Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches

BackgroundAngiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated.MethodThis study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3.ResultOverall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis.ConclusionOur study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3

Removal of 17β-Estradiol by Activated Charcoal Supported Titanate Nanotubes (TNTs@AC) through Initial Adsorption and Subsequent Photo-Degradation: Intermediates, DFT calculation, and Mechanisms

A low-cost composite of activated charcoal supported titanate nanotubes (TNTs@AC) was developed via the facile hydrothermal method to remove the 17β-estradiol (E2, a model of pharmaceutical and personal care products) in water matrix by initial adsorption and subsequent photo-degradation. Characterizations indicated that the modification occurred, i.e., the titanate nanotubes would be grafted onto the activated charcoal (AC) surface, and the micro-carbon could modify the tubular structure of TNTs. E2 was rapidly adsorbed onto TNTs@AC, and the uptake reached 1.87 mg/g from the dual-mode model fitting. Subsequently, the adsorbed E2 could be degraded 99.8% within 2 h under ultraviolet (UV) light irradiation. TNTs@AC was attributed with a unique hybrid structure, providing the hydrophobic effect, π−π interaction, and capillary condensation for E2 adsorption, and facilitating the electron transfer and then enhancing photocatalytic ability for E2-degradation. In addition, the removal mechanism of E2 was elucidated through the density functional theory calculation. Our study is expected to provide a promising material for environmental application

Efficiently Predicting Reaction Rates and Revealing Reactive Sites with a Molecular Image-Vision Transformer and Fukui Function Validation

Hydroxyl radicals (·OH) are prevalent in diverse ecosystems, including aquatic, atmospheric, and biological environments, and are crucial in the regulation of carbon and nitrogen cycles. However, there is still a lack of an easily applicable, efficient, and precise quantitative structure–activity relationship (QSAR) model for determining second-order reaction rate constants between ·OH and pollutants. Herein, a quantitative QSAR model was constructed utilizing molecular images and a vision transformer in conjunction with density functional theory (DFT) to efficiently and precisely forecast second-order reaction rate constants involving hydroxyl radicals (·OH) and pollutants. The model exhibits strong resilience and predictive precision achieved through transfer learning and fine-tuning, yielding test root-mean-square error values within the range of 0.2616–0.3239, surpassing the performance of the molecular image-convolutional neural network model. The reaction sites were accurately identified with a high level of precision, as evidenced by the F1-scores (>0.9775) and AUC-ROC values (>0.9665), along with validation using gradient-weighted class activation mapping and the Fukui function based on DFT. This research offers a cost-effective alternative to complex experimental methods and introduces a novel tool for environmental monitoring and risk assessment, highlighting its environmental significance and practical utility