ArticleComputational and mathematical methods in medicine2022
Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Tumor Hypoxia: How Conventional Histology Is Reshaped in Breast Carcinoma.International journal of molecular sciences · 2025Review
- The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Cancer.Breast cancer (Dove Medical Press) · 2025Article
- Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer.Frontiers in immunology · 2025Article
- Retraction Note: The role of fibroblast activation protein in progression and development of osteosarcoma cells.Clinical and experimental medicine · 2023Article
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Authors and funding
12 authors.
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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.
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