Yashuang Zhao

Background: Depressive disorder is a leading cause of disability, with significant increases among adolescents and young adults. Childhood sexual abuse (CSA) and bullying victimisation are major risk factors, yet their specific contribution remains underexplored. Aims: To quantify the global burden of depressive disorder attributable to CSA and bullying victimisation among adolescents and young adults, examine temporal trends and project burden to 2050. Method: Using Global Burden of Disease 2021 data, years lived with disability (YLD) for depressive disorder related to CSA and bullying victimisation were estimated across 204 countries and 21 regions. Age-standardised YLD rates (ASYR) were calculated, and joinpoint regression was used to assess average annual percentage changes. Bayesian age-period-cohort (BAPC) modelling projected future trends. Results: In 2021, bullying victimisation accounted for a substantially larger proportion of depressive disorder burden (73.31%, ASYR: 87.83 out of 100 000) than CSA (11.81%, ASYR: 14.15 out of 100 000). Both risk factors increased from 1990 to 2021, with bullying victimisation rising faster, particularly in high sociodemographic index regions. Women experienced a substantially higher CSA-related burden (ASYR ratio 1.78:1), whereas men had a slightly higher bullying victimisation-related burden (ASYR ratio 1.02:1). Burden peaked among those aged 20–24 years for both risk factors. BAPC projections suggest that CSA-related depressive disorder will increase through to 2050, whereas bullying victimisation-related depressive disorder may decline, although uncertainty remains regarding future prevention efforts and reporting patterns. Conclusions: ASYR for depressive disorder due to both CSA and bullying victimisation increased from 1990 to 2021; CSA-related burden is projected to continue rising through to 2050, whereas bullying victimisation-related burden may decline. Gender differences, age-specific peaks and regional disparities highlight the need for gender-sensitive, age-appropriate and region-specific interventions to address depressive disorder related to CSA and bullying victimisation.

Hypertension is a significant health risk in older adults and remains inadequately controlled in low- and middle-income countries (LMICs). Limited information is now available on the effectiveness of multicomponent interventions in older patients with hypertension. A multicomponent intervention was conducted in Kaihua county, with participating patients as the intervention group. Patients from Changshan county, where no intervention was conducted, were selected as the control group using a two-stage propensity score matching. The primary outcome was the difference in blood pressure (BP) changes from baseline to end of follow-up. Secondary outcomes included medication adherence rates, BP control rates, and difference in the risk of cardiovascular and cerebrovascular diseases (CVDs). Additionally, the spillover effects of the intervention were delved into. From baseline to 24th month, compared to the control group, the net reductions were 1.190 mmHg in systolic BP (95%CI 0.774 to 1.605; P < 0.001) and 0.376 mmHg in diastolic BP (95%CI 0.143 to 0.503; P = 0.004). Besides, the intervention group showed a 2.76% increase in achieving BP control, along with improved regular medication adherence (P < 0.001) and a reduced risk of CVDs (HR = 0.759; 0.647 to 0.892; P = 0.001). Moreover, non-participating patients in the intervention areas also experienced lower systolic BP (2.094 mm Hg; 95% CI 1.421 to 2.766; P < 0.001) and reduced risk of CVDs compared to those in Changshan due to passive interventions (HR = 0.541; 0.407 to 0.718; P < 0.001). The multicomponent intervention effectively reduced BP in hypertensive patients, with significant spillover effects within the non-intervention population. This feasible, effective, and sustainable strategy is recommended for replication in LMICs. The online version contains supplementary material available at 10.1186/s12877-026-07452-4.

Background: The “gut–brain axis” provides a theoretical foundation for the connection between intestinal and neurological diseases, but whether this reflects a shared genetic etiology or causal relationships exist remains unclear. Methods: We used genome‐wide association study summary data from FinnGen and UK Biobank to investigate the genetic correlations and causal relationships between three intestinal diseases and six neurological diseases. Results: We observed positive global genetic correlations between irritable bowel syndrome and epilepsy ( r _g  = 0.429, p  = 1.53 × 10 ⁻² ), and stroke ( r _g  = 0.368, p  = 2.56×10 ⁻² ). Upon dividing the whole genome into 1703 independent regions, local genetic correlations were identified in a region between ulcerative colitis and multiple sclerosis (Chr6: 31571218–32682664). We also identified 12 novel pleiotropic SNPs shared between intestinal and neurological diseases, as well as a functional gene shared between ulcerative colitis and multiple sclerosis. SNP heritability enrichment analysis indicated that ulcerative colitis and multiple sclerosis have enrichment in several immune cells. Two‐sample Mendelian randomization indicated the causal effect of Crohn's disease on Parkinson's disease (FDR = 1.34 × 10 ⁻² , OR = 1.092). The methylome Mendelian randomization analysis also showed causal relationships between several intestinal and neurological diseases. Conclusions: Through comprehensive and systematic statistical analysis, we identified the global and local genetic correlations and causal relationships between several intestinal and neurological diseases and discovered shared pleiotropic loci and genes between them. Furthermore, the consistent SNP heritability enrichment observed in immune cells also indicated the crucial role of the immune system in the “gut–brain axis.”

