Coexpression of PBX1 and EMP2 as Prognostic Biomarkers in Estrogen Receptor-Negative Breast Cancer via Data Mining

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers

The dark and humid environment of underground coal mines had a detrimental effect on workers’ skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.

The regulation of PBXs and their emerging role in cancer

Pre‐B‐cell leukaemia transcription factor (PBX) proteins are a subfamily of evolutionarily conserved, atypical homeodomain transcription factors that belong to the superfamily of three amino acid loop extension (TALE) homeodomain proteins. Members of the PBX family play crucial roles in regulating multiple pathophysiological processes, such as the development of organs, congenital cardiac defects and carcinogenesis. The dysregulation of PBXs has been shown to be closely associated with many diseases, particularly cancer. However, the detailed mechanisms of PBX dysregulation in cancer progression are still inconclusive. In this review, we summarize the recent advances in the structures, functions and regulatory mechanisms of PBXs, and discuss their underlying mechanisms in cancer progression. We also highlight the great potential of PBXs as biomarkers for the early diagnosis and prognostic evaluation of cancer as well as their therapeutic applications. The information reviewed here may expand researchers’ understanding of PBXs and could strengthen the clinical implication of PBXs in cancer treatment.

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype and lacks targeted treatment. It is diagnosed by the absence of immunohistochemical expression of several biomarkers, but this method still displays some interlaboratory variability. DNA methylome aberrations are common in BC, thereby methylation profiling could provide the identification of accurate TNBC diagnosis biomarkers. Here, we generated a signature of differentially methylated probes with class prediction ability between 5 non-neoplastic breast and 7 TNBC tissues (error rate = 0.083). The robustness of this signature was corroborated in larger cohorts of additional 58 non-neoplastic breast, 93 TNBC, and 150 BC samples from the Gene Expression Omnibus repository, where it yielded an error rate of 0.006. Furthermore, we validated by pyrosequencing the hypomethylation of three out of 34 selected probes (FLJ43663, PBX Homeobox 1 (PBX1), and RAS P21 protein activator 3 (RASA3) in 51 TNBC, even at early stages of the disease. Finally, we found significantly lower methylation levels of FLJ43663 in cell free-DNA from the plasma of six TNBC patients than in 15 healthy donors. In conclusion, we report a novel DNA methylation signature with potential predictive value for TNBC diagnosis.