Identification of potential crucial genes associated with breast cancer using bioinformatics analysis and experimental verification

Abstract

AbstractIn this study, we identified a total of 492 DEGs, including 176 up-regulated and 316 down-regulated DEGs. GO analysis showed that the up-regulated DEGs are mainly involved in cell division, nucleus and protein binding. The down-regulated DEGs mainly involve immune response, extracellular exosome and calcium ion binding. Top five enriched pathways obtained in the KEGG pathway analysis are pathways in cancer, cytokine-cytokine receptor interaction, focal adhesion, the PI3K-akt signaling pathway and ECM-receptor interaction. Top 10 up-regulated hub genes identified from the PPI network are AURKA, CDC6, CCNA2, CDCA8, NUSAP1, CDK1, CCNB1, CCNB2, UBE2C, HMMR. The top 10 down-regulated hub genes are IGF1, JUN, FGF2, CXCL12, KIT, PTGS2, LEP, EGF, EGR1, FOS. Survival analysis showed that the expression levels of WIF1 (P = 0.019) and HMMR (P = 0.027) were correlated with the prognosis of patients with breast cancer. In addition, gene expression and methylation analysis showed that COL11A1 is highly expressed and hyper-methylation. MMP1 is highly expressed and hypo-methylation. SFRP1, WIF1 is low expressed and hyper-methylation in breast cancer. In terms of tumor purity and immune cell infiltration analysis, Interestingly, it is found that HMMR makes a strong connection with B Cell, CD8+T Cell, neutrophil, dendritic cell (P <0.05). MMP1 was negtively associated with tumor purity. The use of bioinformatics can effectively analyze the data of the gene chip, obtain the inherent information of the organism, and provide the basis for the next experiment. This study identifies key genes and pathways in breast cancer that will advance our understanding of molecular mechanisms.