Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.

The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.

[Preparation of PHF10 antibody and analysis of PHF10 expression gastric cancer tissues].

AIM: To prepare PHF10 antibody and check the expression of PHF10 protein in the tissues of gastric cancer and adjacent tissue. METHODS: His-tagged PHF10 was expressed in E.coli BL21. Rabbit PHF10 polyclonal antiserum was generated by injecting the purified recombinant His-tagged PHF10 inclusion body as the antigen, and further separated by affinity purification. To confirm the specificity of the PHF10 antibody, transiently expressed Flag-PHF10 fusion protein was analyzed by immunoblotting with anti-flag monoclonal antibody control. The produced antibody Was used to check the expression of PHF10 protein in gastric cancer and adjacent tissues by Western blot. RESULTS: Antibodies specifically binding to PHF10 could be obtained by immunization, and expression of PHF10 was significantly higher in gastric cancerous tissues comparing with adjacent normal tissues and GES-1 shows more PHF10 expression than gastric cancer cell lines with the generated antibody. CONCLUSION: The specific anti-PHF10 antibody is obtained and it could be used to detect the expression of PHF10 protein in gastric cancer cell lines and tissues, in which PHF10 is unregulated in gastric cancer.

A p38 mitogen-activated protein kinase-dependent mechanism of disinhibition in spinal synaptic transmission induced by tumor necrosis factor-alpha.

Tumor necrosis factor-α (TNFα) is a proinflammatory cytokine that contributes to inflammatory and neuropathic pain. The mechanism by which TNFα modulates synaptic transmission in mouse substantia gelatinosa was studied using whole-cell patch clamp and immunohistochemistry. TNFα was confirmed to significantly increase the frequency of spontaneous EPSCs (sEPSCs) in spinal neurons and to also produce a robust decrease in the frequency of spontaneous IPSCs (sIPSCs). The enhancement of excitatory synaptic transmission by TNFα is in fact observed to be dependent on the suppression of sIPSCs, or disinhibition, in that blockade of inhibitory synaptic transmission prevents the effect of TNFα on sEPSCs but not vice versa. TNFα-induced inhibition of sIPSCs was blocked by neutralizing antibodies to TNF receptor 1 (TNFR1) but not to TNFR2 and was abolished by the p38 mitogen-activated protein kinase inhibitor SB202190 [4-(4-fluorophenyl)-2-(4-hydroxyphenyl)-5-(4-pyridyl)1H-imidazole]. TNFα rapidly inhibited spontaneous action potentials in GABAergic neurons identified in transgenic mice expressing enhanced green fluorescent protein controlled by the GAD67 promoter. This inhibitory effect was also blocked by intracellular delivery of SB202190 to the targeted cells. The inhibition of spontaneous activity in GABAergic neurons by TNFα is shown as mediated by a reduction in the hyperpolarization-activated cation current (Ih). These results suggest a novel TNFα-TNFR1-p38 pathway in spinal GABAergic neurons that may contribute to the development of neuropathic and inflammatory pain by TNFα.