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一种新型免疫检查点相关基因特征的鉴定可预测乳腺癌的预后及免疫治疗效果并进行实验验证。

Identification of a novel immune checkpoint-related gene signature predicts prognosis and immunotherapy in breast cancer and experiment verification.

作者信息

Yin Ke, Guo Yangyang, Wang Jinqiu, Guo Shenchao, Zhang Chunxu, Dai Yongping, Guo Yu, Dai Chen

机构信息

Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China.

Digestive Diseases Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong, China.

出版信息

Sci Rep. 2024 Dec 28;14(1):31065. doi: 10.1038/s41598-024-82266-1.


DOI:10.1038/s41598-024-82266-1
PMID:39730892
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11680899/
Abstract

Breast cancer (BRCA) is one of the pivotal causes of female death worldwide. And the morbidity and mortality of breast cancer have increased rapidly. Immune checkpoints are important to maintain immune tolerance and are regarded as important therapeutic targets. However, research for BRCA were limited to single immune checkpoint-related gene (ICG) and few studies have systematically explored expression profile of Immune checkpoint-related genes or attempted to construct a prognostic gene risk model based on immune checkpoint-related genes. We identified immune checkpoint-related differentially expressed genes (DEGs) in BRCA and normal breast tissues from TCGA database. A 7-gene signature was created by utilizing the univariate Cox regression model with least absolute shrinkage and selection operator (LASSO) Cox regression method. In addition, we conducted a nomogram to predict the prognostic significance. This tool enables quantitative prediction of patient prognosis, serving as a valuable reference for clinical decision-making, thereby improving patient outcomes. Relationships between our risk model and clinical indicators, TME (Tumor Microenvironment), immune cell infiltration, immune response and drug susceptibility were investigated. A set of in vitro cell assays was conducted to decipher the relationship between MAP2K6 and proliferation, invasion, migration, colony formation and apoptosis rate of breast cancer cells. As a result, we established a prognostic model composed of seven ICGs in BRCA. Based on the median risk score, BRCA patients were equally assigned into two groups of high- and low-risk. High-risk BRCA patients have poorer OS (overall survival) than low-risk patients. In addition, there were remarkable differences between these two groups in clinicopathological features, TME, immune cell infiltration, immune response and drug susceptibility. The results of GO and KEGG analyses indicated that DEGs between the high- and low-risk groups were involved in immune-related biological processes and pathways. GSEA analysis also showed that a number of immune-related pathways were notably enriched in the low-risk group. Finally, results of cell-based assays indicated that MAP2K6 may play a pivotal role in the initiation and progression of breast cancer as a tumor suppressor gene. In conclusion, we created a novel ICG signature that has the potential to predict the survival and drug sensitivity of BRCA patients. Furthermore, this study indicated that MAP2K6 may serve as a novel target for BRCA therapy.

摘要

乳腺癌(BRCA)是全球女性死亡的关键原因之一。而且乳腺癌的发病率和死亡率迅速上升。免疫检查点对于维持免疫耐受很重要,被视为重要的治疗靶点。然而,对BRCA的研究仅限于单个免疫检查点相关基因(ICG),很少有研究系统地探索免疫检查点相关基因的表达谱,或尝试基于免疫检查点相关基因构建预后基因风险模型。我们从TCGA数据库中鉴定了BRCA和正常乳腺组织中免疫检查点相关的差异表达基因(DEG)。利用单变量Cox回归模型结合最小绝对收缩和选择算子(LASSO)Cox回归方法创建了一个7基因特征。此外,我们绘制了列线图来预测预后意义。这个工具能够对患者预后进行定量预测,为临床决策提供有价值的参考,从而改善患者的预后。我们研究了我们的风险模型与临床指标、肿瘤微环境(TME)、免疫细胞浸润、免疫反应和药物敏感性之间的关系。进行了一系列体外细胞实验来阐明MAP2K6与乳腺癌细胞增殖、侵袭、迁移、集落形成和凋亡率之间的关系。结果,我们在BRCA中建立了一个由7个ICG组成的预后模型。根据中位风险评分,BRCA患者被平均分为高风险和低风险两组。高风险BRCA患者的总生存期(OS)比低风险患者差。此外,这两组在临床病理特征、TME、免疫细胞浸润、免疫反应和药物敏感性方面存在显著差异。GO和KEGG分析结果表明,高风险组和低风险组之间的DEG参与了免疫相关的生物学过程和途径。GSEA分析还表明,许多免疫相关途径在低风险组中显著富集。最后,基于细胞的实验结果表明,MAP2K6作为一种肿瘤抑制基因可能在乳腺癌的发生和发展中起关键作用。总之,我们创建了一个新的ICG特征,有可能预测BRCA患者的生存和药物敏感性。此外,本研究表明MAP2K6可能作为BRCA治疗的新靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/4dba0113a041/41598_2024_82266_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/40322bf24036/41598_2024_82266_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/d1342ee6a575/41598_2024_82266_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/739d76aab67a/41598_2024_82266_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/01511daac11d/41598_2024_82266_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/75b93113068d/41598_2024_82266_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/4b2e144e9369/41598_2024_82266_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/4dba0113a041/41598_2024_82266_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/40322bf24036/41598_2024_82266_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/f42cea5ac98a/41598_2024_82266_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/b801d6e50ec7/41598_2024_82266_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/d445eba46106/41598_2024_82266_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/07bad7e04eb1/41598_2024_82266_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/deae5e65d5c2/41598_2024_82266_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/6a7bb0a18e17/41598_2024_82266_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/d1342ee6a575/41598_2024_82266_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/739d76aab67a/41598_2024_82266_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/01511daac11d/41598_2024_82266_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/75b93113068d/41598_2024_82266_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/4b2e144e9369/41598_2024_82266_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b33/11680899/4dba0113a041/41598_2024_82266_Fig13_HTML.jpg

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