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DOI: 10.1055/s-0043-1772459
Expression of CD44 in Head and Neck Squamous Cell Carcinoma—An In-Silico Study
Funding None.Abstract
Introduction CD44, a multistructural and multifunctional transmembrane glycoprotein, is a promising cancer stem cell (CSC) marker that regulates the properties of CSCs, including self-renewal, tumor initiation, and metastasis, and confers resistance to chemotherapy and radiotherapy. The aim of the present study was to evaluate the gene and protein expression of CD44 and explore its prognostic value in head and neck squamous cell carcinoma (HNSCC).
Methodology The present observational study employs computational tools for analysis. The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma dataset (520 primary HNSCC and 44 normal tissues) from the University of Alabama at Birmingham Cancer platform was used to study the association of CD44 mRNA transcript levels with various clinicopathological characteristics of HNSCC including age, gender, tumor grade, tumor stage, human papillomavirus (HPV) status, p53 mutation status, and overall survival. The CD44 protein expression in HNSCC and normal tissues was ascertained using the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium Head-and-Neck cancer dataset (108 primary HNSCC and 71 normal tissues).
Results CD44 mRNA transcript and protein expression levels were significantly higher in HNSCC tissues than in normal tissues, and high CD44 expression was correlated with poor survival. CD44 was upregulated in Stage 1 and Grade 2 HNSCC compared with other stages and grades. Overexpression of CD44 was observed in HPV-negative and TP53-positive mutant status in HNSCC.
Conclusion The pleiotropic roles of CD44 in tumorigenesis urge the need to explore its differential expression in HNSCC. The study concludes that CD44 can be a potential diagnostic and prognostic biomarker for HNSCC and offer new molecular targets for CD44-targeted therapy for cancer management.
Publication History
Article published online:
16 August 2023
© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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