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乳腺癌中新型转移相关免疫基因特征的鉴定与验证

Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer.

作者信息

Ma Shen, Hao Ran, Lu Yi-Wei, Wang Hui-Po, Hu Jie, Qi Yi-Xin

机构信息

Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050035, People's Republic of China.

Institutes of Health Research, Hebei Medical University, Shijiazhuang, Hebei, 050017, People's Republic of China.

出版信息

Breast Cancer (Dove Med Press). 2024 Apr 12;16:199-219. doi: 10.2147/BCTT.S448642. eCollection 2024.

DOI:10.2147/BCTT.S448642
PMID:38634039
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11021863/
Abstract

BACKGROUND: Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified. METHODS: Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). RESULTS: The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor. CONCLUSION: Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.

摘要

背景：远处转移仍然是乳腺癌（BRCA）患者死亡的主要原因。癌症转移过程涉及多种机制，包括免疫系统受损。然而，并非所有参与免疫功能的基因都已被全面鉴定。 方法：首先，从基因表达综合数据库（GSE102818、GSE45255、GSE86166）和癌症基因组图谱 - 乳腺癌（TCGA - BRCA）数据集中获取1623个BRCA样本，包括转录组测序和临床信息。随后，使用GSE102818数据集进行加权基因共表达网络分析（WGCNA），以识别与BRCA转移最相关的模块。此外，应用ConsensusClusterPlus将TCGA - BRCA患者分为两个亚组（G1和G2）。同时，使用最小绝对收缩和选择算子（LASSO）回归分析构建转移相关免疫基因（MRIGs）评分，以预测癌症的转移和进展。重要的是，通过逆转录定量聚合酶链反应（RT - qPCR）和免疫组织化学（IHC）验证了关键基因的表达。 结果：通过WGCNA筛选出的76个MRIGs的表达模式将TCGA - BRCA患者分为两个亚组（G1和G2），G1组的预后更差。此外，基于干性指数评分和肿瘤免疫功能障碍与排除评分，G1组表现出更高的mRNA表达水平。另外，较高的MRIGs评分代表BRCA患者进展的可能性更高。值得一提的是，G1组患者的MRIGs评分高于G2组。重要的是，RT - qPCR和IHC结果表明，成束和延伸蛋白ζ1（FEZ1）和胰岛素样生长因子2受体（IGF2R）是危险因素，而白细胞介素（IL）-1受体拮抗剂（IL1RN）是保护因素。 结论：我们的研究揭示了一个由八个免疫相关基因组成的预后模型，该模型可以预测BRCA的转移和进展。评分越高，转移概率越高。此外，验证了BRCA组织中关键基因与mRNA和蛋白质水平的生物信息学分析结果的一致性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/b8d6dbbf1078/BCTT-16-199-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/dbf0f90cc752/BCTT-16-199-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/c2b5675ff911/BCTT-16-199-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/998cfdc029d1/BCTT-16-199-g0003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/60244cade6cc/BCTT-16-199-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/51f36e265542/BCTT-16-199-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/da60db75c23a/BCTT-16-199-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/38fa95634252/BCTT-16-199-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/8d98e352d6f7/BCTT-16-199-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/b8d6dbbf1078/BCTT-16-199-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/dbf0f90cc752/BCTT-16-199-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/c2b5675ff911/BCTT-16-199-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/998cfdc029d1/BCTT-16-199-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/986a5b6f3c13/BCTT-16-199-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/60244cade6cc/BCTT-16-199-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/51f36e265542/BCTT-16-199-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/da60db75c23a/BCTT-16-199-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/38fa95634252/BCTT-16-199-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/8d98e352d6f7/BCTT-16-199-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d71/11021863/b8d6dbbf1078/BCTT-16-199-g0010.jpg

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