Co-expression network and survival analysis of breast cancer inflammation and immune system hallmark genes

IF 2.6 4区 生物学 Q2 BIOLOGY
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Abstract

The tertiary lymphoid structure (TLS) plays a central role in cancer immune response, and its gene expression pattern, called the TLS signature, has shown prognostic value in breast cancer. The formation of TLS and tumor-associated high endothelial venules (TA-HEVs), responsible for lymphocytic infiltration within the TLS, is associated with the expression of cancer hallmark genes (CHGs) related to immunity and inflammation. In this study, we performed co-expression network analysis of immune- and inflammation-related CHGs to identify predictive genes for breast cancer. In total, 382 immune- and inflammation-related CHGs with high expression variance were extracted from the GSE86166 microarray dataset of patients with breast cancer. CHGs were classified into five modules by applying weighted gene co-expression network analysis. The survival analysis results for each module showed that one module comprising 45 genes was statistically significant for relapse-free and overall survival. Four network properties identified key genes in this module with high prognostic prediction abilities: CD34, CXCL12, F2RL2, JAM2, PROS1, RAPGEF3, and SELP. The prognostic accuracy of the seven genes in breast cancer was synergistic and exceeded that of other predictors in both small and large public datasets. Enrichment analysis predicted that these genes had functions related to leukocyte infiltration of TA-HEVs. There was a positive correlation between key gene expression and the TLS signature, suggesting that gene expression levels are associated with TLS density. Co-expression network analysis of inflammation- and immune-related CHGs allowed us to identify genes that share a standard function in cancer immunity and have a high prognostic predictive value. This analytical approach may contribute to the identification of prognostic genes in TLS.

Abstract Image

乳腺癌炎症和免疫系统标志基因的共表达网络和生存分析
三级淋巴结构(TLS)在癌症免疫反应中起着核心作用,其基因表达模式被称为TLS特征,在乳腺癌中显示出预后价值。TLS和肿瘤相关高内皮静脉(TA-HEVs)的形成负责TLS内的淋巴细胞浸润,与免疫和炎症相关的癌症标志基因(CHGs)的表达有关。在这项研究中,我们对免疫和炎症相关的 CHGs 进行了共表达网络分析,以确定乳腺癌的预测基因。我们从乳腺癌患者的 GSE86166 微阵列数据集中共提取了 382 个具有高表达差异的免疫和炎症相关 CHGs。通过加权基因共表达网络分析,将CHGs分为五个模块。每个模块的生存分析结果显示,由 45 个基因组成的一个模块对无复发生存率和总生存率具有统计学意义。四个网络属性确定了该模块中具有较高预后预测能力的关键基因:CD34、CXCL12、F2RL2、JAM2、PROS1、RAPGEF3 和 SELP。这七个基因在乳腺癌中的预后准确性具有协同作用,在小型和大型公共数据集中都超过了其他预测因子。富集分析预测,这些基因的功能与TA-HEV的白细胞浸润有关。关键基因表达与TLS特征之间存在正相关,表明基因表达水平与TLS密度相关。通过对炎症和免疫相关CHG的共表达网络分析,我们确定了在癌症免疫中具有相同标准功能并具有较高预后预测价值的基因。这种分析方法可能有助于确定TLS的预后基因。
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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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