High burden of human papillomavirus infection among men in Guangzhou, South China: Implications for HPV vaccination strategies.

The epidemiological and clinical aspects of Human Papillomavirus (HPV) infection in women have been extensively studied. However, there is a lack of information regarding HPV characteristics in males. In this study, we conducted a retrospective and observational study of 3737 consecutive male individuals attending outpatient clinics of Guangdong Women and Children Hospital from 2012 to 2023 in Guangzhou, South China, to determine the age- and genotype-specific prevalence of HPV in men. The results showed the overall prevalence of HPV among men was 42.15% (1575/3737), with variations ranging from 29.55% to 81.31% across distinct diagnostic populations. Low-risk HPV6 (15.47%), HPV11 (8.94%), and high-risk HPV52 (5.51%) were the most common types. The annual HPV prevalence decreased significantly (Z = -3.882, p < .001), ranging from 31.44% to 52.90%. 28.77% (1075/3737) of men manifested infection with a singular HPV type, predominantly identified as a low-risk type. The age-specific distribution of HPV infections revealed distinctive peaks in the < 25 y age group (47.60%, 208/437) and the 40-44 y age group (44.51%, 154/346). Notably, the positive rate of Chlamydia trachomatis was significantly higher among HPV-positive individuals in comparison to HPV-negatives (16.14% vs. 11.25%, p < .05). Our findings reveal a substantial prevalence of HPV infection among outpatient men in Guangzhou, South China. It is recommended to consider the inclusion of HPV vaccination for adolescent males in national immunization schedules, once an adequate supply of vaccines is accessible.

LLPS of FXR proteins drives replication organelle clustering for ß-coronaviral proliferation.

ß-Coronaviruses remodel host endomembranes to form double-membrane vesicles (DMVs) as replication organelles (ROs) that provide a shielded microenvironment for viral RNA synthesis in infected cells. DMVs are clustered, but the molecular underpinnings and pathophysiological functions remain unknown. Here, we reveal that host fragile X-related (FXR) family proteins (FXR1/FXR2/FMR1) are required for DMV clustering induced by expression of viral non-structural proteins (Nsps) Nsp3 and Nsp4. Depleting FXRs results in DMV dispersion in the cytoplasm. FXR1/2 and FMR1 are recruited to DMV sites via specific interaction with Nsp3. FXRs form condensates driven by liquid-liquid phase separation, which is required for DMV clustering. FXR1 liquid droplets concentrate Nsp3 and Nsp3-decorated liposomes in vitro. FXR droplets facilitate recruitment of translation machinery for efficient translation surrounding DMVs. In cells depleted of FXRs, SARS-CoV-2 replication is significantly attenuated. Thus, SARS-CoV-2 exploits host FXR proteins to cluster viral DMVs via phase separation for efficient viral replication.

TIMM17A overexpression in lung adenocarcinoma and its association with prognosis.

Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan-Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

Emergence and circulation of enterovirus B species in infants in southern China: A multicenter retrospective analysis.

BACKGROUND: Enteroviruses (EV) are common and can cause severe diseases, particularly in young children. However, the information of EV infection in infants in China is limited due to the vast population size and extensive geographical area of the country. Here, we conducted a retrospective multicenter analysis of available EV data to assess the current epidemiological situation in the infant population in southern China. METHODS: The study enrolled infants with suspected EV infection from 34 hospitals across 12 cities in southern China between 2019 to 2022, and the confirmation of EV was done using RT-PCR and VP1 gene sequencing. RESULTS: Out of 1221 infants enrolled, 330 (27.03%) were confirmed as EV-infected. Of these, 260 (78.79%) were newborns aged 0-28 days. The EV belonged to three species: EV-B (80.61%), EV-A (11.82%), and human rhinovirus (7.58%). Newborns were more susceptible to EV-B than older infants (p < 0.001). Within EV-B, we identified 15 types, with coxsackievirus (CV) B3 (20.91%), echovirus (E) 11 (19.70%), and E18 (16.97%) being the most common. The predominant EV types changed across different years. EV infection in infants followed a seasonal pattern, with a higher incidence from May to August. Furthermore, perinatal mother-to-child EV transmission in 12 mother-newborn pairs were observed. CONCLUSION: Our study is the first to demonstrate the emergence and widespread circulation of EV-B species, mainly CVB3, E11, and E18, in southern China, primarily affecting young infants. This research provides valuable insights for future epidemic assessment, prediction, as well as the elimination of mother-to-child transmission.

Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.

In this paper, we study high-dimensional multivariate logistic regression models in which a common set of covariates is used to predict multiple binary outcomes simultaneously. Our work is primarily motivated from many biomedical studies with correlated multiple responses such as the cancer cell-line encyclopedia project. We assume that the underlying regression coefficient matrix is simultaneously low-rank and row-wise sparse. We propose an intuitively appealing selection and estimation framework based on marginal model likelihood, and we develop an efficient computational algorithm for inference. We establish a novel high-dimensional theory for this nonlinear multivariate regression. Our theory is general, allowing for potential correlations between the binary responses. We propose a new type of nuclear norm penalty using the smooth clipped absolute deviation, filling the gap in the related non-convex penalization literature. We theoretically demonstrate that the proposed approach improves estimation accuracy by considering multiple responses jointly through the proposed estimator when the underlying coefficient matrix is low-rank and row-wise sparse. In particular, we establish the non-asymptotic error bounds, and both rank and row support consistency of the proposed method. Moreover, we develop a consistent rule to simultaneously select the rank and row dimension of the coefficient matrix. Furthermore, we extend the proposed methods and theory to a joint Ising model, which accounts for the dependence relationships. In our analysis of both simulated data and the cancer cell line encyclopedia data, the proposed methods outperform the existing methods in better predicting responses.

Systematic characterization of protein structural features of alternative splicing isoforms using AlphaFold 2.

Alternative splicing is an important cellular process in eukaryotes, altering pre-mRNA to yield multiple protein isoforms from a single gene. However, our understanding of the impact of alternative splicing events on protein structures is currently constrained by a lack of sufficient protein structural data. To address this limitation, we employed AlphaFold 2, a cutting-edge protein structure prediction tool, to conduct a comprehensive analysis of alternative splicing for approximately 3,000 human genes, providing valuable insights into its impact on the protein structural. Our investigation employed state of the art high-performance computing infrastructure to systematically characterize structural features in alternatively spliced regions and identified changes in protein structure following alternative splicing events. Notably, we found that alternative splicing tends to alter the structure of residues primarily located in coils and beta-sheets. Our research highlighted a significant enrichment of loops and highly exposed residues within human alternatively spliced regions. Specifically, our examination of the Septin-9 protein revealed potential associations between loops and alternative splicing, providing insights into its evolutionary role. Furthermore, our analysis uncovered two missense mutations in the Tau protein that could influence alternative splicing, potentially contributing to the pathogenesis of Alzheimer's disease. In summary, our work, through a thorough statistical analysis of extensive protein structural data, sheds new light on the intricate relationship between alternative splicing, evolution, and human disease.

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And in fields such as healthcare, there are higher requirements for the accuracy of the generated imputation values. To train a high-performance classifier and pursue high accuracy, we attempted to resolve any potential negative impact by using a novel algorithmic approach based on the combination of multivariate imputation by chained equations and the ensemble learning method (MICEEN), which can solve the two problems simultaneously. We used multivariate imputation by chained equations to generate more accurate imputation values for the training set passed to ensemble learning to build a predictor. On the other hand, missing values were introduced into minority classes and used them to generate new samples belonging to the minority classes in order to balance the distribution of classes. On real-world datasets, we perform extensive experiments to assess our method and compare it to other state-of-the-art approaches. The advantages of the proposed method are demonstrated by experimental results for the benchmark datasets and self-collected datasets of physical fitness assessment of tumor patients with varying missing rates.

Statistical methods for assessing the effects of de novo variants on birth defects.

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

The efficacy and safety of modified ultraearly oral hydration for alleviating thirst in patients after thoracoscopic surgery: a prospective randomized controlled trial.

