Impact of AADAC gene expression on prognosis in patients with Borrmann type III advanced gastric cancer

Arylacetamide deacetylase regulates hepatic iron homeostasis to protect against carbon tetrachloride-induced ferroptosis

Arylacetamide deacetylase (AADAC) catalyzes the hydrolysis of small molecules containing ester and amide bonds. Recently, it has been reported that AADAC can suppress reactive oxygen species production in cancer cells. This study aimed to elucidate the possibility that AADAC protects against drug-induced liver injury accompanied by oxidative stress and to explore its molecular mechanisms. Intraperitoneal administration of carbon tetrachloride induced significantly more severe liver injury in Aadac knockout (KO) mice (plasma alanine aminotransferase level: 19,381 ± 10,578 U/L) than in wild-type (WT) mice (7219 ± 4729 U/L). More severe liver injury in Aadac KO mice was accompanied by higher hepatic malondialdehyde and antioxidant gene mRNA levels than those in WT mice. The increase in plasma alanine aminotransferase levels in Aadac KO mice was substantially suppressed by pretreatment with the ferroptosis inhibitors deferoxamine or ferrostatin-1, suggesting that Aadac deficiency increases susceptibility to ferroptosis. Immunoprecipitation followed by proteomic analysis revealed that AADAC interacts with ceruloplasmin (CP), which oxidizes ferrous iron to ferric iron. Hepatic CP activity was significantly lower in Aadac KO mice than that in WT mice, resulting in elevated hepatic ferrous iron levels in Aadac KO mice. Overexpression of human AADAC in Huh-7 cells significantly attenuated carbon tetrachloride-induced cytotoxicity by suppressing ferrous iron accumulation, suggesting that AADAC interacts with CP to suppress hepatic ferrous iron accumulation. The hepatoprotective role of Aadac in ferroptosis was also observed in mice with acetaminophen-induced liver injury. This study demonstrates a novel function of AADAC in protecting against ferroptosis induced by hepatotoxicants, carbon tetrachloride and acetaminophen.

Role of carboxylesterase and arylacetamide deacetylase in drug metabolism, physiology, and pathology

Carboxylesterases (CES1 and CES2) and arylacetamide deacetylase (AADAC), which are expressed primarily in the liver and/or gastrointestinal tract, hydrolyze drugs containing ester and amide bonds in their chemical structure. These enzymes often catalyze the conversion of prodrugs, including the COVID-19 drugs remdesivir and molnupiravir, to their pharmacologically active forms. Information on the substrate specificity and inhibitory properties of these enzymes, which would be useful for drug development and toxicity avoidance, has accumulated. Recently,in vitroandin vivostudies have shown that these enzymes are involved not only in drug hydrolysis but also in lipid metabolism. CES1 and CES2 are capable of hydrolyzing triacylglycerol, and the deletion of their orthologous genes in mice has been associated with impaired lipid metabolism and hepatic steatosis. Adeno-associated virus-mediated human CES overexpression decreases hepatic triacylglycerol levels and increases fatty acid oxidation in mice. It has also been shown that overexpression of CES enzymes or AADAC in cultured cells suppresses the intracellular accumulation of triacylglycerol. Recent reports indicate that AADAC can be up- or downregulated in tumors of various organs, and its varied expression is associated with poor prognosis in patients with cancer. Thus, CES and AADAC not only determine drug efficacy and toxicity but are also involved in pathophysiology. This review summarizes recent findings on the roles of CES and AADAC in drug metabolism, physiology, and pathology.

Intratumoral and peritumoral radiomics predict pathological response after neoadjuvant chemotherapy against advanced gastric cancer

BACKGROUND

To investigate whether intratumoral and peritumoral radiomics may predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer.

METHODS

Clinical, pathological, and CT data from 231 patients with advanced gastric cancer who underwent neoadjuvant chemotherapy at our hospital between July 2014 and February 2022 were retrospectively collected. Patients were randomly divided into a training group (n = 161) and a validation group (n = 70). The support vector machine classifier was used to establish radiomics models. A clinical model was established based on the selected clinical indicators. Finally, the radiomics and clinical models were combined to generate a radiomics-clinical model. ROC analyses were used to evaluate the prediction efficiency for each model. Calibration curves and decision curves were used to evaluate the optimal model.

RESULTS

A total of 91 cases were recorded with good response and 140 with poor response. The radiomics model demonstrated that the AUC was higher in the combined model than in the intratumoral and peritumoral models (training group: 0.949, 0.943, and 0.846, respectively; validation group: 0.815, 0.778, and 0.701, respectively). Age, Borrmann classification, and Lauren classification were used to construct the clinical model. Among the radiomics-clinical models, the combined-clinical model showed the highest AUC (training group: 0.960; validation group: 0.843), which significantly improved prediction efficiency.

CONCLUSION

The peritumoral model provided additional value in the evaluation of pathological response after neoadjuvant chemotherapy against advanced gastric cancer, and the combined-clinical model showed the highest predictive efficiency.

CRITICAL RELEVANCE STATEMENT

Intratumoral and peritumoral radiomics can noninvasively predict the pathological response against advanced gastric cancer after neoadjuvant chemotherapy to guide early treatment decision and provide individual treatment for patients.

KEY POINTS

1. Radiomics can predict pathological responses after neoadjuvant chemotherapy against advanced gastric cancer. 2. Peritumoral radiomics has additional predictive value. 3. Radiomics-clinical models can guide early treatment decisions and improve patient prognosis.

A gene signature driven by abnormally methylated DEGs was developed for TP53 wild-type ovarian cancer samples by integrative omics analysis of DNA methylation and gene expression data

Background

Integrated omics analysis based on transcriptome and DNA methylation data combined with machine learning methods is very promising for the diagnosis, prognosis, and classification of cancer. In this study, the DNA methylation and gene expression data of ovarian cancer (OC) were analyzed to identify abnormally methylated differentially expressed genes (DEGs), screen potential therapeutic agents for OC, and construct a risk model based on the abnormally methylated DEGs to predict patient prognosis.

Methods

The gene expression and DNA methylation data of primary OC samples with tumor protein 53 (TP53) wild-type and normal samples were obtained from The Cancer Genome Atlas (TCGA) database. DEGs with aberrant methylation were analyzed by screening the intersection between DEGs and differentially methylated genes (DMGs). We attempted to search for potential drugs targeting DEGs with aberrant methylation by employing a network medicine framework. A gene signature based on the DEGs with aberrant methylation was constructed by regularized least absolute shrinkage and selection operator (LASSO) regression analysis.

Results

A total of 440 aberrant methylated DEGs were screened. Based on their gene expression profiles and methylation data from different regions, the results of both discriminative pattern recognition analysis and principal component analysis (PCA) showed a significant separation between tumor tissue and healthy ovarian tissue. In total, 126 potential therapeutic drugs were identified for OC by network-based proximity analysis. Five genes were identified in 440 aberrant methylated DEGs, which formed an aberrant methylated DEGs-driven gene signature. This signature could significantly distinguish the different overall survivals (OS) of OC patients and showed better predictive performance in both the training and validation sets.

Conclusions

In this study, the DNA methylation and gene expression data of OC were analyzed to identify abnormally methylated DEGs and potential therapeutic drugs, and a gene signature based on five aberrant methylation DEGs was constructed, which could better predict the risk of death in patients.