Despite substantial breakthroughs in the treatment of hepatocellular carcinoma (HCC) in recent years, many patients are diagnosed in the middle or late stages, denying them the option for surgical excision. Therefore, it is of great importance to find effective therapeutic targets of HCC. In this study, it is found that Gap junction protein beta‐2 (GJB2) is highly enriched in malignant cells based on single‐cell RNA sequencing and higher expression of GJB2 indicates a worse prognosis. The localization of GJB2 in HCC cancer cells is changed compared with normal liver tissue. In cancer cells, GJB2 tends to be located in the cytoplasm and nucleus, while in normal tissues, GJB2 is mainly located on the cell membrane. GJB2 is related to glycolysis, promoting NF‐κB pathway via inducing the ubiquitination degradation of IκBa, and activating HIF‐1α/GLUT‐1/PD‐L1 pathway. In addition, GJB2 knockdown reshapes tumor immune microenvironment and Salvianolic acid B inhibits the activity of GJB2. In conclusion, GJB2 promotes HCC progression by activating glycolysis through cytoplasmic translocation and generating a suppressive tumor microenvironment. Salvianolic acid B inhibits the expression of GJB2 and enhances the sensitivity of anti‐PD1 therapy, which may provide insights into the development of novel combination therapeutic strategies for HCC.

The significant bidirectional comorbidity risk and extensive subclinical involvement observed between chronic obstructive pulmonary disease (COPD) and inflammatory bowel disease (IBD) underscore the pivotal role of the “gut-lung axis” in cross-organ pathological crosstalk. Here, we comprehensively review the molecular and immunological mechanisms driving this comorbidity. Genome-wide association studies (GWAS) have substantiated genetic pleiotropy that underpins a shared susceptibility to mucosal defense deficits. The “common mucosal immune system” (CMIS), rooted in embryonic homology, constitutes the anatomical basis for this pathological interplay, wherein aberrant immune cell homing, Th17/Treg imbalance, and the cross-organ trafficking of innate lymphoid cells (ILCs) mediate the distal dissemination of inflammation. Furthermore, gut dysbiosis-induced depletion of short-chain fatty acids (SCFAs), acting in concert with systemic hypoxia and the IL-23/IL-17 axis, potentiates synergistic injury to the gut-lung barriers. We highlight the reciprocal, bidirectional causality of this “hypoxic loop” and its testable mechanistic predictions for barrier dysfunction. Furthermore, we evaluate pharmacological evidence from drug repositioning, alongside a critical examination of the “hidden axis” of clinical therapies as profound iatrogenic confounders. Elucidating these mechanisms is critical for establishing systemic diagnostic and therapeutic strategies; interventions targeting shared molecular targets and the microbiota hold promise for achieving a simultaneous treatment approach for these distinct pathologies.

Insulin resistance (IR) and obesity are recognized as major drivers of cardiovascular disease (CVD). The triglyceride-glucose index-a body shape index (TyG-ABSI), a novel metric integrating lipid metabolism with body morphology, may enhance risk stratification. Although this index has been verified in Western cohorts, its long-term prognostic value and prospective incremental benefit over conventional indices remain uncharacterized in the Chinese population, who present a distinct East Asian adiposity phenotype. Utilizing follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011 to 2020, we enrolled participants free of CVD at baseline with complete essential information. Cox proportional hazards models were employed to estimate the associations between TyG-ABSI and incident CVD, while restricted cubic splines (RCS) were utilized to characterize dose-response relationships. The discriminative power of TyG-ABSI was evaluated using time-dependent receiver operating characteristic (ROC) curves and C-indices, with comparisons against other related indices, including ABSI, triglyceride-glucose (TyG), TyG-body mass index (TyG-BMI), TyG-Chinese visceral adiposity index (TyG-CVAI), TyG-waist circumference (TyG-WC) and TyG-waist-to-height ratio (TyG-WHtR). Predictive increments were quantified via net reclassification improvement (NRI) and integrated discrimination improvement (IDI), and clinical utility was assessed through decision curve analysis (DCA). Cross-parameter correlation testing was executed to evaluate index collinearity. Among 7197 participants, 1267 incident CVD cases occurred during the follow-up. In the fully adjusted model, baseline TyG-ABSI was independently associated with an increased risk of composite CVD (HR = 1.08, 95% CI 1.01-1.14, P = 0.016) and incident stroke (HR = 1.16, 95% CI 1.05-1.28, P = 0.003), whereas no independent correlation was identified for heart conditions. RCS analysis revealed no significant non-linear association for either CVD (P = 0.855) or stroke (P = 0.728). TyG-ABSI improved prediction over ABSI alone, but it offered no advantage over traditional indices. DCA confirmed that traditional indices had better net benefit than TyG-ABSI or its components. The independent predictive value remained highly consistent across sensitivity analyses. Correlation analysis revealed that while traditional indices exhibited severe multicollinearity clustering (r: 0.751-0.890), ABSI achieved near-perfect orthogonal independence from BMI (r = - 0.056). TyG-ABSI was significantly and positively associated with an increased risk of incident CVD. Although TyG-ABSI as an isolated screening tool did not surpass traditional parameters like TyG-BMI in overall prospective accuracy, it exhibited only a negligible correlation with BMI, demonstrating that this metric is not redundant. Consequently, TyG-ABSI retains the potential to capture specific pathogenic signals independent of gross body mass. Future risk stratification toolkits should consider transitioning toward an integrated Anthropometric Risk Indicator (ARI) framework that couples the metabolic sensitivity of TyG-BMI with the specific body shape risks indicated by TyG-ABSI.

