Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS and impact on esophageal cancer risk in Japanese people

ADH1B regulates tumor stemness by activating the cAMP/PKA/CREB1 signaling axis to inhibit recurrence and metastasis of lung adenocarcinoma

Lung cancer remains the leading cause of cancer-related mortality, with non-small cell lung cancer (NSCLC) accounting for approximately 85 % of cases. Despite advancements in diagnostics and therapies, tumor metastasis and drug-resistant recurrence present significant clinical challenges. This study evaluates the prognostic role of ADH1B in lung adenocarcinoma (LUAD) metastasis and recurrence. Analysis of tissue samples from 46 LUAD patients revealed that lower ADH1B expression correlates with increased metastasis and poorer overall survival. Kaplan-Meier survival analysis demonstrated that elevated ADH1B levels are significantly associated with longer overall survival and recurrence-free survival. In vitro experiments indicated that ADH1B overexpression inhibits proliferation, migration, and invasion in A549 and H1299 cell lines. Additionally, ADH1B expression was negatively correlated with tumor stemness markers, indicating its role in suppressing stem cell characteristics. Mechanistically, ADH1B activates the cAMP/PKA/CREB1 signaling pathway, enhancing SOX1 expression and inhibiting the ERK pathway, which contributes to reduced tumor stemness. In vivo studies confirmed that ADH1B overexpression decreases stem cell populations and tumor growth in xenograft models. Our findings suggest that ADH1B functions as a critical regulator of LUAD progression, with its low expression acting as a marker of poor prognosis while promoting metastasis and tumor stemness. This research identifies ADH1B as a potential therapeutic target, offering novel strategies to address the challenges of metastasis and recurrence in LUAD.

The Burden of Esophageal Cancer in Five East Asian Countries From 1990 to 2021 and Its Prediction Until 2036: An Analysis of the Global Burden of Diseases Study 2021

BACKGROUND

In 2022, esophageal cancer (EC) was the eleventh most frequently diagnosed cancer and the seventh leading cause of cancer-related deaths worldwide. Nonetheless, the prevalence and burden of EC in Asian countries have been little studied. This study investigated the prevalence, incidence, and burden of EC in five East Asian countries from 1990 to 2021.

METHODS

We retrieved data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 on the prevalence, incidence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life years (DALYs) associated with EC in five East Asian countries from 1990 to 2021. The epidemiological characteristics of EC were analyzed by joinpoint regression, age-period-cohort analysis, and decomposition analysis. The burden of EC until 2036 was estimated using autoregressive integrated moving average and Bayesian age-period-cohort models.

RESULTS

Among these East Asian countries, China had the highest incidence, prevalence, mortality rates, YLLs, YLDs, and DALYs in 1990 and 2021. China and Mongolia exhibited the highest age-standardized incidence rate, age-standardized mortality rate, age-standardized prevalence rate, age-standardized YLDs rate, age-standardized YLLs rate, and age-standardized DALYs rate from 1990 to 2036. The age group with the highest prevalence rate, incidence rate, mortality rate, YLDs rate, YLLs rate, and DALYs rate in these countries was ≥ 55 years. The prevalence and incidence rates in the five countries were influenced by aging and were higher than the global average.

CONCLUSIONS

The burden of EC in five East Asian countries was high over the past three decades, particularly among older adults. EC is a significant public health problem in these countries, particularly because of the large population base and aging demographics.

Personalized prediction of esophageal cancer risk based on virtually generated alcohol data

BACKGROUND

Esophageal cancer (EC) presents a significant public health challenge globally, particularly in regions with high alcohol consumption. Its etiology is multifactorial, involving both genetic predispositions and lifestyle factors.

METHODS

This study aimed to develop a personalized risk prediction model for EC by integrating genetic polymorphisms (rs671 and rs1229984) with virtually generated alcohol consumption data, utilizing advanced artificial intelligence and machine learning techniques. We analyzed data from 86,845 individuals, including 763 diagnosed EC patients, sourced from the Taiwan Biobank. Eight machine learning models were employed: Bayesian Network, Decision Tree, Ensemble, Gradient Boosting, Logistic Regression, LASSO, Random Forest, and Support Vector Machines (SVM). A unique aspect of our approach was the virtual generation of alcohol consumption data, allowing us to evaluate risk profiles under both consuming and non-consuming scenarios.

RESULTS

Our analysis revealed that individuals with the genotypes rs671 = AG and rs1229984 = CC exhibited the highest probabilities of developing EC, with values ranging from 0.2041 to 0.9181. Notably, abstaining from alcohol could decrease their risk by approximately 16.29-49.58%. The Ensemble model demonstrated exceptional performance, achieving an area under the curve (AUC) of 0.9577 and a sensitivity of 0.9211. This transition from consumption to abstinence indicated a potential risk reduction of nearly 50% for individuals with high-risk genotypes.