Colorectal cancer (CRC) is the third most common cancer worldwide. Advanced CRC has a grim prognosis, so there is a high demand for an early non-invasive d

The intratumoral microbiota has been identified as an indispensable part of the tumor microenvironment (TME). However, the relationship between the intratumoral microbiota and host gene expression, as well as its impact on prognosis and TME immunity, remains unclear. We utilized a machine learning-based framework to identify microbiota-host gene associations across 14 tumors from The Cancer Genome Atlas (TCGA) and validated them in 11 tumors from the Gene Expression Omnibus. By calculating immune scores and identifying immune-related microbiota, we developed both a pan-cancer Immune and Prognosis-Related Microbial Score (IPRMS) and cancer-specific IPRMSs and analyzed the relationship between the cancer-specific IPRMSs and immune infiltration at bulk level and single-cell level. Furthermore, we systematically analyzed the potential mechanisms in which the intratumoral microbiota might affect prognosis using survival mediation analyses (SMAs). We identified gene subsets associated with microbiota, which were predominantly enriched in immune-related and cell signaling regulation pathways. Subsequently, we constructed the overall survival-related IPRMS and found that high-IPRMS patients had poorer prognosis in pan-cancer and increased presence of macrophage and cancer-associated fibroblasts. In contrast, low-IPRMS patients showed enrichment in tumor‐infiltrating lymphocytes. SMAs suggest that intratumoral microbiota may influence prognosis by affecting immune cells, pathways, and host genes. High-IPRMSs were consistently associated with poorer prognosis and lower abundance of tumor-infiltrating lymphocytes. At the single-cell level, cancer-associated fibroblasts were predominantly enriched in the high-IPRMS group, while tumor-infiltrating lymphocytes were also mainly enriched in the low-IPRMS group. Our research indicates that the intratumoral microbiota was associated with immune and prognosis, which may impact the cancer prognosis by modifying immune cells, pathways, and host gene expression. IMPORTANCE: The intratumoral microbiota is a vital part of the tumor microenvironment, yet its interplay with host gene expression and immune regulation remains unclear. Based on a machine learning framework for the interaction analysis of intratumoral microbiota and host genes, as well as the construction of the Immune and Prognosis-Related Microbial Score, our findings suggest that intratumoral microbiota may influence gene expression by affecting host pathways, especially immune-related pathways. Moreover, immune-related intratumoral microbiota are significantly associated with patient survival and TME immunity and may influence prognosis by affecting immune cells, pathways, or gene expression, offering new perspectives and potential biomarkers for predicting personalized patient prognosis in the future.

Background: Preoperative identification of breast cancer (BC) subtypes is essential for optimizing treatment strategies and improving patient outcomes. This study aimed to identify circulating cell-free DNA (cfDNA) methylation signatures to differentiate triple-negative breast cancer (TNBC) from other BC subtypes (non-TNBC). Methods: We initially performed a genome-wide analysis to identify differentially methylated CpG sites (DMCs; |Δβ| > 0.10 and P < 0.05) between five TNBC and nine non-TNBC tissues using the Infinium HumanMethylationEPIC BeadChip. These DMCs were further validated using large-scale data from the Cancer Genome Atlas (TCGA, n = 774; |Δβ| > 0. 25 and P < 0.05), and only CpG sites with average β values > 0.90 or < 0.10 in white blood cells (GSE50132, n = 233) were retained to minimize potential background methylation interference. Least absolute shrinkage and selection operator (LASSO) regression was applied to select optimal markers. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), and prognostic value was evaluated using Cox regression analysis. A multiplex digital droplet PCR (mddPCR) assay was developed to simultaneously detect cg06268921 and cg23247845 in cfDNA from TNBC (n = 33) and non-TNBC (n = 80) patients. Results: We identified 113 DMCs, of which eight were selected as optimal markers. They effectively discriminated TNBC from non-TNBC tissues. Then an eight-marker diagnostic panel was developed with an AUC of 0.922 in TCGA and 0.875 in GSE69914. Among them, cg06268921 was significantly associated with overall survival (hazard ratio = 0.249, P = 0.044) and disease-free survival (hazard ratio = 0.194, P = 0.015) in the TCGA-TNBC cohort. In the cfDNA cohort, cg06268921 significantly differentiated TNBC from non-TNBC (P < 0.001), and the combination of both markers yielded an AUC of 0.728. The findings demonstrated the potential of methylation signatures as non-invasive diagnostic tools for TNBC. Future research with larger cohorts is warranted.

