Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer

Author:

Wang Hui1,Guo Yu2,Zhang Peipei3,Lin Zhijun4,Yang Di5,Chen Jiaohong3ORCID,Li Zhanzhan4,Zhang Chi6,Yang Haoyu7,Yan Binghui7,Han Zhimin1ORCID,Tian Chuntao8ORCID

Affiliation:

1. Department of Thyroid and Breast Surgery, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

2. Personnel section, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

3. Department of Radiotherapy, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

4. Department of Medical Image Center, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

5. Department of Pathology, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

6. Community Service Center, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

7. Department of Ultrasonography, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

8. Department of Oncology, Sanmenxia Central Hospital of Henan Province, 472000 Sanmenxia, Henan, China

Abstract

Background. Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. Methods. We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Results. Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. Conclusion. In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.

Funder

Henan Medical Science and Technology Research Plan

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation,General Medicine

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