as well as the individual genotyping-based method of genetic risk score (GRS) were used. Multivariable IVW analysis was further performed to control for potential confounding effects. Furthermore, the UK Biobank cohort was used as external validation, supporting 355,543 European participants (incident cases ranged from 437 for ovarian cancer to 4882 for prostate cancer) for GRS-based estimation of circulating vitamin E, accompanied by a one-sample MR analysis of dietary vitamin E intake underlying the time-to-event analytic framework. Results Specific to cancer GWAS, we found that circulating vitamin E was significantly associated with increased bladder cancer risk (odds ratios [OR]IVW = 6.23, P IVW = 3.05×10-3) but decreased breast cancer risk (ORIVW = 0.68, P IVW = 8.19×10-3); however, the significance of breast cancer was dampened (P multivariable IVW > 0.05) in the subsequent multivariable MR analysis. In the validation stage of the UK Biobank cohort, we did not replicate convincing causal effects of genetically predicted circulating vitamin E concentrations and dietary vitamin E intake on the risk of ten cancers. Conclusions This large-scale population study upon data from cancer-specific GWAS and a longitudinal biobank cohort indicates plausible non-causal associations between circulating vitamin E and ten common cancers in the European populations. Further studies regarding ancestral diversity are warranted to validate such causal associations.

Summary: Background: Polygenic risk score (PRS) has been demonstrated to be effective in identifying individuals at high risk of developing cancer, but its prognostic value remains unclear. Methods: We constructed site-specific PRSs by aggregating the risk effect of independent variants derived from previous genome-wide association studies (GWASs) across 17 cancer types. The Cox proportional hazards model was used to evaluate the association of each PRS with cancer survival, leveraging data from two prospective European cohorts, namely the UK Biobank involving 19,628 incident cases and The Cancer Genome Atlas involving 7079 prevalent cases. The combined PRS (CPRS), determined by merging site-specific PRSs, was further used to assess the prognostic effect of PRS on overall cancer in a sex-specific manner. Findings: We discovered that the cancer risk-related PRS was associated with neither overall survival (OS) nor cancer-specific survival (CSS) of each site-specific cancer with an underlying false discovery rate (FDR) P > 0.05, as evidenced by consistent findings from the two cohorts. Furthermore, the fixed-effect meta-analysis of the two cohorts provided no evidence to support for an association between CPRS and overall cancer survival in both males [OS: hazard ratio (HR)meta = 1.00, Pmeta = 0.760; CSS: HRmeta = 1.01, Pmeta = 0.447] and females (OS: HRmeta = 0.97, Pmeta = 0.067; CSS: HRmeta = 0.96, Pmeta = 0.054). Similar results were observed across multiple sensitivity analyses. Interpretation: Our findings indicate that the risk-specific PRS might not be a clinically useful tool in cancer prognosis prediction and further studies focusing on the development of polygenic prognostic score are warranted. Funding: This project was funded by the National Natural Science Foundation of China (82173601 and 82073631), and Priority Academic Program Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).

Abstract Small non-coding RNAs (ncRNAs) are vital regulators of biological activities, and aberrant levels of small ncRNAs are commonly found in precancerous lesions and cancer. PIWI-interacting RNAs (piRNAs) are a novel type of small ncRNA initially discovered in germ cells that have a specific length (24–31 nucleotides), bind to PIWI proteins, and show 2′-O-methyl modification at the 3′-end. Numerous studies have revealed that piRNAs can play important roles in tumorigenesis via multiple biological regulatory mechanisms, including silencing transcriptional and posttranscriptional gene processes and accelerating multiprotein interactions. piRNAs are emerging players in the malignant transformation of normal cells and participate in the regulation of cancer hallmarks. Most of the specific cancer hallmarks regulated by piRNAs are involved in sustaining proliferative signaling, resistance to cell death or apoptosis, and activation of invasion and metastasis. Additionally, piRNAs have been used as biomarkers for cancer diagnosis and prognosis and have great potential for clinical utility. However, research on the underlying mechanisms of piRNAs in cancer is limited. Here, we systematically reviewed recent advances in the biogenesis and biological functions of piRNAs and relevant bioinformatics databases with the aim of providing insights into cancer diagnosis and clinical applications. We also focused on some cancer hallmarks rarely reported to be related to piRNAs, which can promote in-depth research of piRNAs in molecular biology and facilitate their clinical translation into cancer treatment.

Abstract Cancer is a leading cause of death worldwide, particularly because of its high mortality rate in patients who are diagnosed at late stages. Conventional biomarkers originating from blood are widely used for cancer diagnosis, but their low sensitivity and specificity limit their widespread application in cancer screening among the general population. Currently, emerging studies are exploiting novel, highly-accurate biomarkers in human body fluids that are obtainable through minimally invasive techniques, which is defined as liquid biopsy. Circular RNAs (circRNAs) are a newly discovered class of noncoding RNAs generated mainly by pre-mRNA splicing. Following the rapid development of high-throughput transcriptome analysis techniques, numerous circRNAs have been recognized to exist stably and at high levels in body fluids, including plasma, serum, exosomes, and urine. CircRNA expression patterns exhibit distinctly differences between patients with cancer and healthy controls, suggesting that circRNAs in body fluids potentially represent novel biomarkers for monitoring cancer development and progression. In this study, we summarized the expression of circRNAs in body fluids in a pan-cancer dataset and characterized their clinical applications in liquid biopsy for cancer diagnosis and prognosis. In addition, a user-friendly web interface was developed to visualize each circRNA in fluids ( https://mulongdu.shinyapps.io/circrnas_in_fluids/ ).

