RESUMO
Lidamycin (LDM) has been confirmed to have a strong anti-pancreatic cancer effect and can affect the mitochondrial function of pancreatic cancer cells. Mitofusin-2 (Mfn2) is located in the outer membrane of mitochondria, and Mfn2 is currently believed to play a role in cancer inhibition in pancreatic cancer. In order to explore whether the anti-pancreatic cancer effect of LDM is related to Mfn2-mediated mitophagy, Bioinformatics and in vitro cell experiments are used for experimental research. The experimental results demonstrated that Mfn2 is correlated with mitochondrial autophagy in pancreatic cancer. Lidamycin can increase the expression of Mfn2 in pancreatic cancer and affect the process of EMT, affect the level of reactive oxygen species and mitochondrial membrane potential, and increase the expression of mitochondrial autophagy marker proteins BNIP3L and Beclin1. These results demonstrate that Mfn2 affects mitophagy in pancreatic cancer cells by regulating the expression of Mfn2.
Assuntos
GTP Fosfo-Hidrolases , Proteínas de Membrana , Proteínas Mitocondriais , Mitofagia , Neoplasias Pancreáticas , Humanos , Neoplasias Pancreáticas/metabolismo , Neoplasias Pancreáticas/patologia , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/genética , Mitofagia/efeitos dos fármacos , GTP Fosfo-Hidrolases/metabolismo , GTP Fosfo-Hidrolases/genética , Linhagem Celular Tumoral , Proteínas Mitocondriais/metabolismo , Proteínas Mitocondriais/genética , Proteínas de Membrana/metabolismo , Proteínas de Membrana/genética , Aminoglicosídeos/farmacologia , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Espécies Reativas de Oxigênio/metabolismo , Mitocôndrias/metabolismo , Mitocôndrias/efeitos dos fármacos , Potencial da Membrana Mitocondrial/efeitos dos fármacos , Proteína Beclina-1/metabolismo , Proteína Beclina-1/genética , Proteínas Proto-Oncogênicas/metabolismo , Proteínas Supressoras de TumorRESUMO
Background: Lung cancer is one of the most common epithelial malignancies worldwide, accounting for the highest number of new cases and deaths. Metabolism is the sum of chemical reactions that produce energy to keep an organism alive. Several studies have shown that glucose and lipid metabolic disorders are common phenomena related to cancer cell genesis and progression. Methods: We screened the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD) samples of The Cancer Genome Atlas (TCGA) database, the Gene Set Enrichment Analysis (GSEA), and Gene Card database metabolism-related data, the metabolism-related DEGs of LUAD, as well as the univariate Cox regression analysis genes, for identifying significant outcome-related genes. The least absolute shrinkage and gene selection operator (LASSO) analysis was performed to establish the best risk model. Results: Our study aimed to establish a lipid metabolism-related model for predicting LUAD prognosis. Furthermore, our model's prognosis prediction power was evaluated by survival analysis. This study finally identified 11 DEGs related to lipid metabolism that were significantly associated with the prognosis of lung adenocarcinoma. It provided a new idea for the treatment of high-risk lung adenocarcinoma patients. Conclusions: The constructed clinical prognosis model of lung adenocarcinoma related to lipid metabolism provides a new idea for clinical treatment of lung adenocarcinoma.
RESUMO
Motion artifacts in magnetic resonance imaging (MRI) have always been a serious issue because they can affect subsequent diagnosis and treatment. Supervised deep learning methods have been investigated for the removal of motion artifacts; however, they require paired data that are difficult to obtain in clinical settings. Although unsupervised methods are widely proposed to fully use clinical unpaired data, they generally focus on anatomical structures generated by the spatial domain while ignoring phase error (deviations or inaccuracies in phase information that are possibly caused by rigid motion artifacts during image acquisition) provided by the frequency domain. In this study, a 2D unsupervised deep learning method named unsupervised disentangled dual-domain network (UDDN) was proposed to effectively disentangle and remove unwanted rigid motion artifacts from images. In UDDN, a dual-domain encoding module was presented to capture different types of information from the spatial and frequency domains to enrich the information. Moreover, a cross-domain attention fusion module was proposed to effectively fuse information from different domains, reduce information redundancy, and improve the performance of motion artifact removal. UDDN was validated on a publicly available dataset and a clinical dataset. Qualitative and quantitative experimental results showed that our method could effectively remove motion artifacts and reconstruct image details. Moreover, the performance of UDDN surpasses that of several state-of-the-art unsupervised methods and is comparable with that of the supervised method. Therefore, our method has great potential for clinical application in MRI, such as real-time removal of rigid motion artifacts.
Assuntos
Artefatos , Imageamento por Ressonância Magnética , Imageamento por Ressonância Magnética/métodos , Movimento (Física) , Processamento de Imagem Assistida por Computador/métodosRESUMO
This study investigated the effect of eleutheroside B on apoptosis and autophagy of lung cancer A549 and H460 cells and its molecular mechanism. MTT assay was used to detect the cytotoxicity of eleutheroside B at 5, 10, 15, 20, 25, 30, 35, 40, and 45 mmol·L~(-1) on lung cancer cells. Trypan blue exclusion assay was used to detect the effect of eleutheroside B on the survival rate of lung cancer A549 and H460 cells at different time. Colony formation assay was used to detect the effect of eleutheroside B on the proliferation of lung cancer A549 and H460 cells. AO/EB fluorescence double staining and Hoechst 33342 fluorescence staining were used to detect the effect of eleutheroside B on apoptosis of lung cancer A549 and H460 cells, and Western blot was used to detect apoptosis-related proteins to explore the apoptosis-related molecular mechanism. AO fluorescence staining and Western blot were used to detect the expression of autophagic vesicles and autophagy-related proteins P62 and LC3. The results showed that compared with the control group, eleutheroside B inhibited the growth of lung cancer A549 and H460 cells in a concentration-dependent manner. The optimal effect time of eleutheroside B on lung cancer A549 and H460 cells was 24 h, and the optimal concentrations were 28.64 and 22.16 mmol·L~(-1), respectively. Eleutheroside B could inhibit the colony formation of A549 and H460 cells. Compared with the control group, eleutheroside B could promote the formation of apoptotic bodies and induce cell apoptosis, as well as induce the expression of mitochondrial pathway-related proteins. Under the effect of eleutheroside B, the acidic autophagy vacuole in lung cancer cells increased, LC3â ¡ expression increased, P62 protein expression decreased, and PI3K, p-Akt, and p-mTOR protein expression decreased in the PI3K/Akt/mTOR pathway. Studies have shown that eleutheroside B can inhibit the growth of lung cancer cells, reduce colony formation, induce apoptosis of lung cancer cells through mitochondrial pathway, and induce autophagy. The mechanism may be related to the PI3K/Akt/mTOR pathway.