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2000
Volume 31, Issue 5
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background: Breast cancer is the leading cause of cancer-related mortality among women worldwide. Advanced stages are usually obstinate with chemotherapy, resulting in a poor prognosis; however, they are treatable if diagnosed early. Objective: Identifying biomarkers that can detect cancer early or have therapeutic significance is imperative. Methods: Herein, a comprehensive bioinformatics-based transcriptomics study of breast cancer for identifying differentially expressed genes (DEGs), followed by a screening of potential compounds by molecular docking, was performed. Genome-wide mRNA expression data of breast cancer patients (n=248) and controls (n=65) were retrieved from the GEO database for meta-analysis. Statistically significant DEGs were used for enrichment analysis based on ingenuity pathway analysis and protein-protein network analysis. Results: A total of 3096 unique DEGs (965 up-regulated and 2131 down-regulated) were mapped as biologically relevant. The most upregulated genes were (survivin), , and the most downregulated genes were and . Transcriptomic and molecular pathway analyses identified /survivin as a significant DEG. Kinetochore metaphase signaling is recognized as a prominent dysregulated canonical pathway. Protein-protein interaction study revealed that KIF2C, KIF20A, KIF23, CDCA8, AURKA, AURKB, INCENP, CDK1, BUB1 and CENPA are BIRC5-associated proteins. Molecular docking was performed to exhibit binding interactions with multiple natural ligands. Conclusion: BIRC5 is a promising predictive marker and a potential therapeutic target in breast cancer. Further large-scale studies are required to correlate the significance of BIRC5 in breast cancer, leading to a step toward the clinical translation of novel diagnostic and therapeutic options.

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/content/journals/cmc/10.2174/0929867330666230516102017
2024-02-01
2025-05-13
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  • Article Type:
    Research Article
Keyword(s): biomarkers; Breast cancer; docking; pathway analysis; survivin; transcriptomics
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