BACKGROUND: Pulmonary hypertension (PH) is a fatal progressive disease characterized by pulmonary endothelial injury and occlusive pulmonary vascular remodeling. Lysosomal protease cathepsin L degrades essential molecules to participate in the human pathophysiological process. BMPR2 (bone morphogenetic protein type II receptor) deficiency, an important cause of PH, results from mutational inactivation or excessive lysosomal degradation and induces caspase-3-mediated cell death. Given recent evidence that pyroptosis, as a new form of programmed cell death, is induced by caspase-3-dependent GSDME (gasdermin E) cleavage, we hypothesized that cathepsin L might promote PH through BMPR2/caspase-3/GSDME axis-mediated pyroptosis. METHODS: Cathepsin L expression was evaluated in the lungs and plasma of patients with pulmonary arterial hypertension. The role of cathepsin L in the progression of PH and vascular remodeling was assessed in vivo. Small interfering RNA, specific inhibitors, and lentiviruses were used to explore the mechanisms of cathepsin L on human pulmonary arterial endothelial cell dysfunction. RESULTS: Cathepsin L expression is elevated in pulmonary artery endothelium from patients with idiopathic pulmonary arterial hypertension and experimental PH models. Genetic ablation of cathepsin L in PH rats relieved right ventricular systolic pressure, pulmonary vascular remodeling, and right ventricular hypertrophy, also restoring endothelial integrity. Mechanistically, cathepsin L promotes caspase-3/GSDME-mediated endothelial cell pyroptosis and represses BMPR2 signaling activity. Cathepsin L degrades BMPR2 via the lysosomal pathway, and restoring BMPR2 signaling prevents the pro-pyroptotic role of cathepsin L in PAECs and experimental PH models. CONCLUSIONS: These results show for the first time that cathepsin L promotes the development of PH by degrading BMPR2 to induce caspase-3/GSDME-mediated endothelial pyroptosis.

Coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) often coexist and mutually exacerbate each other by shared mechanisms (including deregulated glucolipid metabolism and insulin resistance), yet their global metabolic signature remain poorly characterized. In this exploratory study, we collected serum samples from 21 CAD patients, 20 T2DM subjects, and 20 healthy individuals (HH). Metabolomics and lipidomics were performed, and bioinformatics analysis identified disease‐related pathways and biomarkers. A machine learning‐based diagnostic prediction model was built using these metabolic biomarkers. Our analysis unveiled significant metabolic resemblance between CAD and T2DM. Apart from higher covariates such as age and weight compared to the healthy population, both conditions demonstrated elevated levels of triglycerides (TGs), diglycerides (DGs), phosphatidylcholines (PCs), and phosphatidylethanolamines (PEs). Concurrently, lipid metabolism‐related signaling pathways were notably activated. Machine learning identified seven lipid metabolites (e.g., TG 16:0/16:0/18:3, PC 31:1, and PE 34:1) that exhibited an auROC ≥ 0.9 for both CAD and T2DM, suggesting their potential as shared biomarkers. A prediction model based on 7‐metabolites achieved predictive accuracies exceeding 95% and 92% for CAD and T2DM, respectively. Furthermore, the model also demonstrated predictive accuracies surpassing 98% and 95% in an external validation cohort, highlighting its robust predictive performance. Through this exploratory study, we have preliminarily delineated the metabolic signatures of CAD and T2DM, and further strengthened the intrinsic relationship between the two diseases from a metabolic perspective.

Hepatocellular carcinoma (HCC) emerges as the third leading cause of cancer mortality, contributing to approximately 830,000 deaths annually. The mechanisms driving its pathogenesis remain largely elusive. Through bioinformatic scrutiny, Mitochondrial Carrier 1 (MTCH1), a component of the mitochondrial carrier family, has been pinpointed as potentially pivotal in HCC evolution. Examination of The Cancer Genome Atlas (TCGA) database indicated a pronounced increase in MTCH1 expression within HCC tissues versus normal liver counterparts. Subsequent analyses, utilizing both Kaplan-Meier mapper and Gene Expression Profiling Interactive Analysis (GEPIA) datasets, associated elevated MTCH1 levels with reduced overall survival (OS) and diseasefree survival (DFS). Complementary in vitro assessments confirmed that MTCH1 downregulation suppresses HCC cell proliferation and notably diminishes HCC xenograft tumor growth in murine models. Additional explorations, including Gene Set Enrichment Analysis (GSEA), STRING database interrogation, and quantitative PCR (qPCR) experiments, suggest MTCH1's involvement in HCC progression via the CDK-RB-E2F signaling axis. Collectively, these insights endorse MTCH1 as a promising therapeutic target for HCC, underscoring its significance in the disease's molecular framework and potential treatment innovation.

Gefitinib is the most frequently employed anti‐lung cancer drug, but its clinical effectiveness is often compromised due to the development of drug resistance. Given that gefitinib failure to long‐term inhibition of the growth of lung carcinoma cell lines, which mirrors the resistance observed in clinical patients, new approaches to improve the curative effect of gefitinib should be found. Surprisedly, inhibiting separase with the specific inhibitor Sepin‐1 has been found to effectively enhance gefitinib‐induced cytotoxic in lung cancer cell by promoting the development of PANoptosis, which includes pyroptosis, apoptosis, and necroptosis. Moreover, in vivo experiments also demonstrated that the combination of Sepin‐1 and gefitinib can induce significant regression of lung xenograft tissues. Mechanically, loss of separase plus gefitinib decreases the expression of PTBP1 and TAK1. Overexpression of PTBP1 or TAK1 suppresses this interaction‐induced PANoptosis by promoting the inactivation of RIPK1. In addition, clinical data showed that better effective of gefitinib maybe associated with lower separase expression or higher PANoptosis marker expression in patient lung carcinoma tissues. Thus, these findings provide a novel anti‐lung cancer strategy and highlight separase as a potential target for overcoming gefitinib resistance in lung cancer treatment.

