Bioinformatic analysis reveals the key pathways and genes in early-onset breast cancer

Early-onset breast cancer is the most prevalent cancer in the female. To identify the differentially expressed genes and the key signaling pathways in early-onset breast cancer, we have carried out the bioinformatic analysis of an RNA array dataset in the GEO database, GSE109169, which was acquired...

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Bibliographic Details
Published in:Medical oncology (Northwood, London, England) London, England), 2018-05, Vol.35 (5), p.67-8, Article 67
Main Authors: Cui, Chuanlong, Li, Lun, Zhen, Jing
Format: Article
Language:English
Subjects:
Breast cancer
Hematology
Internal Medicine
Medical prognosis
Medicine
Medicine & Public Health
Metabolism
Oncology
Original Paper
Pathology
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Summary:Early-onset breast cancer is the most prevalent cancer in the female. To identify the differentially expressed genes and the key signaling pathways in early-onset breast cancer, we have carried out the bioinformatic analysis of an RNA array dataset in the GEO database, GSE109169, which was acquired from early-onset breast cancer patient. A total of 118 differentially expressed genes in early-onset breast cancer were significantly changed compared with that in adjacent normal tissues. Most of these genes are classified into three categories: signaling molecule, enzyme modulator, and hydrolase. Gene ontology terms reveal that most of these genes are involved in cellular and metabolic processes, biological regulation, binding and catalytic activities, and receptor regulation. Protein–protein interaction network was constructed and has two highly enriched modules: one with up-regulated genes and the other with down-regulated genes. The singling pathways are mainly enriched in the cellular immune system, lipid metabolism and other types of metabolic pathways. Finally, we have plotted the Kaplan–Meier curves of two up-regulated and two down-regulated genes for the overall survival prediction in breast cancer. These results greatly expand the current view of early-onset breast cancer and shed light on the discovery of drug candidates and the improvement for the prognosis.
ISSN:1357-0560
1559-131X
DOI:10.1007/s12032-018-1130-7