Hypermethylation in the promoter region inhibits AJAP1 expression and activates the JAK/STAT pathway to promote prostate cancer cell migration and stem cell sphere formation.

BACKGROUND: AJAP1 is down-regulated in multiple cancer types and plays a suppressive role in cancer progression. However, its molecular regulatory mechanism in prostate cancer has not been reported. METHODS: Bioinformatics methods were employed to analyze AJAP1 expression in prostate cancer tissues and its association with TNM staging. MSP and qRT-PCR were used to quantify promoter methylation and AJAP1 expression after 5-aza-20-deoxycytidine (5-AzaC) treatment. Scratch healing assay and Transwell method were adopted to analyze the effects of aberrant AJAP1 expression, 5-AzaC and AG490 on cell migration and invasion. The levels of AJAP1 protein, EMT-related and JAK/STAT pathway-related proteins were determined by Western blot. The effects of AJAP1 aberrant expression and AG490 treatment on the sphere forming ability of prostate cancer cells were analyzed by sphere formation assay. RESULTS: This study confirmed the significant down-regulation of AJAP1 expression in prostate cancer tissues and cells, and its negative correlation with TNM staging. 5-AzaC treatment led to a significant reduction of AJAP1 methylation level and a significant upregulation of AJAP1 expression, indicating that the methylation level of AJAP1 promoter may affect the expression of AJAP1. Cell function experiments found that overexpression or decreased methylation of AJAP1 inhibited epithelial mesenchymal transition (EMT), migration, and invasion, while silencing or increased methylation of AJAP1 had the opposite functions. JAK2/STAT3 pathway inhibiting assay found that inhibition of JAK2/STAT3 pathway significantly reduced EMT, cell migration, and stem cell sphere formation in prostate cancer. SIGNIFICANCE: Therefore, investigating the influence of aberrant AJAP1 expression on functions of prostate cancer cells is conducive to our in-depth understanding of the mechanism of prostate cancer genesis and development.

[Research progress in lung parenchyma segmentation based on computed tomography].

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.