Tamoxifen is a critical drug for the treatment of oestrogen receptor (ER)-positive breast cancer (BC), which represents the majority of BC subtypes. However, many BC tumours that initially respond eventually develop acquired Tamoxifen resistance. Bioinformatics analysis was conducted on genes affected by Tamoxifen and upregulated in Tamoxifen-resistant cells to identify the biological processes associated with Tamoxifen resistance. Metabolomics analysis was conducted to identify the metabolites that were altered in BC with tamoxifen resistance. Resistance to Tamoxifen was evaluated by cell viability, proliferation, invasion, and colony formation in vitro, and by tumour growth in vivo. Metabolomic profiling and the detection of relevant enzymes and metabolites corroborated the metabolic reprogramming towards glycolysis in tamoxifen - resistant BC. The produced lactic acid induced the lactylation of ZMIZ1. This post-translational modification at K843 (but not K537) increased protein stability by suppressing SUMOylation and ubiquitination. The elevated total level of ZMIZ1 increased the enrichment of ZMIZ1 binding to Nanog, resulting in increased transcriptional activity of Nanog, including in OCT4 and NPC2 genes. Therefore, it leads to increased stemness and cholesterol accumulation in Tamoxifen-resistant BC. Knockdown of ZMIZ1 impaired Tamoxifen resistance, but this effect was reversed by Nanog overexpression. In summary, this study identified an important mechanism underlying Tamoxifen resistance and revealed a potential association of glucose glycolysis with cholesterol metabolism through the ZMIZ1/Nanog/NPC2 axis.

The normative value of inflammatory cells in the nasal perfusate of Chinese adults: a pilot study

Whether smokers respond to anti-cancer drugs differently than non-smokers remains controversial. The objective of this study is to explore whether the better response of the smokers is specific to therapy of anti-PD-1/PD-L1, anti-checkpoint inhibitor, individual drugs on the cell surface, or lung cancer. Our results showed that among all non-small cell lung cancer (NSCLC) patients, when the data from anti-PD-1/PD-L1, anti-CTLA-4, and anti-MUC1 drugs are combined, the mean hazard ratios (HR) of smokers and non-smokers were 0.751 and 1.016, respectively. A meta-analysis with a fixed effect (FE) model indicated that the smokers have an HR value of 0.023 lower than that of the non-smokers. A stratified subgroup meta-analysis indicated that when treated with anti-CTLA-4 drugs, smokers had reduced HR values of 0.152 and 0.165 on average and FE model meta-analysis, respectively. When treated with an anti-MUC1 drug, smokers had reduced HR values of 1.563 and 0.645, on average and FE model meta-analysis, respectively. When treated with a combination of nivolumab and ipilimumab drugs, smokers had, on average, reduced HR and FE model meta-analysis values (0.257 and 0.141), respectively. Smoking is a clinical response predictor for anti-PD/PD-L1 monotherapy or first-line treatment in lung, urothelial carcinoma, and head and neck cancer. Smokers treated with other drugs have shown worse responses in comparison to non-smokers. These data suggest that, along with the progress in the development of new drugs for cancer, drugs acting on specific genotypes of smokers likely will arise.

Background: Currently, the 2019-nCoV has spread to most countries of the world. Understanding the environmental factors that affect the spread of the disease COVID-19 infection is critical to stop the spread of the disease. The purpose of this study is to investigate whether population density is associated with the infection rate of the COVID-19. Methods: We collected data from official webpages of cities in China and in the US. The data were uploaded on Excel spreadsheets for statistical analyses. We calculated the morbidity and population density of cities and regions in these two countries. We then examined the relationship between the morbidity and other factors. Results: Our analysis indicated that the population density in cities in Hubei province where the COVID-19 was severe was associated with a higher percentage of morbidity, with an r value of 0.62. Similarly, in the US, the density of 51 states and territories is also associated with morbidity from COVID-19 with an r value of 0.55. In contrast, as a control group, there is no association between the morbidity and population density in 33 other regions of China, where the COVID-19 epidemic is well under control. Interestingly, our study also indicated that these associations were not influenced by the first case of COVID-19. The rate of morbidity and the number of days from the first case in the US has no association, with an r value of -0.1288. Conclusions: Population density is positively associated with the percentage of patients with COVID-19 infection in the population. Our data support the importance of such as social distancing and travel restriction in the prevention of COVID-19 spread.