A class of environmental and endogenous toxins induces BRCA2 haploinsufficiency and genome instability

https://www.journal-of-hepatology.eu/article/S0168-8278(19)30390-3/pdf

ALDH2 deficiency promotes alcohol-associated liver cancer by activating oncogenic pathways via oxidized DNA-enriched extracellular vesicles

https://escholarship.org/content/qt6wf4z22x/qt6wf4z22x.pdf?t=pwgg6r

Alcoholic liver disease: A current molecular and clinical perspective.

The role of ALDH2 in tumorigenesis and tumor progression: Targeting ALDH2 as a potential cancer treatment.

Aldehyde dehydrogenases (ALDHs) defend intracellular homeostasis by catalyzing the conversion of toxic aldehydes into non-toxic carboxylic acids, which is of particular importance to the self-renewal of stem cells and cancer stem cells. The widely used ALDEFLUOR assay was initially designed to indicate the activity of ALDH1A1 in leukemia and has been demonstrated to detect the enzyme activity of several other ALDH isoforms in various cancer types in recent years. However, it is still elusive which isoforms, among the 19 ALDH isoforms in human genome, are the potential contributors in catalyzing ALDEFLUOR assay in different cancers. In the current study, we performed a screening via overexpressing each ALDH isoform to assess their ability of catalyzing ALDEFLUOR assay. Our results demonstrate that nine isoforms are active in ALDEFLUOR assay, whose overexpression significantly increases ALDH-positive (ALDH+) population. Further analysis of the expression of these active isoforms in various cancers reveals cancer-type specific expression patterns, suggesting that different cancer types may exhibit ALDEFLUOR activity through expression of specific active ALDH isoforms. This study strongly indicates that a detailed elucidation of the functions for each active ALDH isoform in CSCs is necessary and important for a profound understanding of the underlying mechanisms of ALDH-associated stemness.

/pdf/identification-of-cancer-type-specific-expression-patterns-1610fwt3gy.pdf

Identification of cancer-type specific expression patterns for active aldehyde dehydrogenase (ALDH) isoforms in ALDEFLUOR assay.

Mitochondrial aldehyde dehydrogenase 2 (ALDH2) in the liver removes toxic aldehydes including acetaldehyde, an intermediate of ethanol metabolism. Nearly 40% of East Asians inherit an inactive ALDH2*2 variant, which has a lysine-for-glutamate substitution at position 487 (E487K), and show a characteristic alcohol flush reaction after drinking and a higher risk for gastrointestinal cancers. Here we report the characterization of knockin mice in which the ALDH2(E487K) mutation is inserted into the endogenous murine Aldh2 locus. These mutants recapitulate essentially all human phenotypes including impaired clearance of acetaldehyde, increased sensitivity to acute or chronic alcohol-induced toxicity, and reduced ALDH2 expression due to a dominant-negative effect of the mutation. When treated with a chemical carcinogen, these mutants exhibit increased DNA damage response in hepatocytes, pronounced liver injury, and accelerated development of hepatocellular carcinoma (HCC). Importantly, ALDH2 protein levels are also significantly lower in patient HCC than in peritumor or normal liver tissues. Our results reveal that ALDH2 functions as a tumor suppressor by maintaining genomic stability in the liver, and the common human ALDH2 variant would present a significant risk factor for hepatocarcinogenesis. Our study suggests that the ALDH2*2 allele-alcohol interaction may be an even greater human public health hazard than previously appreciated.

https://www.pnas.org/content/pnas/112/29/9088.full.pdf

ALDH2(E487K) mutation increases protein turnover and promotes murine hepatocarcinogenesis

