三阴性乳腺癌含溴结构域蛋白相关预后模型的构建。

IF 5.3 2区 医学 Q1 ONCOLOGY
Wei Chen, Yushuai Yu, Chenxi Wang, Zirong Jiang, Xiewei Huang, Yidan Lin, Hongjing Han, Qing Wang, Hui Zhang
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引用次数: 0

摘要

背景:含溴结构域蛋白(BRD)在恶性肿瘤的发生发展中起关键作用。本研究旨在鉴定三阴性乳腺癌(TNBC)患者brd相关基因(BRDRGs)相关的预后基因,并构建一种新的预后模型。方法:从公共数据库检索TCGA-TNBC、GSE135565和GSE161529的数据。使用GSE161529鉴定关键细胞类型。采用单样本基因集富集分析(ssGSEA)计算TCGA-TNBC的BRDRGs评分。通过差异表达分析鉴定差异表达基因(DEGs):关键细胞中的DEGs1,肿瘤与对照之间的DEGs2,以及TCGA-TNBC中BRDRGs评分高亚组和低亚组中的DEGs3。差异表达的BRDRGs (DE-BRDRGs)是通过重叠DEGs1、DEGs2和DEGs3来确定的。通过基因本体(GO)、京都基因与基因组百科全书(KEGG)通路分析和蛋白-蛋白相互作用(PPI)网络分析来研究活性通路和分子相互作用。通过单变量Cox回归和最小绝对收缩和选择算子(LASSO)回归分析选择预后基因,构建风险模型并计算风险评分。根据中位风险评分将TCGA-TNBC样本分为高危组和低危组。此外,还进行了与临床特征、基因集富集分析(GSEA)、免疫分析和伪时间分析的相关性分析。结果:共鉴定出120个DE-BRDRGs,来自4种关键细胞类型的605个DEGs1, 10,776个DEGs2和4,497个DEGs3重叠。氧化石墨烯分析揭示了丰富的术语,如“凋亡过程”、“免疫反应”和“细胞周期调节”,而56个KEGG通路,包括“MAPK信号通路”,与DE-BRDRGs相关。构建由KRT6A、PGF、ABCA1、EDNRB、CTSD和GJA4 6个预后基因组成的风险模型。基于独立预后因素的nomogram也被开发出来。高危组免疫细胞丰度明显增高。在两个危险组中,TP53表现出最高的突变频率。KRT6A、ABCA1、EDNRB和CTSD的表达在假性时间内逐渐降低。结论:建立并验证了与BRDRGs相关的新型TNBC预后模型,为BRD与TNBC之间的关系提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer.

Background: Bromodomain-containing protein (BRD) play a pivotal role in the development and progression of malignant tumours. This study aims to identify prognostic genes linked to BRD-related genes (BRDRGs) in patients with triple-negative breast cancer (TNBC) and to construct a novel prognostic model.

Methods: Data from TCGA-TNBC, GSE135565, and GSE161529 were retrieved from public databases. GSE161529 was used to identify key cell types. The BRDRGs score in TCGA-TNBC was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Differential expression analysis was performed to identify differentially expressed genes (DEGs): DEGs1 in key cells, DEGs2 between tumours and controls and DEGs3 in high and low BRDRGs score subgroups in TCGA-TNBC. Differentially expressed BRDRGs (DE-BRDRGs) were determined by overlapping DEGs1, DEGs2 and DEGs3. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were conducted to investigate active pathways and molecular interactions. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to construct a risk model and calculate risk scores. TNBC samples from TCGA-TNBC were classified into high and low-risk groups based on the median risk score. Additionally, correlations with clinical characteristics, Gene Set Enrichment Analysis (GSEA), immune analysis, and pseudotime analysis were performed.

Results: A total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3. GO analysis revealed enriched terms such as 'apoptotic process,' 'immune response,' and 'regulation of the cell cycle,' while 56 KEGG pathways, including the 'MAPK signaling pathway,' were associated with DE-BRDRGs. A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed. A nomogram based on independent prognostic factors was also developed. Immune cell abundance was significantly higher in high-risk group. In both risk groups, TP53 exhibited the highest mutation frequency. The expression of KRT6A, ABCA1, EDNRB, and CTSD went decreased progressively in pseudotime.

Conclusion: A novel prognostic model for TNBC associated with BRDRGs was developed and validated, providing fresh insights into the relationship between BRD and TNBC.

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来源期刊
CiteScore
10.90
自引率
1.70%
发文量
360
审稿时长
1 months
期刊介绍: Cancer Cell International publishes articles on all aspects of cancer cell biology, originating largely from, but not limited to, work using cell culture techniques. The journal focuses on novel cancer studies reporting data from biological experiments performed on cells grown in vitro, in two- or three-dimensional systems, and/or in vivo (animal experiments). These types of experiments have provided crucial data in many fields, from cell proliferation and transformation, to epithelial-mesenchymal interaction, to apoptosis, and host immune response to tumors. Cancer Cell International also considers articles that focus on novel technologies or novel pathways in molecular analysis and on epidemiological studies that may affect patient care, as well as articles reporting translational cancer research studies where in vitro discoveries are bridged to the clinic. As such, the journal is interested in laboratory and animal studies reporting on novel biomarkers of tumor progression and response to therapy and on their applicability to human cancers.
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