Ding Ran, Liu Qiwei, Yu Jing, Wang Yongkang, Gao Honglei, Kan Hongxing, Yang Yinfeng
School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei 230012, China.
Anhui Computer Application Research Institute of Chinese Medicine, China Academy of Chinese Medical Sciences, Hefei 230013, China.
ACS Omega. 2023 Mar 21;8(13):12217-12231. doi: 10.1021/acsomega.2c08227. eCollection 2023 Apr 4.
: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). : Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. : Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.
我们旨在通过整合基因组分析与肿瘤免疫微环境（TME）来识别乳腺癌（BC）亚型簇和关键基因分类器预后特征。BC数据集源自癌症基因组图谱（TCGA）、METABRIC和基因表达综合数据库（GEO）。进行无监督一致性聚类以获得BC患者的亚型簇。采用加权基因共表达网络分析（WGCNA）、最小绝对收缩和选择算子（LASSO）以及单变量和多变量回归分析来获得基因分类器特征及其生物学功能，并通过METABRIC数据库的BC数据集进行验证。此外，为评估BC患者的总生存率，进行了Kaplan-Meier生存分析。此外，为评估BC亚型簇与TME的关系，进行了单细胞分析。最后，还通过CIBERSORT和ESTIMATE算法计算了BC患者不同表型的药物敏感性和免疫细胞浸润情况。TCGA-BC样本分为三个亚型簇，即S1、S2和S3，其中S2的预后较差，S1和S3的预后较好。筛选出三个关键途径和10个关键预后相关基因特征。最后，单细胞分析表明，S1样本的免疫细胞类型最多，对肿瘤治疗药物更敏感的S2富含更多中性粒细胞，更多的多淋巴祖细胞参与亚型簇S3。我们的新颖之处在于采用大量数据集结合多种生物信息学方法来识别BC亚型簇和基因分类器特征。所有结果为BC的临床精准治疗提供了依据。
