A Novel Peroxisome-Related Gene Signature Predicts Breast Cancer Prognosis and Correlates with T Cell Suppression.

IF 3.3 4区 医学 Q2 ONCOLOGY
Breast Cancer : Targets and Therapy Pub Date : 2024-12-09 eCollection Date: 2024-01-01 DOI:10.2147/BCTT.S490154
Yunxiang Wang, Sheng Xu, Junfeng Liu, Pan Qi
{"title":"A Novel Peroxisome-Related Gene Signature Predicts Breast Cancer Prognosis and Correlates with T Cell Suppression.","authors":"Yunxiang Wang, Sheng Xu, Junfeng Liu, Pan Qi","doi":"10.2147/BCTT.S490154","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Peroxisomes are increasingly linked to cancer development, yet the prognostic role of peroxisome-related genes (PRGs) in breast cancer remains unclear.</p><p><strong>Objective: </strong>This study aimed to construct a prognostic model based on PRG expression in breast cancer to clarify their prognostic value and clinical implications.</p><p><strong>Methods: </strong>Transcriptomic data from TCGA and GEO were used for training and validation cohorts. TME characteristics were analyzed with ESTIMATE, MCP-counter, and CIBERSORT algorithms. qPCR validated mRNA expression levels of risk genes, and data analysis was conducted in R.</p><p><strong>Results: </strong>Univariate and multivariate Cox regression identified a 7-gene PRG risk signature (ACBD5, ACSL5, DAO, NOS2, PEX3, PEX10, and SLC27A2) predicting breast cancer prognosis in training (n=1069), internal validation (n=327), and external validation (merged from four GEO datasets, n=640) datasets. While basal and Her2 subtypes had higher risk scores than luminal subtypes, a significant prognostic impact of the PRG risk signature was seen only in luminal subtypes. The high-risk subgroup exhibited a higher frequency of focal synonymous copy number alterations (SCNAs), arm-level amplifications and deletions, and single nucleotide variations. These increased genomic aberrations were associated with greater immune suppression and reduced CD8+ T cell infiltration. Bulk RNA sequencing and single-cell analyses revealed distinct expression patterns of peroxisome-related genes (PRGs) in the breast cancer TME: PEX3 was primarily expressed in malignant and stromal cells, while ACSL5 showed high expression in T cells. Additionally, the PRG risk signature demonstrated efficacy comparable to that of well-known biomarkers for predicting immunotherapy responses. Drug sensitivity analysis revealed that the PRG high-risk subgroup was sensitive to inhibitors of BCL-2 family proteins (BCL-2, BCL-XL, and MCL1) and other kinases (PLK1, PLK1, BTK, CHDK1, and EGFR).</p><p><strong>Conclusion: </strong>The PRG risk signature serves as a promising biomarker for evaluating peroxisomal activity, prognosis, and responsiveness to immunotherapy in breast cancer.</p>","PeriodicalId":9106,"journal":{"name":"Breast Cancer : Targets and Therapy","volume":"16 ","pages":"887-911"},"PeriodicalIF":3.3000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639899/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer : Targets and Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/BCTT.S490154","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Peroxisomes are increasingly linked to cancer development, yet the prognostic role of peroxisome-related genes (PRGs) in breast cancer remains unclear.

Objective: This study aimed to construct a prognostic model based on PRG expression in breast cancer to clarify their prognostic value and clinical implications.

Methods: Transcriptomic data from TCGA and GEO were used for training and validation cohorts. TME characteristics were analyzed with ESTIMATE, MCP-counter, and CIBERSORT algorithms. qPCR validated mRNA expression levels of risk genes, and data analysis was conducted in R.

Results: Univariate and multivariate Cox regression identified a 7-gene PRG risk signature (ACBD5, ACSL5, DAO, NOS2, PEX3, PEX10, and SLC27A2) predicting breast cancer prognosis in training (n=1069), internal validation (n=327), and external validation (merged from four GEO datasets, n=640) datasets. While basal and Her2 subtypes had higher risk scores than luminal subtypes, a significant prognostic impact of the PRG risk signature was seen only in luminal subtypes. The high-risk subgroup exhibited a higher frequency of focal synonymous copy number alterations (SCNAs), arm-level amplifications and deletions, and single nucleotide variations. These increased genomic aberrations were associated with greater immune suppression and reduced CD8+ T cell infiltration. Bulk RNA sequencing and single-cell analyses revealed distinct expression patterns of peroxisome-related genes (PRGs) in the breast cancer TME: PEX3 was primarily expressed in malignant and stromal cells, while ACSL5 showed high expression in T cells. Additionally, the PRG risk signature demonstrated efficacy comparable to that of well-known biomarkers for predicting immunotherapy responses. Drug sensitivity analysis revealed that the PRG high-risk subgroup was sensitive to inhibitors of BCL-2 family proteins (BCL-2, BCL-XL, and MCL1) and other kinases (PLK1, PLK1, BTK, CHDK1, and EGFR).

Conclusion: The PRG risk signature serves as a promising biomarker for evaluating peroxisomal activity, prognosis, and responsiveness to immunotherapy in breast cancer.

预测乳腺癌预后并与 T 细胞抑制相关的新型过氧化物酶体相关基因特征
背景:过氧化物酶体与癌症发展的关系越来越密切,但过氧化物酶体相关基因(PRGs)在乳腺癌中的预后作用仍不明确:本研究旨在构建基于乳腺癌中过氧化物酶体相关基因表达的预后模型,以明确其预后价值和临床意义:方法:将 TCGA 和 GEO 的转录组数据用于训练和验证队列。采用ESTIMATE、MCP-counter和CIBERSORT算法分析TME特征,qPCR验证风险基因的mRNA表达水平,用R语言进行数据分析:单变量和多变量Cox回归在训练数据集(n=1069)、内部验证数据集(n=327)和外部验证数据集(由四个GEO数据集合并而成,n=640)中发现了预测乳腺癌预后的7个基因PRG风险特征(ACBD5、ACSL5、DAO、NOS2、PEX3、PEX10和SLC27A2)。虽然基底亚型和Her2亚型的风险评分高于管腔亚型,但PRG风险特征仅对管腔亚型的预后有显著影响。高风险亚组的病灶同义拷贝数改变(SCNA)、臂级扩增和缺失以及单核苷酸变异的频率较高。这些基因组畸变的增加与免疫抑制和 CD8+ T 细胞浸润减少有关。大量 RNA 测序和单细胞分析揭示了乳腺癌 TME 中过氧化物酶体相关基因(PRGs)的不同表达模式:PEX3 主要在恶性细胞和基质细胞中表达,而 ACSL5 则在 T 细胞中高表达。此外,PRG风险特征在预测免疫疗法反应方面的功效与知名生物标记物相当。药物敏感性分析表明,PRG高风险亚组对BCL-2家族蛋白(BCL-2、BCL-XL和MCL1)和其他激酶(PLK1、PLK1、BTK、CHDK1和EGFR)抑制剂敏感:PRG风险特征是评估乳腺癌过氧化物酶体活性、预后和对免疫疗法反应性的一种有前途的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.10
自引率
0.00%
发文量
40
审稿时长
16 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信