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

作者全名："Mo, Xiaocong; Yuan, Kaisheng; Hu, Di; Huang, Cheng; Luo, Juyu; Liu, Hang; Li, Yin"

作者地址："[Mo, Xiaocong; Li, Yin] Jinan Univ, Affiliated Hosp 1, Dept Oncol, Guangzhou, Guangdong, Peoples R China; [Yuan, Kaisheng] Jinan Univ, Affiliated Hosp 1, Dept Metab & Bariatr Surg, Guangzhou, Guangdong, Peoples R China; [Hu, Di; Huang, Cheng; Luo, Juyu] Jinan Univ, Affiliated Hosp 1, Dept Neurol, Guangzhou, Guangdong, Peoples R China; [Hu, Di; Huang, Cheng; Luo, Juyu] Jinan Univ, Affiliated Hosp 1, Stroke Ctr, Guangzhou, Guangdong, Peoples R China; [Liu, Hang] Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, Chongqing, Peoples R China"

通信作者："Li, Y (通讯作者)，Jinan Univ, Affiliated Hosp 1, Dept Oncol, Guangzhou, Guangdong, Peoples R China."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001047070300001

JCR分区：Q2

影响因子：3.5

年份：2023

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：biomarker; prostate cancer; AOX1; CIBERSORT; diagnostic

摘要："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."

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