LncRNA miR663AHG represses the development of colon cancer in a miR663a-dependent manner

Abstract The MIR663AHG gene encodes both miR663AHG and miR663a. While miR663a contributes to the defense of host cells against inflammation and inhibits colon cancer development, the biological function of lncRNA miR663AHG has not been previously reported. In this study, the subcellular localization of lncRNA miR663AHG was determined by RNA-FISH. miR663AHG and miR663a were measured by qRT-PCR. The effects of miR663AHG on the growth and metastasis of colon cancer cells were investigated in vitro and in vivo. CRISPR/Cas9, RNA pulldown, and other biological assays were used to explore the underlying mechanism of miR663AHG. We found that miR663AHG was mainly distributed in the nucleus of Caco2 and HCT116 cells and the cytoplasm of SW480 cells. The expression level of miR663AHG was positively correlated with the level of miR663a (r = 0.179, P = 0.015) and significantly downregulated in colon cancer tissues relative to paired normal tissues from 119 patients (P < 0.008). Colon cancers with low miR663AHG expression were associated with advanced pTNM stage (P = 0.021), lymph metastasis (P = 0.041), and shorter overall survival (hazard ratio = 2.026; P = 0.021). Experimentally, miR663AHG inhibited colon cancer cell proliferation, migration, and invasion. The growth of xenografts from RKO cells overexpressing miR663AHG was slower than that of xenografts from vector control cells in BALB/c nude mice (P = 0.007). Interestingly, either RNA-interfering or resveratrol-inducing expression changes of miR663AHG or miR663a can trigger negative feedback regulation of transcription of the MIR663AHG gene. Mechanistically, miR663AHG could bind to miR663a and its precursor pre-miR663a, and prevent the degradation of miR663a target mRNAs. Disruption of the negative feedback by knockout of the MIR663AHG promoter, exon-1, and pri-miR663A-coding sequence entirely blocked these effects of miR663AHG, which was restored in cells transfected with miR663a expression vector in rescue experiment. In conclusion, miR663AHG functions as a tumor suppressor that inhibits the development of colon cancer through its cis-binding to miR663a/pre-miR663a. The cross talk between miR663AHG and miR663a expression may play dominant roles in maintaining the functions of miR663AHG in colon cancer development

Strategy for Avoiding Alicyclobacillus acidocaldarius Contamination of Apple Juice by Adding Magnetosomes/Antibacterial Peptide Composites

The survival of Alicyclobacillus acidocaldarius (A. acidocaldarius) in fruit juice after pasteurization results in high economic losses due to unpalatability. The present work addressed this issue by inhibiting the growth of A. acidocaldarius in apple juice by the addition of MN@IDR-1018 composites formed of innate defense regulator 1018 (IDR-1018) antibacterial peptides that are coupled on the surfaces of magnetosomes (MN) via amidation reactions. MN@IDR-1018 was demonstrated to provide excellent antibacterial activity against A. acidoterrestris with a minimum inhibitory concentration of 100 μg mL–1, which led to cell death via membrane dissolution and rupture. In addition, this concentration of MN@IDR-1018 was proved to present low toxicity in mice and had no discernible effect on the color, flavor, and aroma of apple juice. This enables the active material to be extracted from the apple juice by the application of a magnetic field, thereby avoiding the development of antibiotic resistance

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). Methods A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan–Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. Results To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. Conclusions Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. Critical relevance statement The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. Key points • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine. Graphical Abstrac