Small, noncoding RNA molecules, called microRNAs (miRNAs), are thought to function as either tumor suppressors or oncogenes. Common single-nucleotide polymorphisms (SNPs) in miRNAs may change their property through altering miRNA expression and/or maturation, and thus they may have an effect on thousands of target mRNAs, resulting in diverse functional consequences. However, it remains largely unknown whether miRNA SNPs may alter cancer susceptibility. We evaluated the associations of selected four SNPs (rs2910164, rs2292832, rs11614913, and rs3746444) in pre-miRNAs (hsa-mir-146a, hsa-mir-149, hsa-mir-196a2, and hsa-mir-499) with breast cancer risk in a case-control study of 1,009 breast cancer cases and 1,093 cancer-free controls in a population of Chinese women and we found that hsa-mir-196a2 rs11614913:T>C and hsa-mir-499 rs3746444:A>G variant genotypes were associated with significantly increased risks of breast cancer (odds ratio [OR], 1.23; 95% confidence interval [CI], 1.02-1.48 for rs11614913:T>C; and OR, 1.25; 95% CI, 1.02-1.51 for rs3746444:A>G in a dominant genetic model) in a dose-effect manner (P for trend was 0.010 and 0.037, respectively). These findings suggest, for the first time, that common SNPs in miRNAs may contribute to breast cancer susceptibility. Further functional characterization of miRNA SNPs and their influences on target mRNAs may provide underlying mechanisms for the observed associations and disease etiology.

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Background With evidence of sustained transmission in more than 190 countries, coronavirus disease 2019 (COVID-19) has been declared a global pandemic. Data are urgently needed about risk factors associated with clinical outcomes. Methods A retrospective review of 323 hospitalized patients with COVID-19 in Wuhan was conducted. Patients were classified into three disease severity groups (non-severe, severe, and critical), based on initial clinical presentation. Clinical outcomes were designated as favorable and unfavorable, based on disease progression and response to treatments. Logistic regression models were performed to identify risk factors associated with clinical outcomes, and log-rank test was conducted for the association with clinical progression. Results Current standard treatments did not show significant improvement in patient outcomes. By univariate logistic regression analysis, 27 risk factors were significantly associated with clinical outcomes. Multivariate regression indicated age over 65 years (p<0.001), smoking (p=0.001), critical disease status (p=0.002), diabetes (p=0.025), high hypersensitive troponin I (>0.04 pg/mL, p=0.02), leukocytosis (>10 x 109/L, p<0.001) and neutrophilia (>75 x 109/L, p<0.001) predicted unfavorable clinical outcomes. By contrast, the administration of hypnotics was significantly associated with favorable outcomes (p<0.001), which was confirmed by survival analysis. Conclusions Hypnotics may be an effective ancillary treatment for COVID-19. We also found novel risk factors, such as higher hypersensitive troponin I, predicted poor clinical outcomes. Overall, our study provides useful data to guide early clinical decision making to reduce mortality and improve clinical outcomes of COVID-19.