Membrane Progesterone Receptor Alpha as a Potential Prognostic Biomarker for Breast Cancer Survival: A Retrospective Study

Classically, the actions of progesterone (P4) are attributed to the binding of nuclear progesterone receptor (PR) and subsequent activation of its downstream target genes. These mechanisms, however, are not applicable to PR– or basal phenotype breast cancer (BPBC) due to lack of PR in these cancers. Recently, the function of membrane progesterone receptor alpha (mPRα) in human BPBC cell lines was studied in our lab. We proposed that the signaling cascades of P4→mPRα pathway may play an essential role in controlling cell proliferation and epithelial mesenchymal transition (EMT) of breast cancer. Using human breast cancer tissue microarrays, we found in this study that the average intensity of mPRα expression, but not percentage of breast cancer with high level of mPRα expression (mPRα-HiEx), was significantly lower in the TNM stage 4 patients compared to those with TNM 1–3 patients; and both average intensities of mPRα expression and mPRα-HiEx rates were significantly higher in cancers negative for ER, as compared with those cancers with ER+. However, after adjusting for age at diagnosis and/or TNM stage, only average intensities of mPRα expression were associated with ER status. In addition, we found that the rates of mPRα-HiEx were significantly higher in cancers with epithelial growth factor receptor–1 (EGFR+) and high level of Ki67 expression, indicating positive correlation between mPRα over expression and EGFR or Ki67. Further analysis indicated that both mPRα-HiEx rate and average intensity of mPRα expression were significantly higher in HER2+ subtype cancers (i.e. HER2+ER–PR–) as compared to ER+ subtype cancers. These data support our hypothesis that P4 modulates the activities of the PI3K and cell proliferation pathways through the caveolar membrane bound growth factor receptors such as mPRα and growth factor receptors. Future large longitudinal studies with larger sample size and survival outcomes are necessary to confirm our findings

Somatic SF3B1 hotspot mutation in prolactinomas.

The genetic basis and corresponding clinical relevance of prolactinomas remain poorly understood. Here, we perform whole genome sequencing (WGS) on 21 patients with prolactinomas to detect somatic mutations and then validate the mutations with digital polymerase chain reaction (PCR) analysis of tissue samples from 227 prolactinomas. We identify the same hotspot somatic mutation in splicing factor 3 subunit B1 (SF3B1R625H) in 19.8% of prolactinomas. These patients with mutant prolactinomas display higher prolactin (PRL) levels (p = 0.02) and shorter progression-free survival (PFS) (p = 0.02) compared to patients without the mutation. Moreover, we identify that the SF3B1R625H mutation causes aberrant splicing of estrogen related receptor gamma (ESRRG), which results in stronger binding of pituitary-specific positive transcription factor 1 (Pit-1), leading to excessive PRL secretion. Thus our study validates an important mutation and elucidates a potential mechanism underlying the pathogenesis of prolactinomas that may lead to the development of targeted therapeutics

Insight into 2019 novel coronavirus — An updated interim review and lessons from SARS-CoV and MERS-CoV

BACKGROUND: The rapid spread of the coronavirus disease 2019 (COVID-19), caused by a zoonotic beta-coronavirus entitled 2019 novel coronavirus (2019-nCoV), has become a global threat. Awareness of the biological features of 2019-nCoV should be updated in time and needs to be comprehensively summarized to help optimize control measures and make therapeutic decisions. METHODS: Based on recently published literature, official documents and selected up-to-date preprint studies, we reviewed the virology and origin, epidemiology, clinical manifestations, pathology and treatment of 2019-nCoV infection, in comparison with severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) infection. RESULTS: The genome of 2019-nCoV partially resembled SARS-CoV and MERS-CoV, and indicated a bat origin. The COVID-19 generally had a high reproductive number, a long incubation period, a short serial interval and a low case fatality rate (much higher in patients with comorbidities) than SARS and MERS. Clinical presentation and pathology of COVID-19 greatly resembled SARS and MERS, with less upper respiratory and gastrointestinal symptoms, and more exudative lesions in post-mortems. Potential treatments included remdesivir, chloroquine, tocilizumab, convalescent plasma and vaccine immunization (when possible). CONCLUSION: The initial experience from the current pandemic and lessons from the previous two pandemics can help improve future preparedness plans and combat disease progression

Progesterone inhibits the migration and invasion of A549 lung cancer cells through membrane progesterone receptor α-mediated mechanisms

Lung cancer is the leading cause of cancer morbidity and mortality in the world. The incidence of lung cancer, particularly lung adenocarcinoma, is increasing in women compared to men. The role of sex hormones in the development of lung cancer has attracted substantial interest, but remains largely unknown. In this study, we demonstrated that membrane progesterone receptor α (mPRα) was expressed in a lung adenocarcinoma cell line, A549, and was located on the cell membrane. In additional experiments, we found that mPRα functioned as an essential mediator for progesterone (P4)-induced inhibitory effects on cell migration and invasion of A549 cells. Furthermore, PP1 (an Src pathway inhibitor), when co-incubated with P4, synchronously enhanced the inhibitory effects of P4 on cell migration and invasion. To explore the mechanisms of inhibition, we found that P4 and PP1 induced a cascade of molecular signaling events, such as dephosphorylation of focal adhesion kinase (FAK) and downregulation of matrix metalloproteinase 9 (MMP-9). Our study provides a mechanistic view on the effects of P4 through mPRα→Src/FAK relevant pathways in human lung adenocarcinoma cells and may aid in the development of novel therapeutic tools for the treatment of lung cancer

Green Transportation and Logistics Performance: An Improved Composite Index

This article constructs an environmental logistics performance index (ELPI) for assessing the overall performance in green transportation and logistics practices of 112 countries. The index is measured by logistics performance index (LPI), CO2 emissions and oil consumption from the transport sector, using a range-adjusted measure (RAM) of the data envelopment analysis (DEA). ELPI effectively reflects the tradeoff between logistics efficiency and environmental protection in transportation. This article analyzes the impact of income and region on ELPI scores and discusses those countries’ reduction potential in oil consumption intensity and carbon intensity. The main finding of the research work is that ELPI is strongly correlated with LPI, and countries with high performance in LPI generally perform well in ELPI. Similar to the characteristics of LPI, ELPI is also closely related to income and region. During our study period, high income countries performed best, while Sub-Saharan Africa countries performed worst. However, some exceptions such as Venezuela, RB and Benin, indicate that the level of development of logistics performance and green transportation can outperform or lag behind their income or region group peers

Embedding differential privacy in decision tree algorithm with different depths

Differential privacy (DP) has become one of the most important solutions for privacy protection in recent years. Previous studies have shown that prediction accuracy usually increases as more data mining (DM) logic is considered in the DP implementation. However, although one-step DM computation for decision tree (DT) model has been investigated, existing research has not studied the scenarios when the DP is embedded in two-step DM computation, three-step DM computation until the whole model DM computation. It is very challenging to embed DP in more than two steps of DM computation since the solution space exponentially increases with the increase of computational complexity. In this work, we propose algorithms by making use of Markov Chain Monte Carlo (MCMC) method, which can efficiently search a computationally infeasible space to embed DP into DT generation algorithm. We compare the performance when embedding DP in DT with different depths, i.e., one-step DM computation (previous work), two-step, three-step and the whole model. We find that the deep combination of DP and DT does help to increase the prediction accuracy. However, when the privacy budget is very large (e.g., ϵ = 10), this may overwhelm the complexity of DT model, and the increasing trend is not obvious. We also find that the prediction accuracy decreases with the increase of model complexity