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Background <p>Prostate cancer is one of the common malignant tumors in men. Recent studies have reported that non-invasive liquid biopsy is of great significance in tumor diagnosis. We hope to find relevant detection genes to establish a diagnostic and prognostic model for prostate cancer.</p> Methods <p>This study was based on the RNA expression data of prostate cancer patients from the The Cancer Genome Atlas (TCGA) database and the data of exosome-related genes from the GeneCards website. Key exosome-related differential genes were identified through cluster modeling, univariate and multivariate regression analyses. The roles of these genes in the occurrence and prognosis of prostate cancer were assessed using ROC curves and survival analysis. Validation was performed using prostate cancer patient data from the Gene Expression Omnibus (GEO) database.</p> Results <p>Firstly, we obtained 117 exosome-related differential genes (ERDEGs) from the RNA expression data of prostate cancer patients in the TCGA database. Next, through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis of the ERDEGs, we obtained three genes that were significantly associated with prognosis (AQP2, H4C2, ZNF114) and calculated the risk score accordingly. Patients were divided into high-risk and low-risk groups based on this score, with significant differences in overall survival between the groups. At the same time, we conducted an immunological infiltration analysis on prostate cancer patients and Weighted correlation network analysis (WGCNA) on the ERDEGs. Finally, we used the GEO database (GSE69223, GSE229904) for verification and found that AQP2 and ZNF114 had good predictive value for the occurrence of prostate cancer.</p> Conclusion <p>Exosome-related genes such as AQP2 and ZNF114 exhibit good performance as non-invasive biomarkers in predicting the status and prognosis of prostate cancer to avoid the issues of high invasiveness associated with invasive examinations.</p>

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Identification and verification of exosome-related gene signature to predict the cancer status and prognosis of prostate cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月3日

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  • Haoxi Wang,
  • Xuwen Li,
  • Liyuan Wu,
  • Yabo Zhai,
  • Yihao Zhu,
  • Wenkuan Wang,
  • Dong Chen,
  • Nianzeng Xing,
  • Xiongjun Ye,
  • Feiya Yang

摘要

Background

Prostate cancer is one of the common malignant tumors in men. Recent studies have reported that non-invasive liquid biopsy is of great significance in tumor diagnosis. We hope to find relevant detection genes to establish a diagnostic and prognostic model for prostate cancer.

Methods

This study was based on the RNA expression data of prostate cancer patients from the The Cancer Genome Atlas (TCGA) database and the data of exosome-related genes from the GeneCards website. Key exosome-related differential genes were identified through cluster modeling, univariate and multivariate regression analyses. The roles of these genes in the occurrence and prognosis of prostate cancer were assessed using ROC curves and survival analysis. Validation was performed using prostate cancer patient data from the Gene Expression Omnibus (GEO) database.

Results

Firstly, we obtained 117 exosome-related differential genes (ERDEGs) from the RNA expression data of prostate cancer patients in the TCGA database. Next, through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis of the ERDEGs, we obtained three genes that were significantly associated with prognosis (AQP2, H4C2, ZNF114) and calculated the risk score accordingly. Patients were divided into high-risk and low-risk groups based on this score, with significant differences in overall survival between the groups. At the same time, we conducted an immunological infiltration analysis on prostate cancer patients and Weighted correlation network analysis (WGCNA) on the ERDEGs. Finally, we used the GEO database (GSE69223, GSE229904) for verification and found that AQP2 and ZNF114 had good predictive value for the occurrence of prostate cancer.

Conclusion

Exosome-related genes such as AQP2 and ZNF114 exhibit good performance as non-invasive biomarkers in predicting the status and prognosis of prostate cancer to avoid the issues of high invasiveness associated with invasive examinations.