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Zihan Wang

Publications and source records attributed to Zihan Wang.

5 recordsLinked to original sources
PubMed2026

Association of lipoprotein-associated phospholipase A2 with recurrence risk and its predictive value in large artery atherosclerotic stroke.

OBJECTIVE: To investigate the association of lipoprotein-associated phospholipase A2 (Lp-PLA2) with large artery atherosclerotic (LAA) stroke and its predictive value for recurrence. METHODS: We consecutively enrolled 412 acute LAA stroke patients. Using a cutoff of 200&#xa0;ng/mL, patients were divided into high and low Lp-PLA2 groups, and into recurrence and non&#x2011;recurrence groups based on 1&#x2011;year follow&#x2011;up. Baseline characteristics, lipid profiles, National Institutes of Health Stroke Scale (NIHSS) scores, and vascular stenosis degree were compared. Binary logistic regression and Receiver Operating Characteristic (ROC) analysis were used to identify independent risk factors and evaluate predictive value. RESULTS: The high Lp-PLA2 group had significantly higher low-density lipoprotein cholesterol (LDL-C), small dense low-density lipoprotein cholesterol (sdLDL-C), prevalence of severe stenosis (&#x2265;70%), and proportion of NIHSS&#xa0;>&#xa0;15 (all P&#xa0;<&#xa0;0.05). The recurrence group showed elevated Lp-PLA2, higher LDL&#x2011;C and sdLDL-C, more severe neurological deficits, and more severe stenosis (all P&#xa0;<&#xa0;0.001). Multivariable regression identified elevated Lp-PLA2 (per 10&#xa0;ng/mL: OR&#xa0;=&#xa0;1.139, 95% CI: 1.089-1.191), moderate (OR&#xa0;=&#xa0;3.145) and severe (OR&#xa0;=&#xa0;11.663) neurological deficits, and severe stenosis (OR&#xa0;=&#xa0;9.390) as independent risk factors for recurrence (all P&#xa0;<&#xa0;0.05). The Area Under the Curve (AUC) of Lp-PLA2 was 0.75 (95% CI: 0.69-0.82), with an optimal cutoff of 208.95&#xa0;ng/mL. CONCLUSION: Elevated Lp-PLA2 is associated with adverse lipid profiles, more severe neurological deficits, and greater vascular stenosis in LAA stroke patients, and independently predicts 1&#x2011;year recurrence. Lp-PLA2 shows moderate predictive value, supporting its potential for risk stratification.

Humans↗
PubMed2026

Invasive mucinous adenocarcinoma of the lung: integrating molecular landscape, imaging phenotypes, and translational therapeutic strategies.

Invasive mucinous adenocarcinoma (IMA) of the lung is an uncommon but clinically important subtype of lung adenocarcinoma with distinctive radiologic, histopathologic, and molecular features. Its indolent symptoms, mucin-rich growth pattern, and frequent pneumonia-like or multifocal presentation can obscure early diagnosis and complicate distinction from infection, synchronous primary tumors, and intrapulmonary spread. This review integrates current evidence on the clinical course, imaging phenotypes, diagnostic workflow, histopathologic features, molecular alterations, tumor immune microenvironment, and treatment response patterns of IMA. Emphasis is placed on the relationship between radiologic appearance and underlying mucinous pathology, the clinical significance of spread through air spaces (STAS), and the need for adequate tissue sampling and comprehensive molecular profiling. Compared with non-mucinous lung adenocarcinoma, IMA is enriched for KRAS mutations and selected fusion or receptor alterations, whereas canonical EGFR mutations are less frequent. These biological differences help explain why treatment strategies extrapolated from broader non-small cell lung cancer (NSCLC) populations may be insufficient, particularly for multifocal, pneumonic-type, or advanced disease. Although surgery can provide favorable outcomes in localized disease, systemic therapy remains challenging, and the role of immunotherapy requires further clarification. Future progress will depend on integrated imaging-pathology-genomic models, prospective IMA-specific cohorts, and translational studies aimed at refining classification and developing individualized therapeutic strategies.

Invasive mucinous adenocarcinoma (IMA)↗
PubMed2026

ELN orchestrates prometastatic and immunosuppressive niche in bladder cancer via TGFB1 autocrine signaling.

Bladder cancer (BCa) mortality is mainly driven by metastatic dissemination and an immunosuppressive tumor microenvironment. Here, we identify ELN (tropoelastin), an extracellular matrix protein abundantly secreted by cancer-associated fibroblasts (CAFs), as a critical determinant of these processes and a marker of poor prognosis. ELN promotes epithelial-mesenchymal transition (EMT), facilitates lymphatic spread, and induces immune dysfunction characterized by macrophage polarization toward an M2 phenotype and T cell exhaustion. Mechanistically, ELN functions as a binding partner of TGF-&#x3b2; receptor 2 (TGFBR2), thereby triggering SMAD2/3-dependent TGF-&#x3b2;1 secretion and establishing a feed forward signaling loop. This ELN/TGFBR2/TGF-&#x3b2;1 axis amplifies metastatic capacity and immunosuppressive signaling, ultimately accelerating disease progression and diminishing responsiveness to immune checkpoint blockade. Functional studies in BCa organoids and murine models demonstrated that pharmacologic blockade of the ELN-TGFBR2 interaction effectively suppressed tumor metastasis and restored antitumor immunity. Collectively, our findings establish ELN as a CAF-derived driver of metastasis and immune evasion in BCa. Targeting the ELN-TGFBR2 interaction offers a promising therapeutic strategy to limit metastatic progression and enhance the efficacy of immunotherapy in this lethal disease.

Animals↗
PubMed2026

Mitochondrial retrograde signal through GCN5L1 transition-mediated PPAR&#x3b3; stabilization promotes MASLD development.

Mitochondrial retrograde signaling plays crucial roles in maintaining metabolic homeostasis via regulating genome modification and oxidative responsive gene expression. In this study, we identified GCN5L1, a protein localized in both mitochondria and cytoplasm, and demonstrated its specific translocation from mitochondria to cytoplasm during lipid overload and high-fat diet feeding. Using transcriptome and proteome analyses, we identified that cytoplasmic GCN5L1 binds to and promotes the acetylation of PPAR&#x3b3; at lysine 289 (K289). This acetylation protected PPAR&#x3b3; from ubiquitination-mediated degradation by proteasome. GCN5L1 translocation enhanced protein stability of PPAR&#x3b3; and subsequently promoted lipid accumulation in both cultured cells and murine models. Our study further reveals that PPAR&#x3b3;-K289 mutation reduces the ubiquitination of PPAR&#x3b3; and exacerbates liver steatosis in mice. These findings unveil a mitochondrial retrograde signaling during lipid overload, which regulates the crucial lipogenic transcriptional factor. This discovery elucidates an unrecognized mitochondrial function and mechanism underlying hepatic lipid synthesis.

Animals↗
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&#x2009;=&#x2009;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↗