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Updated: Oct 4, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer
Limin Wei1, Yukun Wang1, Dan Zhou1
1Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital, College of Clinical Medicine, Medical College of Henan University of Science and Technology, Luoyang, China.
Background:
Despite recent advances in screening, treatment, and survival, breast cancer remains the most invasive cancer in women. The development of novel diagnostic and therapeutic markers for breast cancer may provide more information about its pathogenesis and progression.
Methods:
We obtained GSE86374 micro-expression matrix chip data from the Gene Expression Omnibus (GEO) database consisting of 159 samples (124 normal samples and 35 breast cancer samples). The language was then used to perform data processing and differential expression analysis. For all differentially expressed genes (DEGs), "FDR <0.01 and |logFC| ≥1" were selected as thresholds.
Results:
In this study, 173 up-regulated genes and 143 down-regulated genes were selected for GO and KEGG enrichment analysis. These genes are also significantly enriched in the KEGG pathway, including phenylalanine metabolism, staphylococcus aureus infection, and the PPAR signaling pathway. The survival and prognosis of the selected eight key genes (DLGAP5, PRC1, TOP2A, CENPF, RACGAP1, RRM2, PLK1, and ASPM) were analyzed by the Kaplan-Meier plotter database.
Conclusions:
Eight hub genes and pathways closely related to the onset and progression of breast cancer were identified. We found that the PPAR signaling pathway, especially PPARγ, plays an important role in breast cancer and suggest this pathway be the subject of further research.