Background: A machine learning model was developed to estimate the in-hospital mortality risk after congenital heart disease (CHD) surgery in pediatric patient. Methods: Patients with CHD who underwent surgery were included in the study. A Extreme Gradient Boosting (XGBoost) model was constructed based onsurgical risk stratification and preoperative variables to predict the risk of in-hospital mortality. We compared the predictive value of the XGBoost model with Risk Adjustment in Congenital Heart Surgery-1 (RACHS-1) and Society of Thoracic Surgery-European Association for Cardiothoracic Surgery (STS-EACTS) categories. Results: A total of 24,685 patients underwent CHD surgery and 595 (2.4%) died in hospital. The area under curve (AUC) of the STS-EACTS and RACHS-1 risk stratification scores were 0.748 [95% Confidence Interval (CI): 0.707–0.789, p &lt; 0.001] and 0.677 (95% CI: 0.627–0.728, p &lt; 0.001), respectively. Our XGBoost model yielded the best AUC (0.887, 95% CI: 0.866–0.907, p &lt; 0.001), and sensitivity and specificity were 0.785 and 0.824, respectively. The top 10 variables that contribute most to the predictive performance of the machine learning model were saturation of pulse oxygen categories, risk categories, age, preoperative mechanical ventilation, atrial shunt, pulmonary insufficiency, ventricular shunt, left atrial dimension, a history of cardiac surgery, numbers of defects. Conclusions: The XGBoost model was more accurate than RACHS-1 and STS-EACTS in predicting in-hospital mortality after CHD surgery in China. 

/pdf/machine-learning-model-for-predicting-risk-of-in-hospital-2eg7b8zn.pdf

Machine Learning Model for Predicting Risk of In-Hospital Mortality after Surgery in Congenital Heart Disease Patients

BACKGROUND
In the present study, we aimed to identify risk factors of poor prognosis for patients with acute coronary syndrome in the emergency department.


METHODS
The study included 2667 patients, who were admitted to the Emergency Department of Chest Pain Center, Fujian Provincial Hospital, due to chest pain from January 1, 2017 to March 31, 2020. Logistic regression was used to identify factors of poor prognosis for patients with ACS in the ED. Receiver operating characteristic (ROC) curve was plotted to assess the performance of the multivariate logistic regression model. Subgroup analysis was used to analyze the difference of SBP in ACS patients with different characteristics.


RESULTS
The final analysis included 2667 patients, of whom 2,057 patients (77.8%) had poor prognosis. STEMI (compared with UA) (OR=20.139; 95% CI:12.448-32.581; P < 0.001), NSTEMI (compared with UA) (OR=7.430; 95% CI:5.159-10.700; P < 0.001), respiratory rate ≥20 bpm (compared with <20 bpm) (OR=1.334; 95% CI: 1.060-1.679; P = 0.014), and use of antiplatelets (OR=1.557; 95% CI:1.181-2.053; P = 0.002) was associated with increased likelihood of poor prognosis for ACS patients in ED. SBP ≥140 mmHg (compared with<140mmHg) (OR=0.574; 95% CI: 0.477-0.690; P < 0.001) was associated with decreased likelihood of poor prognosis for ACS patients in the ED. The area under curve (AUC) of the predictive efficacy of logistic regression model was 0.825 (95% CI: 0.795-0.833, P < 0.001).


CONCLUSION
This study found that STEMI, NSTEMI, respiratory rate ≥20 bpm, and use of antiplatelets were associated with increased likelihood of poor prognosis for ACS patients in the ED. It also found that SBP≥140 was associated with decreased likelihood of poor prognosis. Our study may be useful for doctors to make clinical decisions for ACS patients.

/pdf/risk-factors-for-poor-prognosis-in-acute-coronary-syndrome-1l3oqh6v.pdf

Risk Factors for Poor Prognosis in Acute Coronary Syndrome Admitted in the Emergency Department: A Retrospective Cohort Study.

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ALDH2(E487K) mutation increases protein turnover and promotes murine hepatocarcinogenesis

Machine Learning Model for Predicting Risk of In-Hospital Mortality after Surgery in Congenital Heart Disease Patients

Risk Factors for Poor Prognosis in Acute Coronary Syndrome Admitted in the Emergency Department: A Retrospective Cohort Study.