Exosome-Associated Gene Signature for Predicting the Prognosis of Ovarian Cancer Patients. (23rd January 2023)

• Record Type: Journal Article
• Authors: Zhu, Zihan; Geng, Rui; Zhang, Yixin; Liu, Jinhui; Bai, Jianling
• Other Names: Wang Fu Academic Editor.
• Is Part Of: Journal of immunology research. Volume 2023(2023)
• Publication Date: 2023-01-23
• Subjects: Immunology -- Periodicals; Immunology -- Research -- Periodicals; 616.07905
• DOI: 10.1155/2023/8727884
• Languages: English
• ISSNs: 2314-8861

Abstract

Abstract : Background. The exosome is of vital importance throughout the entire progression of cancer. Because of the lack of effective biomarkers in ovarian cancer (OV), we intend to investigate the connection between exosomes and tumor immune microenvironment to verify that exosome-related genes (ERGs) can precisely forecast the prognosis of OV patients. Methods. First, 117 ERGs in The Cancer Genome Atlas (TCGA) dataset were recognized. Afterwards, the risk signature consisting of four ERGs with prognostic significance was built by univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. We also validated the risk signature by Kaplan-Meier analysis, receiver operating characteristic curve analysis and principal component analysis. Furthermore, gene set enrichment analysis was performed to compare the enrichment patterns between the two risk subgroups. The connections between the exosome-related gene risk score (ERGRS) and clinical features, immune infiltration, immune checkpoint-related genes, copy number variation, and drug sensitivity were explored. We also assessed the function of the ERGRS to forecast immunotherapeutic efficacy by immunophenoscore (IPS). Results. The high-risk group had a worse prognosis than the group with low risk. We verified that the established model possessed a relatively good prognostic value. Pathway enrichment analysis indicated that the genome-wide group with low risk was enriched in immune-related pathways. We discovered that resting dendritic cells and stromal scores were upregulated in patients with high risk in the TCGA and Gene Expression Omnibus (GEO) cohorts. Moreover, the expression of six common immune checkpoint inhibitor targets was assessed, which revealed that the expression levels of CD274 (PD-L1), PDCD1 (PD-1), and IDO1 in patients with high risk were lower than those in patients with low risk. Afterwards, the low-risk group had higher IPS across the four immunotherapies, implying that it had better effects of immunotherapies. Conclusion. Our study demonstrates that the exosome-related gene risk model is closely associated with immune infiltration. It can well forecast the prognosis of OV patients and guide the selection of immunotherapeutic strategies.