OBJECTIVE: Postoperative fasting following thoracoscopic surgery can cause intense thirst and oral discomfort. However, there is currently no research on ultraearly oral hydration (UEOH) in middle-aged or elderly patients after thoracoscopic surgery. The aim of this study was to investigate the effectiveness and safety of UEOH for improving oral discomfort after thoracoscopic surgery. METHODS: This single-center prospective double-blind randomized controlled trial was conducted from April 2022 to November 2023. A total of 64 middle-aged and elderly patients who underwent the first thoracoscopic surgery on the day were enrolled at our institution. Postoperatively, in the Postanesthesia Care Unit (PACU), patients were randomly assigned at a 1:1 ratio to either the UEOH group or the standard care (SC) group. The primary outcome was the patient's thirst score at 6 h after surgery. Secondary outcomes included the incidence of postoperative oral discomfort; pain scores; the occurrence of adverse reactions such as nausea, vomiting, regurgitation and aspiration; anxiety scores on the first postoperative day; the time to first flatus; and recovery satisfaction scores. RESULTS: The demographic and surgical characteristics were similar between the two groups. Patients in the UEOH group had lower thirst scores 6 h after surgery than did those in the SC group(16.1 ± 6.70 vs. 78.4 ± 8.42, P < 0.01). The incidence of postoperative oral discomfort (P < 0.01), anxiety scores on the first postoperative day (P<0.05), and time to first flatus (P<0.05) were better in the UEOH group. Additionally, the incidences of adverse reactions, such as postoperative nausea, vomiting, regurgitation and aspiration, were similar between the two groups (P>0.05). CONCLUSION: For middle-aged and elderly patients undergoing thoracoscopic surgery, the use of a modified UEOH protocol postoperatively can improve thirst and promote gastrointestinal recovery without increasing complications. TRIAL REGISTRATION: This single-center, prospective, RCT has completed the registration of the Chinese Clinical Trial Center at 07/12/2023 with the registration number ChiCTR2300078425.

Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder.

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

A mediation analysis framework based on variance component to remove genetic confounding effect.

Identification of pleiotropy at the single nucleotide polymorphism (SNP) level provides valuable insights into shared genetic signals among phenotypes. One approach to study these signals is through mediation analysis, which dissects the total effect of a SNP on the outcome into a direct effect and an indirect effect through a mediator. However, estimated effects from mediation analysis can be confounded by the genetic correlation between phenotypes, leading to inaccurate results. To address this confounding effect in the context of genetic mediation analysis, we propose a restricted-maximum-likelihood (REML)-based mediation analysis framework called REML-mediation, which can be applied to either individual-level or summary statistics data. Simulations demonstrated that REML-mediation provides unbiased estimates of the true cross-trait causal effect, assuming certain assumptions, albeit with a slightly inflated standard error compared to traditional linear regression. To validate the effectiveness of REML-mediation, we applied it to UK Biobank data and analyzed several mediator-outcome trait pairs along with their corresponding sets of pleiotropic SNPs. REML-mediation successfully identified and corrected for genetic confounding effects in these trait pairs, with correction magnitudes ranging from 7% to 39%. These findings highlight the presence of genetic confounding effects in cross-trait epidemiological studies and underscore the importance of accounting for them in data analysis.

Immunosuppressant medication behaviours in solid organ transplant recipients: a cross-sectional study from south-central China during COVID-19 reopening period.

