Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer

IF 3.2 4区医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Journal of Gene Medicine Pub Date : 2023-11-23 DOI:10.1002/jgm.3637

Quanyi Long, Yunfei Wang, Hongjiang Li

{"title":"Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer","authors":"Quanyi Long, Yunfei Wang, Hongjiang Li","doi":"10.1002/jgm.3637","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC<sub>50</sub> values of 62 drugs.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.</p>\n </section>\n </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Gene Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jgm.3637","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background

In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses.

Methods

In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs.

Results

Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC₅₀ values of 62 drugs.

Conclusions

In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.

Abstract Image

查看原文本刊更多论文

同源重组缺乏反映了乳腺癌患者的异质性和监测治疗反应。

背景:在乳腺癌中，同源重组缺陷(HRD)是一种常见的致癌机制。根据HRD生物标志物对BC进行分类，为BC分子特征、病理特征和治疗反应的鉴别建立平台具有重要意义。方法:共收集109个HRD基因，采用单因素Cox回归分析筛选预后基因，构建共识矩阵确定BC亚型。采用Limma包筛选差异表达基因(differential expressed genes, DEGs)，采用随机森林分析法筛选，建立模型分析BC患者对不同药物的免疫治疗反应、敏感性及预后。结果:109个HRD基因中有13个是BC的预后基因，根据表达情况将BC分为两个亚组。第1类患者的生存结果明显落后，适应性免疫评分明显高于第2类患者。通过随机森林分析确定了6个基因作为建立模型的因子。该模型提供了一个称为风险评分的预测，该预测对62种药物的BC预后、免疫治疗反应和IC50值有显著的分层效应。结论:在本研究中，成功鉴定了两种HRD亚型BC，并确定了其突变和免疫学特征。建立了基于HRD亚型差异基因的模型，该模型可作为预测BC预后、免疫治疗反应和药物敏感性的潜在指标。

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

Journal of Gene Medicine 医学-生物工程与应用微生物

CiteScore

6.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The aims and scope of The Journal of Gene Medicine include cutting-edge science of gene transfer and its applications in gene and cell therapy, genome editing with precision nucleases, epigenetic modifications of host genome by small molecules, siRNA, microRNA and other noncoding RNAs as therapeutic gene-modulating agents or targets, biomarkers for precision medicine, and gene-based prognostic/diagnostic studies. Key areas of interest are the design of novel synthetic and viral vectors, novel therapeutic nucleic acids such as mRNA, modified microRNAs and siRNAs, antagomirs, aptamers, antisense and exon-skipping agents, refined genome editing tools using nucleic acid /protein combinations, physically or biologically targeted delivery and gene modulation, ex vivo or in vivo pharmacological studies including animal models, and human clinical trials. Papers presenting research into the mechanisms underlying transfer and action of gene medicines, the application of the new technologies for stem cell modification or nucleic acid based vaccines, the identification of new genetic or epigenetic variations as biomarkers to direct precision medicine, and the preclinical/clinical development of gene/expression signatures indicative of diagnosis or predictive of prognosis are also encouraged.