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基于免疫细胞相互作用网络的乳腺癌预后相关关键模型和生物标志物。

Pivotal models and biomarkers related to the prognosis of breast cancer based on the immune cell interaction network.

机构信息

Department of Mammary Surgery I, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), No. 519, Kunzhou Road, Kunming, 650118, China.

Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.

出版信息

Sci Rep. 2022 Aug 11;12(1):13673. doi: 10.1038/s41598-022-17857-x.

DOI:10.1038/s41598-022-17857-x
PMID:35953532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9372165/
Abstract

The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. In this study, single cell transcriptome sequencing data of breast cancer were used to analyze the relationship between breast cancer heterogeneity and prognosis. In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). Proportion analysis of immune cells showed that NK cells were significantly aggregated in triple negative breast cancer, and the proportion of macrophages was significantly increased in primary breast cancer, while B cells, T cells, and neutrophils may be involved in the metastasis of breast cancer. The results of ligand receptor interaction network revealed that macrophages and DC cells were the most frequently interacting cells with other cells in breast cancer. The results of WGCNA analysis suggested that the MEblue module is most relevant to the overall survival time of triple negative breast cancer. Twenty-four prognostic genes in the blue module were identified by univariate Cox regression analysis and KM survival analysis. Multivariate regression analysis combined with risk analysis was used to analyze 24 prognostic genes to construct a prognostic model. The verification result of our prognostic model showed that there were significant differences in the expression of PCDH12, SLIT3, ACVRL1, and DLL4 genes between the high-risk group and the low-risk group, which can be used as prognostic biomarkers.

摘要

乳腺癌异质性对患者预后的影响尚不清楚，特别是免疫细胞在乳腺癌预后中的作用。本研究利用乳腺癌单细胞转录组测序数据，分析乳腺癌异质性与预后的关系。本研究在两个单细胞数据集（GSE75688 和 G118389）中鉴定出 14 个细胞簇。免疫细胞比例分析表明，NK 细胞在三阴性乳腺癌中明显聚集，原发性乳腺癌中巨噬细胞的比例明显增加，而 B 细胞、T 细胞和中性粒细胞可能参与乳腺癌的转移。配体受体相互作用网络的结果表明，巨噬细胞和 DC 细胞是与乳腺癌中其他细胞相互作用最频繁的细胞。WGCNA 分析结果表明，MEblue 模块与三阴性乳腺癌的总生存时间最相关。通过单因素 Cox 回归分析和 KM 生存分析鉴定出蓝色模块中的 24 个预后基因。通过多因素回归分析结合风险分析，分析 24 个预后基因构建预后模型。我们的预后模型的验证结果表明，高危组和低危组之间 PCDH12、SLIT3、ACVRL1 和 DLL4 基因的表达存在显著差异，可作为预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/45b89cd9d983/41598_2022_17857_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/45b89cd9d983/41598_2022_17857_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/f5ee0787717d/41598_2022_17857_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/1582968c53e7/41598_2022_17857_Fig2_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/4fc767a8b423/41598_2022_17857_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/0bef4cd51a27/41598_2022_17857_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/485c5f6d07ec/41598_2022_17857_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/06efc62cc8d3/41598_2022_17857_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/894353b80894/41598_2022_17857_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/bf9c6271aef9/41598_2022_17857_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cf7/9372165/45b89cd9d983/41598_2022_17857_Fig10_HTML.jpg

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