OBJECTIVE: Medication non-adherence to immunosuppressants threatens allograft survival and function maintenance among solid organ transplant (SOT) recipients. This study aimed to investigate the prevalence of immunosuppressant medication non-adherence and associated factors during the COVID-19 reopening period among Chinese SOT recipients. DESIGN: Cross-sectional study. SETTING: South-central China. POPULATION: Adult patients who received SOT with functioning graft. METHODS: Sociodemographic questionnaire and scales to measure physical activity, depression and medication non-adherence were used to collect data. Logistic regression analysis was conducted to identify factors associated with medication non-adherence. Mediation and moderated mediation analyses were performed to examine the potential mechanisms influencing medication behaviour during the pandemic reopening period using SPSS PROCESS macro 4.3 software. RESULTS: A total of 1121 participants were recruited and the prevalence of medication non-adherence was 36.3% in this study. Recipients who were men, had a higher monthly income, lived alone, had received transplantation for a minimum of 3 years, had received COVID-19 vaccination and experienced depressive symptoms exhibited an increased risk of non-adherence. Contrarily, those who engaged in high-intensity physical activity exhibited a decreased risk. Physical activity was negatively associated with medication non-adherence (r=-0.124, p<0.001) with depression fully mediating this relationship (B=-0.014, 95% CI: -0.032 to -0.003). COVID-19 vaccination significantly moderated the relationship between physical activity and depression (B=-0.303, 95% CI: -0.515 to -0.090). CONCLUSION: This study investigated the prevalence of medication non-adherence among SOT recipients during the COVID-19 reopening period in China, its associated factors and a potential mechanism. Depression fully mediated the association between physical activity and medication non-adherence, and COVID-19 vaccination moderated the relationship between physical activity and depression. These findings provide some insights for managing medication behaviour when confronting public health emergencies. However, relationships displayed in the moderated mediation model should be tracked after returning to normal life and other potential relationships should be explored to deeply understand medication non-adherent behaviour.

A genotyping array for the globally invasive vector mosquito, Aedes albopictus.

BACKGROUND: Although whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health. METHODS: We designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions. RESULTS: Probes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth < 20, while there was near complete agreement with WGS read depths > 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture. CONCLUSIONS: The Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS.

Evaluating the Utilities of Foundation Models in Single-cell Data Analysis.

Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.

Transition-metal ions intercalation chemistry enabled the manganese oxides-based cathode with enhanced capacity and cycle life for high-performance aqueous zinc-ion batteries.

Aqueous zinc-ion batteries (AZIBs) employing mild aqueous electrolytes are recognized for their high safety, cost-effectiveness, and scalability, rendering them promising candidates for large-scale energy storage infrastructure. However, the practical viability of AZIBs is notably impeded by their limited capacity and cycling stability, primarily attributed to sluggish cathode kinetics during electrochemical charge-discharge processes. This study proposes a transition-metal ion intercalation chemistry approach to augment the Zn2+ (de)intercalation dynamics using copper ions as prototypes. Electrochemical assessments reveal that the incorporation of Cu2+ into the host MnO2 lattice (denoted as MnO2-Cu) not only enhances the capacity performance owing to the additional redox activity of Cu2+ but also facilitates the kinetics of Zn2+ ion transport during charge-discharge cycles. Remarkably, the resulting AZIB employing the MnO2-Cu cathode exhibits a superior capacity of 429.4 mA h g-1 (at 0.1 A g-1) and maintains 50% capacity retention after 50 cycles, surpassing both pristine MnO2 (146.8 mA h g-1) and non-transition-metal ion-intercalated MnO2 (MnO2-Na, 198.5 mA h g-1). Through comprehensive electrochemical kinetics investigations, we elucidate that intercalated Cu2+ ions serve as mediators for interlayer stabilization and redox centers within the MnO2 host, enhancing capacity and cycling performance. The successful outcomes of this study underscore the potential of transition-metal ion intercalation strategies in advancing the development of high-performance cathodes for AZIBs.

Stabilizing Lattice Oxygen through Mn Doping in NiCo2O4-δ Spinel Electrocatalysts for Efficient and Durable Acid Oxygen Evolution.

Design the electrocatalysts without noble metal is still a challenge for oxygen evolution reaction (OER) in acid media. Herein, we reported the manganese (Mn) doping method to decrease the concentration of oxygen vacancy (VO) and form the Mn-O structure adjacent octahedral sites in spinel NiCo2O4-δ (NiMn1.5Co3O4-δ), which highly enhanced the activity and stability of spinel NiCo2O4-δ with a low overpotential (η) of 280 mV at j=10 mA cm-2 and long-term stability of 80 h in acid media. The isotopic labelling experiment based on differential electrochemical mass spectrometry (DEMS) clearly demonstrated the lattice oxygen in NiMn1.5Co3O4-δ is more stable due to strong Mn-O bond and shows synergetic adsorbate evolution mechanism (SAEM) for acid OER. Density functional theory (DFT) calculations reveal highly increased oxygen vacancy formation energy (EVO) of NiCo2O4-δ after Mn doping. More importantly, the highly hydrogen bonding between Mn-O and *OOH adsorbed on adjacent Co octahedral sites promote the formation of *OO from *OOH due to the greatly enhanced charge density of O in Mn substituted sites.

