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Year : 2022  |  Volume : 17  |  Issue : 5  |  Page : 508-526

Expression analysis of HIF-3α as a potent prognostic biomarker in various types of human cancers: a case of meta-analysis

1 Department of Tissue Engineering, Najafabad Branch, Islamic Azad University, Najafabad, I.R. Iran
2 Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran

Correspondence Address:
Hajar Sirous
Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
I.R. Iran
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/1735-5362.355210

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Background and purpose: Hypoxia-inducible factors (HIFs) are transcription factors that get activated and stabilized in the heterodimerized form under hypoxic conditions. many studies have reported the importance of the HIF-1α and HIF-2α activity in biological pathways of hypoxic cancer cells. However, the importance of HIF-3α in a variety of cancers remains unknown. Experimental approach: The expression profile of 13 different types of cancer samples from the Cancer Genome Atlas (TCGA) database were subjected to normalization, and differential gene expression analysis was performed using computational algorithms by R programming. Receiver operating characteristic tests and survival analyses were carried out for HIF-α subunits in different cancers. Findings / Results: The expression status of HIF-3α was notably less in all cancer samples in contrast to their adjacent normal tissues. The expression degree of HIF-1α varied among distinct types of cancer and the expression degree of HIF-2α was lower in nearly all types of cancers. HIF-3α had very weak diagnostic potential, while HIF-2α had better diagnostic potential in most types of cancers compared to HIF-1α. Patients who had a higher level of HIF-3α had better survival, while the higher expression level of HIF-1α and HIF-2α were associated with worse survival in many types of cancers. Conclusion and implications: Our findings showed that each HIF-α subunit had a unique heterogeneous expression pattern in different classes of cancers. The expression level of each HIF-α subunit correlated differently with the stages, tumor sizes, and survival rate of patients from different classes of TCGA cancers.

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