RESUMEN
BACKGROUND: KRAS mutation is a common driver of NSCLC, and there is a high proportion of lung cancer patients with KRAS G12C and G12D mutation. KRAS was previously considered an "undruggable" target, but the first KRAS G12C mutation-targeted drug AMG510, entered the market in 2021. However, treatments for G12D mutant tumors remain to be discovered. Salvianolic acid F (SalF), a monomer derived from the traditional Chinese medicine Salvia miltiorrhiza (SM), and KRAS had high binding affinity, especially for KRAS G12D. There is an urgent need to investigate effective and safe novel targeted therapies against KRAS G12D-driven NSCLC. METHODS: To evaluate the anticancer effect of SalF, we used KRAS-overexpressing lung cancer cells in vitro, a subcutaneous transplant tumor model, and KRAS G12D mice model in vivo. Then, the binding effect of SalF and KRAS was investigated using molecular docking, proteolytic assays and protein thermal shift assays. More critically, the PI3K/AKT signaling pathway in the lung was investigated utilizing RT-qPCR and Western Blotting. RESULTS: This is the first study to evaluate the anticancer effect of SalF on KRAS-overexpressing lung cancer cells or KRAS G12D lung tumors in vivo. We demonstrated that SalF inhibits OE-KRAS A549 cell migration, proliferation and promotes apoptosis in vitro. In addition, we used a subcutaneous transplant tumor model to show that SalF suppresses the growth of lung cancer cells in vivo. Interestingly, our group found that SalF was strongly bound to G12D and could decrease the stability and promoted the degradation of the KRAS G12D mutant through molecular docking, proteolytic assays and protein thermal shift assays. Further research demonstrated that in the KrasG12D mice model, after SalF treatment, the number and size of mouse lung tumors were significantly reduced. More importantly, SalF can promote apoptosis by inhibiting downstream PI3K/AKT signaling pathway activation. CONCLUSION: SalF activated apoptosis signaling pathways, suppressed anti-apoptotic genes, and inhibited lung cancer cell growth. These datas suggested that SalF could effectively inhibit the growth of lung tumors with KRAS G12D mutation. SalF may be a novel inhibitor against KRAS G12D, providing a strong theoretical basis for the clinical treatment of lung cancer with KRAS mutations.
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Ratones , Animales , Proteínas Proto-Oncogénicas c-akt/metabolismo , Fosfatidilinositol 3-Quinasas/metabolismo , Proteínas Proto-Oncogénicas p21(ras)/genética , Proteínas Proto-Oncogénicas p21(ras)/metabolismo , Simulación del Acoplamiento Molecular , Proliferación Celular , Transducción de Señal , Neoplasias Pulmonares/patología , Carcinoma de Pulmón de Células no Pequeñas/metabolismo , Transformación Celular Neoplásica , Mutación , Línea Celular Tumoral , Pulmón/patologíaRESUMEN
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy.