Aspirin Use and Lung Cancer Risk: A Possible Relationship? Evidence from an Updated Meta-Analysis

Background and Purpose

Growing evidence has emerged and controversial results reported on possible relationship between aspirin use and lung cancer risk. We, therefore, conducted this updated and comprehensive meta-analysis to evaluate this issue, with focus on dose-risk and duration-risk relationships.

Methods

We searched electronic databases including PUBMED, EMBASE and Cochrane library to identify eligible studies. Relative risk (RR) and its 95% confidence interval (CI) were used for cohort studies, while odds ratio (OR) were employed for case-control studies. The random effects and fixed effects models were used for analyses.

Results

18 studies were identified including 19835 lung cancer cases, which were eligible for inclusion in the present meta-analysis. Pooled data from case-control studies showed a significant inverse association between regular aspirin use and lung cancer risk. But for cohort studies, insignificant association was detected with little evidence of heterogeneity (RR: 1.05, 95%CI: 0.95 – 1.16; I²: 10.3%, p value: 0.351). In case-control studies, standard aspirin use (>325mg) was related to lower lung cancer incidence, compared with low-dose aspirin use (75–100mg). A similar trend was observed in cohort studies. Besides, when analysis was restricted to long time regular aspirin use (>5 years), insignificant results were reported in both cohort and case-control studies. Finally, regular aspirin use might result in higher reduction of non-small cell lung cancer incidence among men.

Conclusions

Our findings do not support the protective effect of regular aspirin use on lung cancer risk. Long time aspirin use, sex, dose and type of lung cancer might alter the effect of aspirin use on lung cancer risk. More well-designed studies are needed to further clarify these associations.     

Differential Phosphorylation of Smad1 Integrates BMP and Neurotrophin Pathways through Erk/Dusp in Axon Development

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Stable Li Metal Anode Enabled by Space Confinement and Uniform Curvature through Lithiophilic Nanotube Arrays

The application of lithium (Li) metal anodes in rechargeable batteries is primarily restricted by Li dendrite growth on the metal's surface, which leads to shortened cycle life and safety concerns. Herein, well‐spaced nanotubes with ultrauniform surface curvature are introduced as a Li metal anode structure. The ultrauniform nanotubular surface generates uniform local electric fields that evenly attract Li‐ions to the surface, thereby inducing even current density distribution. Moreover, the well‐defined nanotube spacing offers Li diffusion pathways to the electroactive areas as well as the confined spaces to host deposited Li. These structural attributes create a unique electrodeposition manner; i.e., Li metal homogenously deposits on the nanotubular wall, causing each Li nanotube to grow in circumference without obvious sign of dendritic formation. Thus, the full‐cell battery with the spaced Li nanotubes exhibits a high specific capacity of 132 mA h g^?1 at 1 C and an excellent coulombic efficiency of ≈99.85% over 400 cycles.     

Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods

Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods.     

Auto-adaptive Alpha Allocation: A Strategy to Mitigate Risk on Study Assumptions

In some clinical development programs, there are potential biomarkers with promising but uncertain predictive effect, while the probability of success in the overall population cannot be readily dismissed. It is risky to focus only on the overall population, or just the biomarker subpopulation. In 2009, Chen and Beckman proposed a Bayesian decision framework to optimize the type I error rate (alpha) allocation in a Phase III clinical study with possible predictive subset effect. The utilization of internal data in this framework is of particular interest because it provides an opportunity to mitigate the potential risk of misspecified study assumptions using an auto-adaptive strategy. In this paper, we examine this auto-adaptive strategy in detail through extensive numerical case studies and provide guidance on the appropriate use of partial current trial (internal) data in this data-driven optimization framework. We show that internal data can be used to inform the alpha allocation to hypothesis testing in the overall population and the subgroup. The resulting adaptive testing strategy is robust with respect to the uncertainty in the predictive subgroup effect and biomarker prevalence.     

