An online diagnosis method for cancer lesions based on intelligent imaging analysis.

With the popularization and application of artificial intelligence and medical image big data in the field of medical image, the universality of modes and the rapid development of deep learning have endowed multi-mode fusion technology with great development potential. Technologies of 5G and artificial intelligence have rapidly promoted the innovation of online hospitals. To assist doctors in the remote diagnosis of cancer lesions, this article proposes a cancer localization and recognition model based on magnetic resonance images. We combine a convolution neural network with Transformer to achieve local features and global context information, which can suppress the interference of noise and background regions in magnetic resonance imaging. We design a module combining convolutional neural networks and Transformer architecture, which interactively fuses the extracted features to increase the cancer localization accuracy of magnetic resonance imaging (MRI) images. We extract tumor regions and perform feature fusion to further improve the interactive ability of features and achieve cancer recognition. Our model can achieve an accuracy of 88.65%, which means our model can locate cancer regions in MRI images and effectively identify them. Furthermore, our model can be embedded into the online hospital system by 5G technology to provide technical support for the construction of network hospitals.

Long Intergenic Nonprotein Coding RNA 173 Inhibits Tumor Growth and Promotes Apoptosis by Repressing Sphingosine Kinase 1 Protein Expression in Pancreatic Cancer.

Pancreatic cancer is a common malignant tumor worldwide. Extensive studies have been conducted on the functional role of long noncoding RNAs in pancreatic cancer. In this study, long intergenic nonprotein coding RNA 173 (LINC00173) was highly expressed in pancreatic cancer tissues. In vitro functional experiments showed that LINC00173 overexpression inhibited the proliferation and invasion of pancreatic cancer cells and promoted cell apoptosis in MIA PaCa-2 and PANC-1 cells. RNA sequencing analysis and Western blot assays demonstrated that LINC00173 reduced the expression of sphingosine kinase 1 (SPHK1) and then inhibited the protein expression of activated phospho-protein kinase B (AKT) and NF-κB. In vivo functional assays also revealed that LINC00173 inhibited the growth of pancreatic cancer xenografts, repressed cell proliferation, promoted cell apoptosis, and inhibited SPHK1 expression. The combined results of this study indicate that LINC00173 inhibits pancreatic cancer progression by repressing SPHK1 expression. Improving LINC00173 may represent a therapeutic strategy for pancreatic cancer in the future.

Metformin Increases the Chemosensitivity of Pancreatic Cancer Cells to Gemcitabine by Reversing EMT Through Regulation DNA Methylation of miR-663.

BACKGROUND: Pancreatic cancer is a devastating malignancy with poor prognosis. Metformin, a classic anti-diabetes drug, seems to improve survival of pancreatic cancer patients in some studies. METHODS: Cell counting kit-8 assay was used to detect the BxPC-3 and MIAPaCa-2 cell viability after treatment with gemcitabine only or with different concentrations of metformin. The methylation state and expression level of miR-663 were detected by methylation analysis and RT-PCR. Dual-luciferase reporter gene analysis, Western blot and RT-PCR were used to confirm the target of miR-663. Moreover, xenograft experiment was also performed to validate the role of metformin in chemosensitivity in vivo. RESULTS: We found that metformin increased the chemosensitivity of pancreatic cancer cells to gemcitabine, and epithelial-mesenchymal transition (EMT) progress caused by gemcitabine was suppressed by metformin. We further explored the possible molecular mechanisms and it was demonstrated that CpG islands of miR-663 were hypomethylated and relative expression level of miR-663 was up-regulated after treatment of metformin. miR-663, an important cancer suppressor miRNA, was confirmed to increase the chemosensitivity of pancreatic cancer cells by reversing EMT directly targeted TGF-ß1. Moreover, we identified that metformin increased the chemosensitivity through up-regulating expression of miR-663. CONCLUSION: We demonstrated that metformin increased the chemosensitivity of pancreatic cancer cells to gemcitabine by reversing EMT through regulation DNA methylation of miR-663.

Comparative peptidome profiling reveals critical roles for peptides in the pathology of pancreatic cancer.

BACKGROUNDS/AIMS: Pancreatic cancer is a digestive system tumour disease with a notably poor prognosis and a 5-year survival rate of less than 10 %. In recent years, peptide drugs have shown great clinical value in antitumour applications. We aim to identify differentially expressed peptides by using peptidomics techniques to explore the mechanisms involved in the development and pathology of pancreatic cancer. METHODS: We performed peptidomic analysis of pancreatic cancer and paired paracancerous tissues by using ITRAQ labelling technology and conducted in-depth bioinformatics analysis and functional studies on differentially expressed peptides. RESULTS: A total of 2,881 peptides were identified, of which 133 were differentially expressed (116 were upregulated and 17 were downregulated). By using GO analysis, the differentially expressed peptides were found to be closely related to the tumour microenvironment and extracellular matrix. KEGG enrichment analysis revealed that precursor proteins were closely related to the T2DM and RAS signalling pathways. The endogenous peptide P1DG can significantly inhibit the proliferation, migration and invasion of pancreatic cancer cells. CONCLUSION: P1DG and its precursor GAPDH may be closely related to the proliferation, migration and invasion of pancreatic cancer. Peptidomics can aid in understanding the pathogenesis of pancreatic cancer more comprehensively.