Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

Xiong, Luyang; Feng, Yuchen; Hu, Wei; Tan, Jiahong; Li, Shusheng; Wang, Hongjie

doi:10.3389/fgene.2021.683173

Cited by 11 publications

(8 citation statements)

References 36 publications

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“…In this work, we intersected the gene AOX1 between 6 hub genes and 664 oxidative stress-related genes. Xiong et al has identi ed that AOX1 was decreased and acted as a tumor suppressor gene in clear cell Renal Cell Carcinoma (ccRCC) and PCa [49,50]. In this work, we illustrated that AOX1 was down-regulated in PCa cells.…”

Section: Discussionsupporting

confidence: 52%

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

Yuan

et al. 2022

Preprint

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Objectives To explore the potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa). Methods We downloaded from the Gene Expression Omnibus (GEO) database, six previously published datasets containing gene expression data (GSE8511, GSE14206, GSE46602, GSE55945, GSE69223, and GSE71016). Take GSE8511, GSE14206, GSE46602, and GSE55945 as the training group (127 PCa cases and 52 normal controls) and GSE69223 and GSE71016 as the test group (63 PCa cases and 62 normal controls). Between 127 PCa cases and 52 normal controls, differentially expressed genes (DEGs) were filtered. Following that, candidate PCa biomarkers were discovered using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. We conducted a difference analysis for these genes in the test group, and a significance level of P< 0.05 was considered statistically significant. To determine the train group’s discriminating ability, the area under the receiver operating characteristic curve (AUC) value was acquired and utilized, with hub genes defined as those having an AUC greater than 85%. The biomarkers’ expression levels and diagnostic utility in PCa were confirmed further in the GSE69223 and GSE71016 datasets. Lastly, to assess the relevant invasion of cells per every sample, the CIBERSORT algorithm and the ESTIMATE technique were utilized. Results The possible PCa diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were found and verified using the GSE69223 and GSE71016 datasets. Immune cell infiltration analysis revealed that AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were correlated with Mast cells resting, Macrophages M0, Macrophages M2, Neutrophils, NK cells activated, T cells CD4 memory resting, T cells gamma delta, T cells follicular helper, T cells CD4 naive and B cells memory. Meanwhile, AOX1 was identified as an oxidative stress-related biomarker that could use to be a prognostic biomarker. From expreimental validations, we detected that AOX1 was lowly expressed in PCa cell lines. Overexpression of AOX1 clearly reduced the proliferation and migration of the PCa cells, the anti-tumor effect of APX1 may be related to oxidative stress. Conclusion We used a multi-pronged approach to identify PCa biomarkers and examine the important function of infiltration of cells in PCa. AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN can be used as diagnostic markers of PCa, and AOX1 can be used to predicting the prognosis, which providing new perspectives into the incidence and molecular mechanisms of PCa in the future.

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Section: Discussionsupporting

confidence: 52%

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

Yuan

et al. 2022

Preprint

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“…AOX1 down-regulation in all SCCs underlines its potential importance in SCC pathogenesis. AOX1 encodes aldehyde oxidase 1 that is down-regulated in several tumors mainly as a consequence of hypermethylation [30][31][32]. This gene is implicated in the AKT pathway as well as NAD+ catabolism.…”

Section: Discussionmentioning

confidence: 99%

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

Minafò,

Antonini,

Dellambra

2024

Cancers

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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.

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“…Radical nephrectomy is the standard treatment for localized primary kidney cancer, but approximately one-quarter of these have relapses in distant sites ( Choueiri and Motzer 2017 ). With the development of bioinformatics, although some biomarkers related to ccRCC have been found in recent years, such as AOX1 ( Xiong et al, 2021 ), circCHST15 ( Gui et al, 2021 ), DDX39 ( Bao et al, 2021 ) and PPAR α ( Luo et al, 2019 ), the risk of death in patients with ccRCC is still high, so it is necessary to find more reliable markers and obtain more treatments to reduce the risk of death in patients with ccRCC. We identified 397 differentially expressed genes with consistent expression trends in the GSE46699 and TCGA-KIRC databases by WGCNA and Differential gene expression analysis.…”

Section: Discussionmentioning

confidence: 99%

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

Jiang

Wang²,

Xie³

et al. 2023

Front. Genet.

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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.

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Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

Cited by 11 publications

References 36 publications

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

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

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

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

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