Glycogen synthase kinase-3beta regulates growth, calcium homeostasis, and diastolic function in the heart

Glycogen synthase kinase (GSK) 3beta is a negative regulator of stress-induced cardiomyocyte hypertrophy. It is not clear, however, if GSK-3beta plays any role in regulating normal cardiac growth and cardiac function. Herein we report that a transgenic mouse expressing wild type GSK-3beta in the heart has a dramatic impairment of normal post-natal cardiomyocyte growth as well as markedly abnormal cardiac contractile function. The most striking phenotype, however, is grossly impaired diastolic relaxation, which leads to increased filling pressures of the left ventricle and massive atrial enlargement. This is due to profoundly abnormal calcium handling, leading to an inability to normalize cytosolic [Ca2+] in diastole. The alterations in calcium handling are due at least in part to direct down-regulation of the sarcoplasmic reticulum calcium ATPase (SERCA2a) by GSK-3beta, acting at the level of the SERCA2 promoter. These studies identify GSK-3beta as a regulator of normal growth of the heart and are the first of which we are aware, to demonstrate regulation of expression of SERCA2a, a critical determinant of diastolic function, by a cytosolic signaling pathway, the activity of which is dynamically modulated. De-regulation of GSK-3beta leads to severe systolic and diastolic dysfunction and progressive heart failure. Because down-regulation of SERCA2a plays a central role in the diastolic and systolic dysfunction of patients with heart failure, these findings have potential implications for the therapy of this disorder.     

Metabolic alterations in yeast lacking copper-zinc superoxide dismutase

Yeast lacking copper-zinc superoxide dismutase (sod1?) have a number of oxygen-dependent defects, including auxotrophies for lysine and methionine and sensitivity to oxygen. Here we report additional defects in metabolic regulation. Under standard growth conditions with glucose as the carbon source, yeast undergo glucose repression in which mitochondrial respiration is deemphasized, energy is mainly derived from glycolysis, and ethanol is produced. When glucose is depleted, the diauxic shift is activated, in which mitochondrial respiration is reemphasized and stress resistance increases. We find that both of these programs are adversely affected by the lack of Sod1p. Key events in the diauxic shift do not occur and sod1? cells do not utilize ethanol and stop growing. The ability to shift to growth on ethanol is gradually lost as time in culture increases. In early stages of culture, sod1? cells consume more oxygen and have more mitochondrial mass than wild-type cells, indicating that glucose repression is not fully activated. These changes are at least partially dependent on the activity of the Hap2,3,4,5 complex, as indicated by CYC1-lacZ reporter assays. These changes may indicate a role for superoxide in metabolic signaling and regulation and/or a role for glucose derepression in defense against oxidative stress.     

Long noncoding RNA LINC01234 promoted cell proliferation and invasion via miR-1284/TRAF6 axis in colorectal cancer

Colorectal cancer is one of the most common and leading malignancies globally. Long noncoding RNAs (lncRNAs) function as potentially critical regulator in colorectal cancer. LINC01234, a novel lncRNA in tumor biology, regulates the progression of various tumors. However, the tumorigenic mechanism of LINC01234 in colorectal cancer is still unclear. This study was performed with the aim to prospectively investigate clinical significance, effect, and mechanism of lncRNA LINC01234 in colorectal cancer. First, we found that LINC01234, localized in the cytoplasm, was increased in both colorectal cancer cell lines and tissues. Subsequent functional assays suggested LINC01234 knockdown suppressed cell proliferation, migration, and invasion of colorectal cancer cells, while blocked cell cycle and induced cell apoptosis. Moreover, we identified that miR-1284 was target of LINC01234, we further demonstrated a negative correlation with LINC01234 in colorectal cancer tissues and cells. Furthermore, miR-1284 targeted and suppressed tumor necrosis factor receptor–associated factor 6 (TRAF6). Loss-of-function assay revealed that LINC01234 silencing suppressed colorectal cancer progression through inhibition of miR-1284. In vivo subcutaneous xenotransplanted tumor model indicated LINC01234 knockdown inhibited in vivo tumorigenic ability of colorectal cancer via downregulation of TRAF6. Collectively, this study clarified the biological significance of LINC01234/miR-1284/TRAF6 axis in colorectal cancer progression, providing insights into LINC01234 as novel potential therapeutic target for colorectal cancer therapeutic from bench to clinic.