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

Md Shahedur Rahman, Polash Kumar Biswas, Subbroto Kumar Saha, Mohammad Ali Moni

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number7
Number of pages20
JournalNetwork Modeling Analysis in Health Informatics and Bioinformatics
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 2022

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