Current liver cancer research lacks reliable in vitro models that replicate tumor pathophysiology. This study establishes primary liver cancer (PLC) organoids from three major subtypes—hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (CHC)—to enable precise diagnostics and personalized therapies through comprehensive genomic profiling. Organoid cultures were generated from 11 PLC patients (5 HCC, 3 ICC, 3 CHC). Whole exome sequencing (WES), RNA-seq, and single-cell RNA-seq (scRNA-seq) were performed to analyze molecular differences. Drug screening targeting subtype-specific pathways was conducted to validate sequencing findings. WES and RNA-seq confirmed that organoids retained parental tumor genetics and heterogeneity, distinct from paracancerous tissues. scRNA-seq revealed distinct cell populations in HCC, ICC, and CHC organoids. Lipid metabolism was enriched in HCC organoids; tumor migration pathways were upregulated in ICC organoids; and mitochondrial function was enhanced in CHC organoids. Rosuvastatin inhibited HCC growth by targeting lipid metabolism, while pemigatinib reduced ICC malignancy by suppressing epithelial-mesenchymal transition. Regorafenib impaired mitochondrial function in CHC organoids, slowing progression. PLC-derived organoids serve as robust tools for biomarker discovery and drug screening. scRNA-seq elucidates inter- and intra-tumoral heterogeneity, offering insights for precision therapy in liver cancer. This model advances personalized treatment strategies for diverse PLC subtypes. The online version contains supplementary material available at 10.1007/s13402-026-01190-w.

Although the cardiovascular-kidney-metabolic (CKM) syndrome framework offers a structured approach to risk stratification, the identification of individual

Background: Stroke-associated pneumonia (SAP) is a common and serious infectious complication following acute ischemic stroke (AIS), leading to worsened clinical outcomes. We aimed to develop and validate a novel predictive model based on Blending Ensemble Learning, to enable early, precise screening and individualized risk stratification for SAP. Methods: In this multi-center study, a derivation cohort was initially established using 1,123 patients admitted between 2022 and 2024. Subsequently, 199 patients were prospectively enrolled in early 2025 as a temporal validation set, and 287 patients were extracted from a regional health big data platform (covering 12 hospitals) to serve as an external validation set. High-value clinical and biomarker features were identified via least absolute shrinkage and selection operator (LASSO) regression. To overcome the limitations of single classifiers, a Blending Ensemble Learning strategy was adopted to construct the final predictive model. The model underwent comprehensive evaluation for discrimination, calibration, and clinical utility, and was benchmarked against single random forest (RF) and extreme gradient boosting (XGBoost) models. Results: Seventeen predictors were identified, including systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), the National Institutes of Health Stroke Scale (NIHSS), and D-dimer. The Blending ensemble model demonstrated superior discrimination in the validation cohort, achieving an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.898 (95% CI: 0.845–0.951), which significantly outperformed the optimal single model. Furthermore, SHapley Additive exPlanations (SHAP) analysis highlighted hyperinflammation, immunothrombosis, and nutritional depletion as pivotal mechanisms driving SAP pathogenesis. Conclusion: The proposed Blending ensemble model exhibits excellent generalizability and high sensitivity. The accompanying web-based tool provides robust decision support for identifying high-risk patients and implementing early precision interventions.