Background: Breast ultrasonography and mammography remain predominant in breast tumor evaluations, yet they often result in false positives, particularly for tumors classified as BI‐RADS 4a or those no more than 10 mm, which are not ideal for core needle biopsy (CNB). Early‐stage breast cancer detection via circulating tumor DNA (ctDNA) methylation holds potential to bridge these diagnostic gaps.Methods: We curated a breast cancer‐specific panel by harnessing methylation profiles from in‐house and public databases. Leveraging breast tissue‐plasma‐leukocyte samples, we identified breast cancer‐specific markers, culminating in a 103‐marker methylation model which underwent rigorous validation in two independent cohorts. To assess its performance, we compared it against the accuracy of ultrasonography, mammography, and CNB.Results: The 103‐marker model exhibited remarkable proficiency in discerning benign from malignant breast tumors in plasma, with AUCs of 0.838, 0.838 and 0.823 in the validation set and two independent test sets, respectively. In BI‐RADS 4a breast cancer, when compared to ultrasonography or mammography, the model augmented breast cancer diagnostic accuracy by 40.58% and 25.49%, separately. Retrospective analyses suggested that our model achieved a sensitivity of 66.67% (4/6) and a specificity of 80.36% (45/56) for surgical patients in the BI‐RADS 4a category with tumors ≤ 10 mm, who did not undergo CNB, potentially sparing 45 benign patients from overtreatment. Notably, significant differences emerged in cancer scores between DCIS and invasive ductal carcinoma (p < 0.05). Higher cancer scores correlated with a more unfavorable prognosis (p < 0.05).Conclusions: The 103‐marker methylation model demonstrates impressive performance in distinguishing between malignant and benign tumors, facilitating precise early diagnosis of BC, and holds promise as a prognostic tool.

Plasma cell-free DNA (cfDNA) methylation is emerging as a non-invasive marker for various cancers. We aimed to identify specific methylation markers for diagnosis, differentiation from benign tumors, and prognosis of breast cancer (BC), which are essential for clinical decision-making yet seldom examined together. BC-specific methylation markers were identified using an in-house 850K dataset combined with large-scale publicly available 450 or 850K datasets. Multiplex digital droplet PCR (mddPCR) assays were developed to detect methylation in cfDNA from 201 BC patients, 83 healthy donors, and 71 individuals harboring benign tumors. Diagnostic and prognostic performance were evaluated using logistic and Cox regression models, respectively. The basic mechanism of a selected gene was explored through in vitro experiments. We identified 21 BC-specific methylated CpG sites that distinguished BC from tumor-adjacent tissues with high diagnostic accuracy. In the cfDNA cohort, three mddPCR assays targeting eight markers achieved an area under the curve (AUC) of 0.856 (95% CI = 0.814–0.898) for distinguishing BC from healthy controls, and 0.742 (95% CI = 0.684–0.801) for differentiating BC from benign tumors. Notably, combining these methylation markers with mammography and ultrasound improved diagnostic performance, resulting in an AUC of 0.898 (95% CI = 0.858–0.938) for differentiating BC from benign tumors. In the TCGA-BC dataset, prognostic model based on six sites was associated with poor overall survival prognosis (hazard ratio = 2.826, 95%CI: 1.841–4.338, p < 0.0001). In vitro experiments elucidated that FAM126A overexpression regulates BC cells malignant phenotypes. Our study demonstrated potential values of methylation-based markers in the detection and prognosis of BC.