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Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis

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Abstract

Breast cancer is an expanding threat that leads to many women's death worldwide. Despite the improvement of the early detection methods and treatment, still, there is a high number of breast cancer mortality. To increase patient survival in breast cancer, identifying novel biomarkers is essential for therapeutics targets. The Glycophorin C (GYPC) gene is correlated with patient survival, which can be a possible biomarker for early detection in breast cancer progression. However, the expression of GYPC is not clearly defined in breast cancer. Here, we widely analyzed the expression pattern of GYPC in breast cancer and patient survival datasets through several bioinformatics tools. GYPC mRNA expression using ONCOMINE, GENT2, and GTX2 webs. Also, The co-expression profile of GYPC has been repossessed from Ma breast four datasets from Oncomine dataset. Our study revealed that mRNA expression of GYPC is strongly correlated with the survival of breast cancer patients, suggesting its role as a tumor suppressor. The downregulation of GYPC in breast cancer tissue is examined by promoter methylation and copy number alterations. The downregulation of GYPC expression was significantly correlated with high patient survival. Moreover, we performed pathway analysis via Enricher and gene ontology web using 20 positively correlated genes. Consequently, our analyzed data suggested that GYPC might be an essential therapeutics and prognostic biomarker in breast cancer.

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Source databases for KM plots include GEO, EGA, and TCGA

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Data availability

The authors declare that all the data supporting the findings of this study are available within the paper and supplementary file.

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Funding

MSR acknowledges that this research was supported by grants from the Research & Development Project from the Ministry of Science and Technology and Jashore University of Science and Technology, Bangladesh.

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Authors and Affiliations

  1. Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore-7408, Bangladesh

    Md. Shahedur Rahman

  2. Department of Stem Cell and Regenerative Biotechnology, Incurable Disease Animal Model & Stem Cell Institute (IDASI), Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul, 05029, Korea

    Polash Kumar Biswas & Subbroto Kumar Saha

  3. School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA

    Subbroto Kumar Saha

  4. WHO Collaborating Centre of eHealth, UNSW Digital Health, School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW, Australia

    Mohammad Ali Moni

  5. Bioinformatics and Microbial Biotechnology Laboratory, Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore-7408, Bangladesh

    Md. Shahedur Rahman

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  1. Md. Shahedur Rahman
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  2. Polash Kumar Biswas
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  3. Subbroto Kumar Saha
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  4. Mohammad Ali Moni
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Contributions

MSR conceived the idea, analyzed the data, and drafted the manuscript. PKB, SKS, and MAM reviewed the manuscript. All authors approved the final version of the manuscript.

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Correspondence to Md. Shahedur Rahman.

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This in silico study does not involve human, animal, plant, or clinical experiments. Thus, it does not require ethical approval.

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Rahman, M., Biswas, P.K., Saha, S.K. et al. Identification of glycophorin C as a prognostic marker for human breast cancer using bioinformatic analysis. Netw Model Anal Health Inform Bioinforma 11, 7 (2022). https://doi.org/10.1007/s13721-021-00352-0

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  • DOI: https://doi.org/10.1007/s13721-021-00352-0

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