Gender disparities in summer outdoor heat risk across China: Findings from a national county-level assessment during 1991-2020.

Increasing anthropogenic global warming has emerged as a significant challenge to human health in China, as extreme heat hazards increasingly threaten outdoor-exposed populations. Differences in thermal comfort, outdoor activity duration, and social vulnerability between females and males may exacerbate gender inequalities in heat-related health risks, which have been overlooked by previous studies. Here, we combine three heat hazards and outdoor activity duration to identify the spatiotemporal variation in gender-specific heat risk in China during 1991-2020. We found that females' heat risk tends to be higher than that of males. Gender disparities in heat risk decrease in southern regions, while those in northern regions remain severe. Males are prone to overheating in highly urbanized areas, while females in low urbanized areas. Males' overheating risk is mainly attributed to population clustering associated with prolonged outdoor activity time and skewed social resource allocation. In contrast, females' overheating risk is primarily affected by social inequalities. Our findings suggest that China needs to further diminish gender disparities and accelerate climate adaptation planning.

Evaluating and improving health equity and fairness of polygenic scores.

Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.

Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank.

BACKGROUND: Models with polygenic risk scores and clinical factors to predict risk of different cancers have been developed, but these models have been limited by the polygenic risk score-derivation methods and the incomplete selection of clinical variables. METHODS: We used UK Biobank to train the best polygenic risk scores for 8 cancers (bladder, breast, colorectal, kidney, lung, ovarian, pancreatic, and prostate cancers) and select relevant clinical variables from 733 baseline traits through extreme gradient boosting (XGBoost). Combining polygenic risk scores and clinical variables, we developed Cox proportional hazards models for risk prediction in these cancers. RESULTS: Our models achieved high prediction accuracy for 8 cancers, with areas under the curve ranging from 0.618 (95% confidence interval = 0.581 to 0.655) for ovarian cancer to 0.831 (95% confidence interval = 0.817 to 0.845) for lung cancer. Additionally, our models could identify individuals at a high risk for developing cancer. For example, the risk of breast cancer for individuals in the top 5% score quantile was nearly 13 times greater than for individuals in the lowest 10%. Furthermore, we observed a higher proportion of individuals with high polygenic risk scores in the early-onset group but a higher proportion of individuals at high clinical risk in the late-onset group. CONCLUSION: Our models demonstrated the potential to predict cancer risk and identify high-risk individuals with great generalizability to different cancers. Our findings suggested that the polygenic risk score model is more predictive for the cancer risk of early-onset patients than for late-onset patients, while the clinical risk model is more predictive for late-onset patients. Meanwhile, combining polygenic risk scores and clinical risk factors has overall better predictive performance than using polygenic risk scores or clinical risk factors alone.

Analysis of gene mutation characteristics and its correlation with prognosis in patients with myelodysplastic syndromes.

Gene mutations are a pivotal component of the pathogenesis of MDS, and they hold profound prognostic significance for predicting treatment responses and survival outcomes. However, reports about mutation patterns in Chinese MDS patients are limited. In this study, we analyzed the genetic mutation of 23 genes in 231 patients with MDS using next-generation sequencing (NGS) technology, and explored the characteristics of gene mutations in MDS patients and their associations with clinical outcomes, survival, and transformation outcomes. Our results showed that 68.83% patients had at least one gene mutation, and the most common mutations were ASXL1 (21.65%), SF3B1 (17.32%), U2AF1 (16.02%), TET2 (14.72%) and TP53 (8.66%). We also showed that the genetic mutations of TP53, U2AF1 and DNMT3A are independent risk factors for death in patients with MDS, and the ETV6 gene mutation was an independent risk factor for the transformation of MDS patients to AML through the univariate and multivariate Cox regression analysis model. Additionally, the study developed a risk score based on gene mutation data that demonstrated robust predictive capability and stability for the overall survival of MDS patients. Our research provided a strong theoretical basis for the establishment of personalized treatment and prognostic risk assessment models for Chinese MDS patients.