Gene-Set Enrichment Analysis (GSEA) of Microarray Data in Patients with HER2-Positive Breast Cancer Treated with Preoperative Chemotherapy Including Trastuzumab.

Abstract Background: HER2-positive breast cancer (BC) is a highly aggressive phenotype and accounts for 15–20% of all BC cases. The role of the host immune features in predictive response to anti-HER2 therapies and prognosis in BC is already suggested. We aimed to develop a predictive and prognostic model and examine its relevance to the clinical outcomes of patients with HER2-positive BC.Methods: Based on the single-sample gene set enrichment analysis (ssGSEA) scores of the signatures related to neoadjuvant trastuzumab therapy response, an immune effective score (IES) was constructed using principal component analysis algorithms. A bioinformatic analysis using 4 independent cohorts (GSE66305, n=88; GSE130786, n=110; TCGA, n=123; METABRIC, n=236) established associations between IES and clinical outcomes.Results: Genes associated with neoadjuvant trastuzumab therapy response enriched in pathways related to antitumor immune activities, including T cell receptor signaling pathway, Natural killer cell-mediated cytotoxicity and NF−kappa B signaling pathway. IES was demonstrated to be a predictive biomarker to neoadjuvant trastuzumab therapy benefits (GSE66305: area under the curve (AUC) = 0.804; GSE130786: AUC = 0.704). In addition, IES was identified as an independent prognostic factor for overall survival (OS) in the TCGA cohort (P = 0.036, hazard ratio (HR): 0.66, 95% confidence interval (CI): 0.449–0.97) and METABRIC cohort (P = 0.037, HR: 0.9, 95% CI: 0.81–0.99).Conclusions: The IES has a predictive value for response to neoadjuvant trastuzumab therapy and independent prognostic value for HER2-positive breast cancer.

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m6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment

Journal of Oncology ◽

10.1155/2021/9987376 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Fei Liu ◽

Xiaopeng Yu ◽

Guijin He

Keyword(s):

Breast Cancer ◽

Tumor Microenvironment ◽

Immune Cell ◽

Principal Component ◽

Enrichment Analysis ◽

Genomic Variation ◽

Gene Set Enrichment Analysis ◽

Single Sample ◽

Gene Set Enrichment ◽

Gene Set

Background. We analyzed the n6-methyladenosine (m6A) modification patterns of immune cells infiltrating the tumor microenvironment of breast cancer (BC) to provide a new perspective for the early diagnosis and treatment of BC. Methods. Based on 23 m6A regulatory factors, we identified m6A-related gene characteristics and m6A modification patterns in BC through unsupervised cluster analysis. To examine the differences in biological processes among various m6A modification modes, we performed genomic variation analysis. We then quantified the relative infiltration levels of different immune cell subpopulations in the tumor microenvironment of BC using the CIBERSORT algorithm and single-sample gene set enrichment analysis. Univariate Cox analysis was used to screen for m6A characteristic genes related to prognosis. Finally, we evaluated the m6A modification pattern of patients with a single BC by constructing the m6Ascore based on principal component analysis. Results. We identified three different m6A modification patterns in 2128 BC samples. A higher abundance of the immune infiltration of the m6Acluster C was indicated by the results of CIBERSORT and the single-sample gene set enrichment analysis. Based on the m6A characteristic genes obtained through screening, the m6Ascore was determined. The BC patients were segregated into m6Ascore groups of low and high categories, which revealed significant survival benefits among patients with low m6Ascores. Additionally, the high-m6Ascore group had a higher mutation frequency and was associated with low PD-L1 expression, and the m6Ascore and tumor mutation burden showed a positive correlation. In addition, treatment effects were better in patients in the high-m6Ascore group. Conclusions. In case of a single patient with BC, the immune cell infiltration characteristics of the tumor microenvironment and the m6A methylation modification pattern could be evaluated using the m6Ascore. Our results provide a foundation for improving personalized immunotherapy of BC.

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Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer

Cancer Cell International ◽

10.1186/s12935-019-0986-8 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 17

Author(s):

Jingbo Sun ◽

Jingzhan Huang ◽

Jin Lan ◽

Kun Zhou ◽

Yuan Gao ◽

...

Keyword(s):

Breast Cancer ◽

Bone Metastasis ◽

Western Blot ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Free Survival ◽

Gene Set ◽

Oncomine Database ◽

Kaplan Meier

Abstract Background Centromere Protein F (CENPF) associates with the centromere–kinetochore complex and influences cell proliferation and metastasis in several cancers. The role of CENPF in breast cancer (BC) bone metastasis remains unclear. Methods Using the ONCOMINE database, we compared the expression of CENPF in breast cancer and normal tissues. Findings were confirmed in 60 BC patients through immunohistochemical (IHC) staining. Microarray data from GEO and Kaplan–Meier plots were used analyze the overall survival (OS) and relapse free survival (RFS). Using the GEO databases, we compared the expression of CENPF in primary lesions, lung metastasis lesions and bone metastasis lesions, and validated our findings in BALB/C mouse 4T1 BC models. Based on gene set enrichment analysis (GSEA) and western blot, we predicted the mechanisms by which CENPF regulates BC bone metastasis. Results The ONCOMINE database and immunohistochemical (IHC) showed higher CENPF expression in BC tissue compared to normal tissue. Kaplan–Meier plots also revealed that high CENPF mRNA expression correlated to poor survival and shorter progression-free survival (RFS). From BALB/C mice 4T1 BC models and the GEO database, CENPF was overexpressed in primary lesions, other target organs, and in bone metastasis. Based on gene set enrichment analysis (GSEA) and western blot, we predicted that CENPF regulates the secretion of parathyroid hormone-related peptide (PTHrP) through its ability to activate PI3K–AKT–mTORC1. Conclusion CENPF promotes BC bone metastasis by activating PI3K–AKT–mTORC1 signaling and represents a novel therapeutic target for BC treatment.

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Cross-Platform Microarray Data Integration Combining Meta-Analysis and Gene Set Enrichment Analysis

Bioinformatics ◽

10.4018/978-1-4666-3604-0.ch031 ◽

2013 ◽

pp. 570-585

Author(s):

Jian Yu ◽

Jun Wu ◽

Miaoxin Li ◽

Yajun Yi ◽

Yu Shyr ◽

...

Keyword(s):

Microarray Data ◽

Molecular Mechanisms ◽

Meta Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Gene Expression Studies ◽

Microarray Datasets ◽

Pathway Level

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

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