Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

IF 6 2区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Hongwei Wang, Yu-Nan Zhu, Sifan Zhang, Kexin Liu, Rong Huang, Zhigao Li, Lan Mei, Yingpu Li
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引用次数: 0

Abstract

Background: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive.

Methods: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters.

Results: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy.

Conclusion: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

转录组分析揭示了CFD和ANGPTL4在成纤维细胞调节B细胞谱系细胞外基质驱动的聚类和乳腺癌免疫治疗的新途径中的潜在作用。
背景:细胞外基质(extracellular matrix, ECM)的重塑在肿瘤进展和耐药过程中起着关键作用。然而,乳腺癌中ECM的组成模式及其潜在的生物学功能仍然难以捉摸。方法:下载TCGA数据库中乳腺癌患者的转录组和基因组数据。基于ECM成分和调节因子的特征,采用非负矩阵分解(NMF)方法将患者分为不同的聚类。加权基因共表达网络分析(Weighted Gene Co-expression Network Analysis, WGCNA)用于鉴定与ECM簇相关的核心基因。另外收集Metabric、SCAN_B、GSE12276、GSE16446、GSE19615、GSE20685、GSE21653、GSE58644、GSE58812、GSE88770等10个独立公共队列组成Training or Testing队列,采用机器学习计算ECM相关指数(ECI)进行生存分析。通过途径富集和相关分析探讨ECM簇、ECI和TME之间的关系。对来自GSE161529的单细胞转录组数据进行处理,以揭示ECM簇之间的差异。结果:使用NMF,我们在TCGA数据库中确定了三个ECM簇:C1(神经元),C2 (ECM)和C3(免疫)。随后,WGCNA被用于确定簇特异性基因并建立预后模型。该模型在Training队列(n = 5392, AUC = 0.861)和Testing队列(n = 1344, AUC = 0.711)中均显示出强大的乳腺癌患者生存预测能力。通过分析肿瘤微环境(TME),我们发现成纤维细胞和B细胞谱系是与ECM集群表型相关的核心细胞类型。单细胞RNA测序数据进一步显示,血管生成素样4 (ANGPTL4)+成纤维细胞与C2表型特异性相关,而补体因子D (CFD)+成纤维细胞表征其他ECM簇。CellChat分析表明,ANGPTL4+和CFD+成纤维细胞通过不同的信号通路调节B细胞谱系。此外,Kaplan-Meier Plotter网站的分析显示,CFD有利于免疫治疗应答,而ANGPTL4对接受免疫治疗的癌症患者的预后有负面影响。结论:我们在乳腺癌患者中发现了不同的ECM簇,与分子亚型无关。此外,我们基于这些ECM簇构建了一个有效的预后模型,并确认ANGPTL4+和CFD+成纤维细胞是乳腺癌免疫治疗的潜在生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Medicine
Molecular Medicine 医学-生化与分子生物学
CiteScore
8.60
自引率
0.00%
发文量
137
审稿时长
1 months
期刊介绍: Molecular Medicine is an open access journal that focuses on publishing recent findings related to disease pathogenesis at the molecular or physiological level. These insights can potentially contribute to the development of specific tools for disease diagnosis, treatment, or prevention. The journal considers manuscripts that present material pertinent to the genetic, molecular, or cellular underpinnings of critical physiological or disease processes. Submissions to Molecular Medicine are expected to elucidate the broader implications of the research findings for human disease and medicine in a manner that is accessible to a wide audience.
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