Biomedical subjects

Yizhi Wang

Publications and source records attributed to Yizhi Wang.

3 recordsLinked to original sources

PubMed2026

Deciphering miRNA-mediated genetic architecture of immune cell subsets in hypertrophic scars and keloids: A 2-step Mendelian randomization study unveiling causal associations.

This study aimed to investigate the potential causal roles of specific circulating microRNAs (miRNAs) and immune cell subsets in the pathogenesis of hypertrophic scars and keloids using a 2-step Mendelian randomization framework. We employed a 2-sample Mendelian randomization approach to evaluate the causal relationships between miRNAs, immune cell genotypes, and scar phenotypes. The analysis integrated miRNA expression quantitative trait loci, immune cell genome-wide association studies, and scar datasets. A 2-step mediation analysis was conducted to assess the indirect effects of miRNAs on scars through immune cell genotypes, using inverse variance weighted methods and complementary sensitivity analyses to ensure robustness. Our analysis identified significant associations between specific miRNAs and scar phenotypes. Notably, miR-6887-5p exhibited a total effect on keloid formation risk (β = 0.324, 95% confidence interval [CI]: 0.073-0.576) and a direct effect (β = 0.283, 95% CI: 0.027, 0.538), with a marginally significant mediation effect through B-cell activating factor receptor on CD20- CD38- B cells (β = 0.042, 95% CI: -0.001, 0.084, P = .047). For hypertrophic scars, miR-345-5p demonstrated a significant total effect (β = -0.501, 95% CI: -0.903, -0.099) and direct effect (β = -0.469, 95% CI: -0.872, -0.066), with a significant mediation effect through CD28+ CD45RA- CD8dim T cell percentage (β = -0.032, 95% CI: -0.062, -0.002, P = .034). miR-4801 showed a significant total effect (β = -0.246, 95% CI: -0.429, -0.064) and direct effect (β = -0.218, 95% CI: -0.402, -0.033), with a marginally significant mediation effect through T cell absolute count (β = -0.028, 95% CI: -0.057, -0.000, P = .043). These findings highlight the interplay between miRNAs and immune cell subsets in scar pathogenesis. This study provides preliminary evidence for the causal roles of specific miRNAs and immune cell subsets in scar formation, emphasizing the potential of miRNA-immune cell axes as therapeutic targets. While the identified associations offer important insights into the molecular mechanisms of scar heterogeneity, further validation through mechanistic studies and clinical trials is necessary to translate these genetic insights into clinical interventions.

Humans↗

PubMed2026

A 2-step, 2-sample Mendelian randomization study of gut microbiota, blood metabolites and dry age-related macular degeneration.

Dry age-related macular degeneration (dAMD) is the leading cause of blindness among elderly people in developed countries. The main objective of this study is to investigate the causal relationship between gut microbiota (GM), blood metabolites, and dAMD among European participants. Based on the genome-wide association analysis database, double sample Mendelian randomization (MR) analysis was performed on GM, blood metabolites, and dAMD. The inverse-variance weighted method is used to estimate the causal relationship between GM, blood metabolites, and dAMD, while multiple methods are employed to eliminate pleiotropy and heterogeneity. A 2-step MR analysis quantitatively assessed the effect of metabolite-mediated GM on dAMD. In MR analysis, 15 GM were found to be associated with increased or decreased risk of dAMD, and 18 blood metabolites were found to be associated with increased or decreased risk of dAMD. Our research also found that the potential association between GM and dAMD may be mediated by blood metabolite levels, specifically, ADpSGEGDFXAEGGGVR levels accounted for 38.9% of the causal pathway from genus Parasutterella to dAMD. Our research findings indicate that certain GM and blood metabolites can affect the onset of dAMD, and increasing the abundance of genus Parasottella can increase the risk of dAMD through the mediation of ADpSGEGDFXAEGGGVR levels.

Humans↗

PubMed2026

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial intelligence (AI)-assisted diagnostic system for GAS based exclusively on H&E-stained histopathological images. We included 309 slides from 96 GAS cases collected at Peking University Third Hospital from January 2018 to January 2025, representing the largest GAS cohort reported to date for AI research. In addition, we incorporated other morphologically analogous diseases, encompassing a total of 1,320 slides sourced from four categories: normal cervical mucosa (NORM), benign endocervical lesion entities (BELE), HPV-associated adenocarcinoma (HPVA), and endometrioid carcinoma with mucinous differentiation (ECMD). We developed GASPath, based on a novel multiple instance learning framework that efficiently captures fine-grained morphological variations from H&E-stained images. Beyond internal validation, GASPath was evaluated across 12 independent retrospective cohorts and further subjected to large-scale real-world validation on more than 7,000 samples from March 2024 to April 2025. Across three stages, GASPath demonstrated high performance. In internal validation (Stage I), it achieved an accuracy of 0.980 (95% CI 0.977-0.983) and an ROC-AUC of 0.995 (95% CI 0.994-0.997). In external validation (Stage II), the sensitivity reached 0.902 and improved to 0.968 with proposed strategies. For biopsy samples, GASPath achieved an ROC-AUC of 0.990 (95% CI 0.984-0.997). In large-scale real-world deployment (Stage III, n = 7,056), GASPath achieved a balanced accuracy of 0.953, with 100% sensitivity for GAS (45/45 cases correctly identified). The heatmaps highlight morphological features of GAS that are easily underestimated, such as irregular, angulated glands, subtle loss of nuclear polarity, and mild cytologic atypia, which show substantial morphological overlap with other diagnostic categories. GASPath enables high-sensitivity detection of GAS in routine H&E-stained slides, obviating the need for extensive auxiliary testing while preventing underdiagnosis and misdiagnosis. This advancement addresses a critical gap by streamlining diagnostic workflows without compromising accuracy. Its implementation could enable cost-effective, scalable AI-assisted diagnostics, potentially transforming the early detection and management of this aggressive cancer subtype.

Female↗