Background: To understand a causal role of modifiable lifestyle factors in angiotensin-converting enzyme 2 (ACE2) expression (a putative severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] receptor) across 44 human tissues/organs, and in coronavirus disease 2019 (COVID-19) susceptibility and severity, we conducted a phenome-wide two-sample Mendelian randomization (MR) study. Methods: More than 500 genetic variants were used as instrumental variables to predict smoking and alcohol consumption. Inverse-variance weighted approach was adopted as the primary method to estimate a causal association, while MR-Egger regression, weighted median, and MR pleiotropy residual sum and outlier (MR-PRESSO) were performed to identify potential horizontal pleiotropy. Results: We found that genetically predicted smoking intensity significantly increased ACE2 expression in thyroid (β=1.468, p=1.8×10−8), and increased ACE2 expression in adipose, brain, colon, and liver with nominal significance. Additionally, genetically predicted smoking initiation significantly increased the risk of COVID-19 onset (odds ratio=1.14, p=8.7×10−5). No statistically significant result was observed for alcohol consumption. Conclusions: Our work demonstrates an important role of smoking, measured by both status and intensity, in the susceptibility to COVID-19. Funding: XJ is supported by research grants from the Swedish Research Council (VR-2018–02247) and Swedish Research Council for Health, Working Life and Welfare (FORTE-2020–00884).

Background: Fine particulate matter (PM2.5) is suspected to increase the risk of colorectal cancer, but the mechanism remains unknown. We aimed to investigate the association between PM2.5 exposure, genetic variants and colorectal cancer risk in the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening trial. Methods: We included a prospective cohort of 139,534 cancer-free individuals from 10 United States research centers with over ten years of follow-up. We used a Cox regression model to assess the association between PM2.5 exposure and colorectal cancer incidence by calculating the hazard ratio (HR) and 95% confidence interval (CI) with adjustment for potential confounders. The polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between PM2.5 exposure and genetic variants in regard to colorectal cancer risk. Results: After a median of 10.43 years of follow-up, 1,666 participants had been diagnosed with colorectal cancer. PM2.5 exposure was significantly associated with an increased risk of colorectal cancer (HR = 1.27; 95% CI = 1.17–1.37 per 5 μg/m3 increase). Five independent susceptibility loci reached statistical significance at P

The whale optimization algorithm (WOA) is a high-performance metaheuristic algorithm that can effectively solve many practical problems and broad application prospects. However, the original algorithm has a significant improvement in space in solving speed and precision. It is easy to fall into local optimization when facing complex or high-dimensional problems. To solve these shortcomings, an elite strategy and spiral motion from moth flame optimization are utilized to enhance the original algorithm’s efficiency, called MEWOA. Using these two methods to build a more superior population, MEWOA further balances the exploration and exploitation phases and makes it easier for the algorithm to get rid of the local optimum. To show the proposed method’s performance, MEWOA is contrasted to other superior algorithms on a series of comprehensive benchmark functions and applied to practical engineering problems. The experimental data reveal that the MEWOA is better than the contrast algorithms in convergence speed and solution quality. Hence, it can be concluded that MEWOA has great potential in global optimization.

Summary: The genetic effect explains the causality from genetic mutations to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a deep association kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects where existing approaches fail. When applied to four real-world GWAS datasets including cancers and schizophrenia, our DAK discovered potential casual pathways, including the association between dilated cardiomyopathy pathway and schizophrenia. The Bigger Picture: Genetic mutations cause complex diseases in many different ways. Comprehensively identifying the genetic causality can lead to valuable insights into the development and treatment of diseases. However, existing genome-wide association study (GWAS) approaches are always built under linear assumption and simple disease models, restricting their generalization in discovering the complicated causality. DAK (deep association kernel learning) is a GWAS method that is constructed in a deep-learning framework and can simultaneously identify multiple types of genetic causalities without any modifications to the model. For biological contributions, the proposed approach enables the understanding of non-linear, complex genetic causalities and improves functional studies of the disease; for computational contributions, our method unifies kernel learning and association analysis in a joint explainable deep-learning framework.