Lung cancer is the leading cause to induce cancer-related mortality. Effective biomarkers for prediction the occurrence of lung cancer is urgently needed. Our previous studies indicated that pyroptosis-related cytokines TNF-α, IFN-γ, MIP-1α, MIP-1β, MIP-2 and IP-10 is important to influence the efficacy of chemotherapy drug in lung cancer tissues. But the role of pyroptosis-related cytokines in prediction the occurrence of lung cancer is still unknown. Blood samples were collected from 258 lung cancer patients at different stage and 80 healthy volunteers. Serum levels of pyroptosis-related cytokines including TNF-α, IFN-γ, MIP-1α, MIP-1β, MIP-2 and IP-10 were measured by Cytometric Bead Array (CBA). ROC curve was performed to evaluate the cut-off value and diagnosis value for prediction and diagnosis of lung cancer. Compared with control group, the levels of IP-10, MIP-1α, MIP-1β, MIP-2 and TNF-α were significantly higher in lung cancer patients (45.5 (37.1–56.7): 57.2 (43.0–76.5), 34.4 (21.8–75.2): 115.4 (96.6–191.2), 49.3 (25.6–78.7): 160.5 (124.9–218.6), 22.6 (17.8–31.2): 77.9 (50.1–186.5), 3.80 (2.3–6.2): 10.3 (5.7–16.6)), but the level of IFN-γ was decreased in the patients (12.38 (9.1–27.8): 5.9 (3.5–9.7)). All the above cytokines were significantly associated with the diagnosis of lung cancer, and the AUC values of IFN-γ, IP-10, MIP-1α, MIP-1β, MIP-2, and TNF-α were 0.800, 0.656, 0.905, 0.921, 0.914, and 0.824. And the AUC can rise to 0.986 after combining the above factors, and the sensitivity and specificity also up to 96.7 % and 93.7 %, respectively. Additionally, TNF-α (r = 0.400, P < 0.01), MIP-2 (r = 0.343, P < 0.01), MIP-1α (r = 0.551, P < 0.01) and MIP-1β (r = 0.403, p < 0.01) were positively associated with occurrence of lung cancer, but IFN-γ (r = −0.483, p < 0.01) was negatively associated with occurrence of lung cancer. As far as the potential of early diagnosis of lung cancer, TNF-α (AUC = 0.577), MIP-1α (AUC = 0.804) and MIP-1β (AUC = 0.791) can predict the early stage of lung cancer, and combination of the above three cytokines has a better predictive efficiency (AUC = 0.854). Our study establishes a link between the levels of IP-10, MIP-1α, MIP-1β, MIP-2, TNF-α and IFN-γ and diagnosis of lung cancer. Besides, we observed a synergistic effect of these five pyroptosis-related cytokines in diagnosing lung cancer patient, suggesting their potential as biomarkers for lung cancer diagnosis. Moreover, the combination of TNF-α, MIP-1α and MIP-1β are also potential predictors for the early diagnosis of lung cancer.

Hypokalemia is a frequent electrolyte abnormality in the ICU and is associated with short-term mortality. However, risk prediction in this population remains challenging, and the prognostic information provided by routinely collected physiologic and laboratory variables has not been fully characterized in externally evaluated machine-learning models. Adult ICU admissions with documented hypokalemia (serum K⁺ < 3.5 mmol/L) and ICU stay > 24 h were analyzed as a retrospective admission-window risk-stratification study among patients retrospectively identified as hypokalemic, rather than as an admission-time deployment model for all ICU admissions. MIMIC-III was used for model development, MIMIC-IV for temporal validation, and two Chinese ICU cohorts for exploratory geographic evaluation. Because baseline-characterization, locked endpoint-specific evaluation, subgroup, and sensitivity analyses use different denominators, all analysis subsets and event counts are mapped explicitly in Supplementary Table S7. Twenty routinely available variables were selected using leakage-conscious preprocessing and LASSO. Model performance was evaluated with discrimination, calibration, decision-curve analysis, subgroup analyses, and sensitivity analyses; SHAP was used as a post-hoc model-audit method rather than as evidence of causality, actionability, or bedside interpretability. In the locked endpoint-specific evaluation, the 20-variable stacking model achieved an AUROC of 0.783 (95% CI, 0.708–0.857; n = 163; events = 50) in the MIMIC-III internal test subset and 0.838 (95% CI, 0.808–0.868; n = 1,413; events = 130) in MIMIC-IV temporal validation. The simplified 8-variable model achieved corresponding AUROCs of 0.717 (95% CI, 0.627–0.807) and 0.835 (95% CI, 0.805–0.866). Exploratory geographic evaluation showed heterogeneous performance in Third Xiangya (AUROC 0.830; 95% CI, 0.750–0.910) and Nanhua (AUROC 0.717; 95% CI, 0.616–0.819), supporting cautious interpretation and likely center-specific heterogeneity. We developed a SHAP-audited model for retrospective short-term mortality risk stratification in ICU patients with documented hypokalemia. The primary analysis does not eliminate temporal-selection bias; therefore, the findings support methodological and prospective evaluation but do not establish clinical deployment readiness, superiority over established severity scores, or first-hypokalemia-triggered use.