Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

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.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer

Background

Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes.

Methods

The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database.

Results

In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration.

Conclusions

The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

Genetic Signature of Human Pancreatic Cancer and Personalized Targeting

Pancreatic cancer is a highly lethal disease with a 5-year survival rate of around 11-12%. Surgery, being the treatment of choice, is only possible in 20% of symptomatic patients. The main reason is that when it becomes symptomatic, IT IS the tumor is usually locally advanced and/or has metastasized to distant organs; thus, early diagnosis is infrequent. The lack of specific early symptoms is an important cause of late diagnosis. Unfortunately, diagnostic tumor markers become positive at a late stage, and there is a lack of early-stage markers. Surgical and non-surgical cases are treated with neoadjuvant and/or adjuvant chemotherapy, and the results are usually poor. However, personalized targeted therapy directed against tumor drivers may improve this situation. Until recently, many pancreatic tumor driver genes/proteins were considered untargetable. Chemical and physical characteristics of mutated KRAS are a formidable challenge to overcome. This situation is slowly changing. For the first time, there are candidate drugs that can target the main driver gene of pancreatic cancer: KRAS. Indeed, KRAS inhibition has been clinically achieved in lung cancer and, at the pre-clinical level, in pancreatic cancer as well. This will probably change the very poor outlook for this disease. This paper reviews the genetic characteristics of sporadic and hereditary predisposition to pancreatic cancer and the possibilities of a personalized treatment according to the genetic signature.

Prognostic assessment capability of a five-gene signature in pancreatic cancer: a machine learning based-study

BACKGROUND

A prognostic assessment method with good sensitivity and specificity plays an important role in the treatment of pancreatic cancer patients. Finding a way to evaluate the prognosis of pancreatic cancer is of great significance for the treatment of pancreatic cancer.

METHODS

In this study, GTEx dataset and TCGA dataset were merged together for differential gene expression analysis. Univariate Cox regression and Lasso regression were used to screen variables in the TCGA dataset. Screening the optimal prognostic assessment model is then performed by gaussian finite mixture model. Receiver operating characteristic (ROC) curves were used as an indicator to assess the predictive ability of the prognostic model, the validation process was performed on the GEO datasets.

RESULTS

Gaussian finite mixture model was then used to build 5-gene signature (ANKRD22, ARNTL2, DSG3, KRT7, PRSS3). Receiver operating characteristic (ROC) curves suggested the 5-gene signature performed well on both the training and validation datasets.

CONCLUSIONS

This 5-gene signature performed well on both our chosen training dataset and validation dataset and provided a new way to predict the prognosis of pancreatic cancer patients.

Identification of potential biomarkers in Barrett's esophagus derived esophageal adenocarcinoma

Almost 50% of esophageal adenocarcinoma (EAC) patients progressed from Barrett's esophagus (BE). EAC is often diagnosed at late stages and is related to dismal prognosis. However, there are still no effective methods for stratification and therapy in BE and EAC. Two public datasets (GSE26886 and GSE37200) were analyzed to identify differentially expressed genes (DEGs) between BE and EAC. Then, a series of bioinformatics analyses were performed to explore potential biomarkers associated with BE-EAC. 27 up- and 104 down-regulated genes were observed between GSE26886 and GSE37200. The GO and KEGG enrichment analysis indicated that the DEGs were highly involved in tumorigenesis. Subsequently, Weighted Gene Co-Expression Network Analysis (WGCNA) were performed to explore the potential genes related to BE-EAC, which were validated in The Cancer Genome Atlas (TCGA) database, and 5 up-regulated genes (MYO1A, ACE2, COL1A1, LGALS4, and ADRA2A) and 3 down-regulated genes (AADAC, RAB27A, and P2RY14) were found in EAC. Meanwhile, ADRA2A and AADAC could contribute to EAC pathogenesis and progression. MYO1A, ACE2, COL1A1, LGALS4, ADRA2A, AADAC, RAB27A, and P2RY14 could be potential novel diagnostic and prognostic biomarkers in BE-EAC.

Characterization, comparative, and functional analysis of arylacetamide deacetylase from Gnathostomata organisms

BACKGROUND

Arylacetamide deacetylase (AADAC) is a lipolytic enzyme involved in xenobiotic metabolism. The characterization in terms of activity and substrate preference has been limited to a few mammalian species. The potential role and catalytic activities of AADAC from other organisms are still poorly understood. Therefore, in this work, the physicochemical properties, proteomic analysis, and protein-protein interactions from Gnathostomata organisms were investigated.

