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Integrated Profiling Identifies CCNA2 as a Potential Biomarker of Immunotherapy in Breast Cancer

Authors Wang Y, Zhong Q, Li Z, Lin Z, Chen H, Wang P

Received 23 December 2020

Accepted for publication 26 March 2021

Published 9 April 2021 Volume 2021:14 Pages 2433—2448

DOI https://doi.org/10.2147/OTT.S296373

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Takuya Aoki


Yichao Wang,1,* Qianyi Zhong,1,* Zhaoyun Li,1 Zhu Lin,2 Hanjun Chen,1 Pan Wang1

1Department of Clinical Laboratory Medicine, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, 318000, People’s Republic of China; 2Department of Ultrasound, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, 318000, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Pan Wang
Department of Clinical Laboratory Medicine, Taizhou Central Hospital (Taizhou University Hospital), No. 999 Donghai Road, Jiaojiang District, Taizhou, Zhejiang, 318000, People’s Republic of China
Tel +86 13586106875
Email [email protected]

Introduction: Breast cancer is the main reason for cancer-related deaths in women and the most common malignant cancer among women. In recent years, immunosuppressive factors have become a new type of treatment for cancer. However, there are no effective biomarkers for breast cancer immunotherapy. Therefore, exploring immune-related biomarkers is presently an important topic in breast cancer.
Methods: Gene expression profile data of breast cancer from The Cancer Genome Atlas (TCGA) was downloaded. Scale-free gene co-expression networks were built with weighted gene co-expression network analysis. The correlation of genes was performed with Pearson’s correlation values. The potential associations between clinical features and gene sets were studied, and the hub genes were screened out. Gene Ontology and gene set enrichment analysis were used to reveal the function of hub gene in breast cancer. The gene expression profiles of GSE15852, downloaded from the Gene Expression Omnibus database, were used for hub gene verification. In addition, candidate biomarkers expression in breast cancer was studied. Survival analysis was performed using Log rank test and Kaplan–Meier. Immunohistochemistry was used to analyze the expression of CCNA2.
Results: A total of 6 modules related to immune cell infiltration were identified via the average linkage hierarchical clustering. According to the threshold criteria (module membership > 0.9 and gene significance > 0.35), a significant module consisting of 13 genes associated with immune cells infiltration were identified as candidate hub genes after performed with the human protein interaction network. And 3 genes with high correlation to clinical traits were identified as hub genes, which were negatively associated with the overall survival. Among them, the expression of CCNA2 was increased in metastatic breast cancer compare with non-metastatic breast cancer, who underwent immunotherapy. Immunohistochemistry results showed that CCNA2 expression in carcinoma tissues was elevated compared with normal control.
Discussion: CCNA2 identified as a potential immune therapy marker in breast cancer, which were first reported here and deserved further research.

Keywords: breast cancer, immune infiltration, molecular markers, immunotherapy

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