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Introduction: Metastasis is a leading cause of cancer-related-deaths worldwide. Recently, cancer stem cells (CSCs) have been believed to be responsible for tumor initiation and metastasis, but till now, difference of cellular features and behaviors between CSCs from tumor tissues (TCSCs) and circulation (CCSCs) remains largely unknown, which hinders the progression of targeted therapies for metastasis., Methods and Results: Here, we provide the features of circulating gastric cancer stem cells (CGCSCs) isolated from human gastric adenocarcinoma. The CGCSCs and TGCSCs were culture in a same serum free stem cell culture medium, however the morphology are different with each other. EMT-associated markers were measured by Immunofluorescence, Western Blotting, and RT-PCR methods, and the results indicated that the CGCSCs and TGCSCs carry different epithelial-mesenchymal features. And then, proliferation and apoptosis assays revealed that the CGCSCs exhibited characteristics of higher proliferation and resistance to apoptosis in vitro. Soft agar assay and nude mice tumorigenicity assay displayed strong tumorigenicity of CGCSCs. Finally, Matrigel invasion assays and in vivo experimental metastasis assay were also performed, which demonstrated that CGCSCs carry high invasive and metastatic capabilities than TGCSCs., Conclusions: As expected, the CGCSCs indeed showed extremely invasive and metastatic properties. They also exhibited distinctive mesenchymal phenotypes, high self-renewal, proliferative capabilities, tumor induction and low apoptosis. Interestingly, CGCSCs show small cell-size than TGCSCs (tissue gastric cancer stem cells). The findings might help us to understand the biological characteristic of CGCSCs deeply, and give light to strategies for cancer therapies.

To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. [ABSTRACT FROM AUTHOR]