Cancer invasion and metastasis are critical factors that influence patient prognosis. Carbonic anhydrase IX (CA IX) and carbonic anhydrase XII (CA XII) are key regulators of hypoxia and pH homeostasis in the tumor microenvironment (TME). It has been verified that both CA IX and CA XII play significant roles in promoting tumor metastasis in recent years, but most of the literature tends to treat them as separate entities rather than exploring their synergistic effects. This review provides a comprehensive overview of the roles of CA IX and CA XII in tumor invasion and metastasis, along with their clinical applications, including their spatial distribution characteristics, molecular mechanisms that facilitate tumor metastasis, and their potential for clinical translation. Moreover, this review incorporates the classical tumor core–invasive front model to propose a metabolic coupling model of CA IX and CA XII, offering a fresh perspective on precision therapies that target tumor metabolism. By emphasizing the metabolic coupling between these two molecules, this review offers new insights distinct from previous studies and highlights the clinical therapeutic potential of simultaneously targeting both during treatment. It sheds new light on future research and clinical applications, aiming to enhance the prognosis of cancer patients through innovative therapeutic strategies.

As one of the most prevalent malignant neoplasms among women globally, the optimization of therapeutic strategies for breast cancer has perpetually been a research hotspot. The tumor microenvironment (TME) is of paramount importance in the progression of breast cancer, among which the extracellular matrix (ECM) and hypoxia are two crucial factors. The alterations of these two factors are predominantly regulated by the Hippo signaling pathway, which promotes tumor invasiveness, metastasis, therapeutic resistance, and susceptibility. Hence, this review focuses on the Hippo pathway in breast cancer, specifically, how the ECM and hypoxia impact the biological traits and therapeutic responses of breast cancer. Moreover, the role of miRNAs in modulating ECM constituents was investigated, and hsa-miR-33b-3p was identified as a potential therapeutic target for breast cancer. The review provides theoretical foundations and potential therapeutic direction for clinical treatment strategies in breast cancer, with the aspiration of attaining more precise and effective treatment alternatives in the future.

The tumor microenvironment, especially the extracellular matrix (ECM), plays a critical role in cancer initiation and progression, although its underlying mechanisms remain incompletely understood. Conventional therapies (such as chemotherapy, surgery, and radiotherapy) often produce unsatisfactory outcomes. Immunotherapy, while showing limited clinical success to date, holds considerable promise. Growing evidence indicates that the biophysical properties of the ECM interact with immune cells, contributing to mechanisms of immunotherapy resistance in cancer. Alterations in these ECM properties can impair immune cell infiltration and function, thereby diminishing the effectiveness of immunotherapeutic approaches. This review explores how the biophysical features of the ECM and their crosstalk with tumor immune evasion pathways highlight the potential of ECM-targeted immunotherapy as an innovative strategy for cancer treatment.

Heart failure (HF) as the final stage of cardiovascular disease in the elderly leads to frequent readmissions and seriously affects their quality of life. This study aimed to develop a predictive model for hospital readmission or death during the vulnerable phase in elderly HF patients, and to identify the key associated risk factors. The dataset was randomly divided into 70% training sets and 30% validation sets. Three feature selection methods were applied to the training data, followed by the construction of 18 predictive models using six machine learning (ML) algorithms (XGBoost, LightGBM, AdaBoost, GBDT, GNB, and SVM). The performance of each model was assessed on the validation set using receiver operating characteristic (ROC) curves, sensitivity, accuracy, specificity, F1 score, and Brier score. SHapley Additive exPlanations (SHAP) were used to interpret the feature contributions both globally and locally. Eleven models achieved an area under the ROC curve (AUC) greater than 0.8, with the Boruta‐XGBoost model performing best, showing an AUC of 0.873 in the validation set, along with a sensitivity of 0.839, accuracy of 0.769, specificity of 0.747, F1 score of 0.634, and Brier score of 0.130. SHAP analysis revealed that the top five important features were hemoglobin (HGB), serum free thyroxine (FT4), age, diabetes, and serum potassium (K). The Boruta‐XGBoost based risk prediction model, combined with SHAP interpretation, demonstrated high predictive accuracy and robust interpretability for forecasting hospital readmission or death during the vulnerable phase in elderly HF patients.

Background: Obesity is a major public health concern and has been implicated in the pathogenesis of heart failure (HF). However, the causal nature of this relationship remains to be comprehensively elucidated through robust genetic epidemiological approaches. Objective: This systematic review and meta-analysis aimed to synthesize evidence from Mendelian randomization (MR) studies regarding the causal effects of genetically predicted obesity and related anthropometric traits on HF and its subtypes. Methods: A comprehensive literature search was conducted across PubMed, Google Scholar, Web of Science, Embase, and the Cochrane Library for studies published up to October 2025. Study quality was assessed, and random-effects meta-analysis were performed where applicable. Result: Genetically predicted adult BMI was significantly associated with increased HF risk across European (OR _SD : 1.79; 95% CI: 1.64–1.94), and East Asian ancestries (OR _kg/m ² : 2.17; 95% CI: 1.79–2.63). A significant association was also observed for HF with preserved ejection fraction (HFpEF) in European-ancestry individuals (OR _SD : 2.68; 95% CI: 1.07–4.28). Childhood body size traits were associated with HF (OR _SD : 1.30; 95% CI: 1.21–1.39). Fat mass, WC, WHR, and unfavorable adiposity were also identified as causal risk factors. Tissue-specific analyses indicated that both brain- and adipose-tissue-specific genetic instruments for BMI were associated with elevated HF risk. Conclusion: This study provides robust MR evidence supporting a causal role of multiple obesity-related traits, particularly adult BMI, in the development of HF across diverse populations. Further MR studies in non-European populations and investigations into specific pathways are warranted to enhance generalizability and mechanistic understanding. Systematic Review Registration: crd.york.ac.uk/PROSPERO/display_record.php?RecordID=576216 identifier, CRD42024576216.