Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

Identification of biomarkers related to tryptophan metabolism in osteoarthritis

Background

OA (osteoarthritis) is a common joint disease characterized by damage to the articular cartilage and affects the entire joint tissue, with its main manifestations being joint pain, stiffness, and limited movement.Currently,we know that OA is a complex process composed of inflammatory and metabolic factors.It is reported that the occurrence and development of OA is related to the change of tryptophan metabolism.Therefore, the study of tryptophan metabolism and OA related genes is hopeful to find a new therapeutic target for OA.

Methods

Differentially expressed genes (DEGs) in GSE55235 were gained via differential expression analysis (OA samples vs normal samples). The tryptophan metabolic related DEGs (TMR-DEGs) were obtained by overlapping tryptophan metabolism related genes (TMRGs) and DEGs. Further, biomarkers were screening via Least absolute shrinkage and selection operator (LASSO), naive bayes (NB) and supportvector machine-recursive feature elimination (SVM-RFE) algorithm to establish a diagnostic model. Afterward, Gene Set Enrichment Analysis (GSEA) and drug prediction were performed based on diagnostic biomarkers by multiple software and databases. Eventually, expression level of biomarker public databases was verified using real-time quantitative polymerase chain reaction (RT-qPCR).

Results

Three tryptophan metabolism related biomarkers (TDO2, AOX1 and SLC3A2) were identified in OA. GSEA analysis demonstrated that biomarkers were associated with the function of 'FoxO signaling pathway', 'spliceosome' and 'ribosome'. There were seven drugs with therapeutic potential on TDO2 and AOX1. Ultimately, compared with normal group, expression of AOX1 and SLC3A2 in OA group remarkable lower.

Conclusion

Overall, three tryptophan metabolic related diagnostic biomarkers that associated with OA were obtained, which provided a original direction for the diagnosis and treatment of OA.

Contribution of aldehyde oxidase to methotrexate-induced hepatotoxicity: in vitro and pharmacoepidemiological approaches

BACKGROUND

Methotrexate (MTX) is partially metabolized by aldehyde oxidase (AOX) in the liver and its clinical impact remains unclear. In this study, we aimed to demonstrate how AOX contributes to MTX-induced hepatotoxicity in vitro and clarify the relationship between concomitant AOX inhibitor use and MTX-associated liver injury development using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).

METHODS

We assessed intracellular MTX accumulation and cytotoxicity using HepG2 cells. We used the FAERS database to detect reporting odds ratio (ROR)-based MTX-related hepatotoxicity event signals.

RESULTS

AOX inhibition by AOX inhibitor raloxifene and siRNA increased the MTX accumulation in HepG2 cells and enhanced the MTX-induced cell viability reduction. In the FAERS analysis, the ROR for MTX-related hepatotoxicity increased with non-overlap of 95% confidence interval when co-administered with drugs with higher Imax, u (maximum unbound plasma concentration)/IC50 (half-maximal inhibitory concentration for inhibition of AOX) calculated based on reported pharmacokinetic data.

CONCLUSION

AOX inhibition contributed to MTX accumulation in the liver, resulting in increased hepatotoxicity. Our study raises concerns regarding MTX-related hepatotoxicity when co-administered with drugs that possibly inhibit AOX activity at clinical concentrations.

NAD+ Metabolism-Related Gene Profile Can Be a Relevant Source of Squamous Cell Carcinoma Biomarkers

Poor survival rates of squamous cell carcinomas (SCCs) are associated with high recurrence, metastasis, and late diagnosis, due in part to a limited number of reliable biomarkers. Thus, the identification of signatures improving the diagnosis of different SCC types is mandatory. Considering the relevant role of NAD+ metabolism in SCC chemoprevention and therapy, the study aimed at identifying new biomarkers based on NAD+ metabolism-related gene (NMRG) expression. Gene expression of 18 NMRGs and clinical-pathological information for patients with head and neck SCC (HNSCC), lung SCC (LuSCC), and cervix SCC (CeSCC) from The Cancer Genome Atlas (TCGA) were analyzed by several bioinformatic tools. We identified a 16-NMRG profile discriminating 3 SCCs from 3 non-correlated tumors. We found several genes for HNSCC, LuSCC, and CeSCC with high diagnostic power. Notably, three NMRGs were SCC-type specific biomarkers. Furthermore, specific signatures displayed high diagnostic power for several clinical-pathological characteristics. Analyzing tumor-infiltrating immune cell profiles and PD-1/PD-L1 levels, we found that NMRG expression was associated with suppressive immune microenvironment mainly in HNSCC. Finally, the evaluation of patient survival identified specific genes for HNSCC, LuSCC, and CeSCC with potential prognostic power. Therefore, our analyses indicate NAD+ metabolism as an important source of SCC biomarkers and potential therapeutic targets.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

LGALS1 was related to the prognosis of clear cell renal cell carcinoma identified by weighted correlation gene network analysis combined with differential gene expression analysis

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.