CONCLUSION

Overall, our findings highlight the importance of integrating virtually generated alcohol data for more precise personalized risk assessments for EC.

Leveraging genome-wide association studies to better understand the etiology of cancers

Genome-wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome-wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low-penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype-phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome-wide association studies. Furthermore, aggregating genome-wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypes and helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS-derived downstream analyses, and demonstrate their applications to GWAS of cancers.

Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach

Objective

Physical inactivity increases the risk of mortality and chronic morbidity. Therefore, it is crucial to establish strategies to encourage individuals to increase their physical activity and develop exercise habits. The objective of this study was to explore factors associated with acquiring exercise habits using machine learning algorithms.

Methods

The analyzed dataset was obtained from the Specific Health Guidance for metabolic syndrome systematically implemented by the Japanese Ministry of Health, Labor, and Welfare. We selected target individuals for health guidance without exercise habits in 2017 and assessed whether the participants acquired exercise habits through health guidance in 2018. We applied ten machine learning algorithms to build prediction models for acquiring exercise habits.

Results

This study included 16,471 middle-aged Japanese workers (age, 49.5 ± 6.2 years). Among the machine learning algorithms, the Boosted Generalized Linear Model was the best for predicting the acquisition of exercise habits based on the receiver operating characteristic curve on the test set (ROC-AUCtest, 0.68). According to the analyses, the following factors were associated with the acquisition of exercise habits: being in the maintenance or action stage of changing exercise and eating behaviors based on the transtheoretical model; regular physical activity or walking; normal high-density lipoprotein cholesterol; and high alcohol consumption.

Conclusions

Our findings can be used to establish an efficient strategy for encouraging individuals to acquire exercise habits through Specific Health Guidance or other health guidance. However, the lower ROC-AUCtest suggests that additional variables are necessary to enhance the prediction model.

Corynoxine exerts the anti-tumor effect on esophageal squamous cell carcinoma principally via the EZH2-DUSP5-ERK1/2-mediated cell growth inhibition

BACKGROUND

Esophageal cancer is one of the most prevalent malignant tumors and the sixth largest cause of tumor-associated death worldwide. Squamous cell carcinoma (ESCC) accounts for 85 % of all esophageal cancer cases. ESCC treatment remains to be significantly difficult. Corynoxine (Cory) is a tetracyclic hydroxyindole alkaloid isolated from Uncaria macrophylla. It is unclear whether Cory has an anti-tumor effect on ESCC.

PURPOSE

To determine the anti-tumor activity of Cory and the associated mechanisms in ESCC.

STUDY DESIGN

Cory's effects on proliferation, apoptosis, migration, and invasion, as well as the underlying molecular causes were assessed using two ESCC cell lines, KYSE150 and TE-1. A xenograft mouse model was then applied to evaluate the anti-tumor activity of Cory in vivo.

METHODS

Western blot, assays including CCK-8, colony formation, EdU staining, TUNEL staining, cell scratch and Transwell, and a xenograft mouse model were used in this study.

RESULTS

Cory suppressed cell growth, provoked cell apoptosis, and hindered cell migration and invasion of ESCC cells. DUSP5 knockdown reduced the Cory-induced cell death and restored cell migration and invasion through ERK1/2 activation. Further analyses showed that Cory promoted DUSP5 expression via inhibiting EZH2 expression, leading to inactivation of ERK1/2 signaling and the subsequent cell growth inhibition of ESCC. In vivo experiments disclosed that Cory suppressed tumor growth of ESCC through upregulating DUSP5 expression.

CONCLUSIONS

Cory plays an anti-tumor role in ESCC by regulating EZH2-DUSP5-ERK1/2 signaling pathway. Cory may be promising to be a novel therapy for treating ESCC.

Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders

Substance use disorders (SUDs) are highly prevalent and associated with excess morbidity, mortality, and economic costs. Thus, there is considerable interest in the early identification of individuals who may be more susceptible to developing SUDs and in improving personalized treatment decisions for those who have SUDs. SUDs are known to be influenced by both genetic and environmental factors. Polygenic scores (PGSs) provide a single measure of genetic liability that could be used as a biomarker in predicting disease development, progression, and treatment response. Although PGSs are rapidly being integrated into clinical practice, there is little information to guide clinicians in their responsible use and interpretation. In this Review, we discuss the potential benefits and pitfalls of the use of PGSs in the clinical care of SUDs, highlighting current research. We also provide suggestions for important considerations prior to implementing the clinical use of PGSs and recommend future directions for research.