Backgrounds: Genome-wide association studies (GWASs) have identified several gastric cancer (GC) susceptibility loci in Asians, but their effects on disease outcome are still unknown. This study aimed to investigate whether these GWAS-identified genetic variants could serve as robust prognostic biomarkers for GC. Methods: A multistage clinical cohort, including a total of 2432 GC patients in the Chinese population, was used to identify the association between GWAS-identified risk variants and overall survival of GC. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed by Cox regression analysis, and the log-rank P was calculated by the log-rank test with the Kaplan-Meier method. Results: We found that rs2274223 A>G in PLCE1 was associated with increased GC survival in both training set (P = .011), which was independently replicated in validation set 1 (P = .045), but not in validation set 2. The area under the curve (AUC) from receiver-operator characteristic (ROC) curve showed this clinical relevance with onset age-dependence, especially in the subgroup of early-onset cases. Moreover, a significant improvement in overall survival prediction was identified when the rs2274223 genetic effect was included in the estimation; this result was also supported by the prognostic nomogram. In addition, patients with lower expression of PLCE1 showed benefits via longer survival, potentially due to the functional effect of rs2274223. Interpretation: This preliminary study suggests that a GWAS-identified genetic variant in PLCE1 may serve as a potential biomarker for GC survival. Additional replication with larger samples size is warranted to further investigation. Keywords: GWAS, Genetic variants, Gastric cancer, Survival

Abstract The association between air pollution and childhood respiratory disease is inconsistent. In the present study, we investigated a short-term effect of ambient air pollutants and daily childhood lower respiratory diseases (CLRD). Daily air pollutants, weather data, and CLRD data were collected from January 2014 to April 2015 (452 days) in Nanjing, China. Time-series regression and generalized additive models were used to assess the effects of air pollutants (PM10, PM2.5, NO2, SO2, O3, and CO) on CLRD. We observed that an interquartile range (IQR) increase in concentrations of PM10, NO2, and SO2 significantly increased the daily CLRD with 6 days cumulative effects (difference of estimates: 2.8%, 95% CI: 0.6–5.0%; 4.1%, 1.2–7.0%; 5.6%, 2.6–8.6%, respectively). However, no significant association was found in IQR concentrations of PM2.5, O3, and CO. Specifically, elevated PM10, PM2.5, NO2, and SO2 significantly increased the numbers of CLRD in cool season (3.6%, 1.5–5.7%; 2.4%, 0.3–4.5%; 4.9%, 2.9–7.0%; 6.3%, 3.7–9.0%, respectively). Additionally, the effect estimates of PM10, NO2, and SO2 in female and age >27 months were more pronounced than in male and age ≤27 months. This study suggested that short-term exposure to ambient PM10, NO2, and SO2 were associated with the increased CLRD numbers.

Core 1 synthase glycoprotein-N-acetylgalactosamine 3-β-galactosyltransferase 1 (C1GALT1) is known to play a critical role in the development of gastric cancer, but few studies have elucidated associations between genetic variants in C1GALT1 and gastric cancer risk. By using the genome-wide association study data from the database of Genotype and Phenotype (dbGAP), we evaluated such associations with a multivariable logistic regression model and identified that the rs35999583G>C in C1GALT1 was associated with gastric cancer risk (odds ratio, 0.83; 95% confidence interval [CI], 0.75–0.92; P = 3.95 × 10−4). C1GALT1 mRNA expression levels were significantly higher in gastric tumor tissues than in normal tissues, and gastric cancer patients with higher C1GALT1 mRNA levels had worse overall survival rates (hazard ratio, 1.33; 95% CI, 1.05–1.68; Plog-rank = 1.90 × 10−2). Furthermore, we found that C1GALT1 copy number differed in various immune cells and that C1GALT1 mRNA expression levels were positively correlated with the infiltrating levels of CD4+ T cells and macrophages. These results suggest that genetic variants of C1GALT1 may play an important role in gastric cancer risk and provide a new insight for C1GALT1 as a promising predictor of gastric cancer susceptibility and immune status. [ABSTRACT FROM AUTHOR]

The current study aimed to investigate associations of circRNAs and related genetic variants with the risk of prostate cancer (PCa) as well as to elucidate biological mechanisms underlying the associations. We first compared expression levels of circRNAs between 25 paired PCa and adjacent normal tissues to identify riskassociated circRNAs by using the MiOncoCirc database. We then used logistic regression models to evaluate associations between genetic variants in candidate circRNAs and PCa risk among 4 662 prostate cancer patients and 3 114 healthy controls, and identified circHIBADH rs11973492 T>C as a significant risk-associated variant (odds ratio = 1.20, 95% confidence interval: 1.08–1.34, P = 7.06 × 10−4) in a dominant genetic model, which altered the secondary structure of the corresponding RNA chain. In the in silico analysis, we found that circHIBADH sponged and silenced 21 RNA-binding proteins (RBPs) enriched in the RNA splicing pathway, among which HNRNPA1 was identified and validated as a hub RBP using an external RNA-sequencing data as well as the in-house (four tissue samples) and publicly available single-cell transcriptomes. Additionally, we demonstrated that HNRNPA1 influenced hallmarks including MYC target, DNA repair, and E2F target signaling pathways, thereby promoting carcinogenesis. In conclusion, genetic variants in circHIBADH may act as sponges and inhibitors of RNA splicing-associated RBPs including HNRNPA1, playing an oncogenic role in PCa. [ABSTRACT FROM AUTHOR]