RESULTS

The analysis were performed with 142 orthologue sequences with ~ 48-100% identity with human AADAC. The catalytic motif HGG[A/G] tetrapeptide block was conserved through all AADAC orthologues. Four variations were found in the consensus pentapeptide GXSXG sequence (GDSAG, GESAG, GDSSG, and GSSSG), and a novel motif YXLXP was found. The prediction of N-glycosylation sites projected 4, 1, 6, and 4 different patterns for amphibians, birds, mammals, and reptiles, respectively. The transmembrane regions of AADAC orthologues were not conserved among groups, and variations in the number and orientation of the active site and C-terminal carboxyl were observed among the sequences studied. The protein-protein interaction of AADAC orthologues were related to cancer, lipid, and xenobiotic metabolism genes.

CONCLUSION

The findings from this computational analysis offer new insight into one of the main enzymes involved in xenobiotic metabolism from mammals, reptiles, amphibians, and birds and its potential use in medical and veterinarian biotechnological approaches.

AADAC promotes therapeutic activity of cisplatin and imatinib against ovarian cancer cells

OBJECTIVE

To explore how AADAC functions in the malignant progression of ovarian cancer, and the effect of AADAC on drug therapeutic activity against ovarian cancer cells.

METHODS

AADAC level in tumor and normal samples from TCGA-OV dataset and its survival significance were analyzed by bioinformatics methods. Signaling pathway enrichment analysis for the high- and low-AADAC patients was achieved by using GSEA software. AADAC expression in the cell lines with different treatments was evaluated via qRT-PCR. Cell proliferative ability was assessed via MTT assay Cell migratory and invasive abilities were evaluated via transwell assay. Angiogenesis assay was performed to examine the angiogenetic ability.

RESULTS

AADAC was upregulated in ovarian cancer tissues, and patients with high expression of AADAC had favorable survival conditions compared to the low AADAC expression ones. Overexpression of AADAC inhibited the malignant progression of ovarian cancer cells. Both cisplatin and imatinib suppressed cancer cell malignant progression, while overexpressed AADAC synergistically enhanced such inhibition.

CONCLUSIONS

The study demonstrated that AADAC could somehow suppress the malignant progression of ovarian cancer, especially at the cellular level. In addition, synergic tumor-inhibitory effects between AADAC and the anti-cancer drugs were identified. All the above results proposed a novel idea and candidate biomarker for ovarian cancer therapy.

Deciphering CHFR Role in Pancreatic Ductal Adenocarcinoma

Checkpoint with forkhead-associated and ring finger domains (CHFR) has been proposed as a predictive and prognosis biomarker for different tumor types, but its role in pancreatic ductal adenocarcinoma (PDAC) remains unknown. The aim of this study was two-pronged: to review the role of CHFR in PDAC and evaluating CHFR as a potential predictive biomarker in this disease. For this purpose, we first explored the CHFR messenger (m)RNA expression and promoter methylation through the TCGA database. Secondly, the CHFR expression and promoter methylation were prospectively evaluated in a cohort of patients diagnosed with borderline (n = 19) or resectable (n = 16) PDAC by immunohistochemistry (IHC), methylation specific-PCR (MSP), and pyrosequencing. The results from the TCGA database showed significant differences in terms of progression-free survival (PFS) and overall survival (OS) based on the CHFR mRNA expression, which was likely independent from the promoter methylation. Importantly, our results showed that in primarily resected patients and also the entire cohort, a higher CHFR expression as indicated by the higher IHC staining intensity might identify patients with longer disease-free survival (DFS) and OS, respectively. Similarly, in the same cohorts, patients with lower methylation levels by pyrosequencing showed significantly longer OS than patients without this pattern. Both, the CHFR expression intensity and its promoter methylation were established as independent prognostic factors for PFS and OS in the entire cohort. In contrast, no significant differences were found between different methylation patterns for CHFR and the response to taxane-based neoadjuvant treatment. These results suggest the potential role of the higher expression of CHFR and the methylation pattern of its promoter as potential prognostic biomarkers in PDAC, thus warranting further comprehensive studies to extend and confirm our preliminary findings.

A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BACKGROUND

Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer.

METHODS

Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores.

RESULTS

TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) - (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823.

CONCLUSIONS

The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.