In contrast to SUVmax, the MTV and TLG may be significant prognostic markers for PFS in DLBCL.

Photoionization mass spectrometry (PI-MS) has become a versatile tool in the real-time analysis of volatile organic compounds (VOCs) from the atmosphere or exhaled breath. However, some key species, e.g., acetonitrile,...

Background The aim of this study was to confirm the role of Brachyury in breast cancer and to verify whether four types of machine learning models can use Brachyury expression to predict the survival of patients.</p>  Methods We conducted a retrospective review of the medical records to obtain patient information, and made the patient's paraffin tissue into tissue chips for staining analysis. We selected  303 patients for research and implemented four machine learning algorithms, including multivariate logistic regression model, decision tree, artificial neural network and random forest, and compared the results of these models with each other. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results.</p>  Results The chi-square test results of relevant data suggested that the expression of Brachyury protein in cancer tissues was significantly higher than that in paracancerous tissues (p=0.0335); breast cancer patients with high Brachyury expression had a worse overall survival (OS) compared with patients with low Brachyury expression. We also found that Brachyury expression was associated with ER expression (p=0.0489). Subsequently, we used four machine learning models to verify the relationship between Brachyury expression and the survival of breast cancer patients. The results showed that the decision tree model had the best performance (AUC=0.781).</p>  Conclusions Brachyury is highly expressed in breast cancer and indicates that patients had a poor prognosis. Compared with conventional statistical methods, decision tree model shows superior performance in predicting the survival status of breast cancer patients.

Background SMC1A (Structural maintenance of chromosomes 1) is overexpressed in various cancers and acts as an oncogene which has been implicated in critical biological functions (cell-cycle checkpoints regulation, cell division, and DNA repair). However, the mechanism and role of SMC1A in breast cancer are poorly understood. Methods TCGA database was utilized to explore the expression of SMC1A and the relationship between SMC1A and FOXM1 and STMN1. Subsequently, short hairpin RNA (shRNA) targeting SMC1A was used to examined the biological functions of it in MDA-MB-231 and MDA-MB-468 cells. Finally, subcutaneous xenograft model to verify the roles of SMC1A in vivo. Results In the present study, we demonstrated that SMC1A was significantly increased in breast cancer (BC) via TCGA database. Then loss and gain of function studies revealed that SMC1A contributed to BC cell survival, apoptosis, and invasion. Interestingly, we found that SMC1A triggered the AKT/FOXM1 cascade, which promoted BC cell proliferation. Furthermore, overexpression of FOXM1 abolished the inhibition of cell growth induced by SMC1A silencing in vitro. Clinically, the expression of SMC1A in BC tumor tissues is positively correlated with the expression of FOXM1. Conclusion Taken together, our findings not only enhanced our understanding of molecular mechanisms of SMC1A in BC, but also might provide a novel target for the development of therapeutic strategies.