Abstract:

Objective To screen out the aging genes that are significantly related to the prognosis of breast cancer through bioinformatics. Method Clinical data and mRNA sequencing data were downloaded from the cancer genome atlas (TCGA) database collected by the National Cancer Center from September, 2010 to June, 2015. Aging genes were downloaded from the Aging Atlas database. Differential express genes between the normal tissue and the cancer tissue were compared. Single factor Cox and Lasso regression were applied to obtain prognostic-related aging genes. Then the signature was constructed, the patients were divided into high-risk group and low-risk group with the median of risk coefficient as the cut-off value. Univariate and multivariate regression were used to identify the independent factor, then Nomogram was constructed. Gene set enrichment analysis (GSEA) software was used for the functional enrichment analysis of key genes. Result 119 differential aging genes and 10 prognostic-related genes were obtained, including 2 tumor suppressor genes(NRG1, IL2RG) and 8 cancer-promoting genes (EIF4EBP1,MMP1, PLAU, MMP13, RAD51, FGF7, DLL3, IGFBP1). The 10-aging-gene signature was constructed by Cox. The prognosis between the high risk and low risk group was significantly differently. The Nomogram showed good performance in predicting the overall survival of breast cancer patient. The GSEA showed the high-risk group was significantly enriched in signal pathways such as cell cycle and homologous recombination. The genes of lowrisk group was significantly enriched in the JAK-STAT signaling pathway, cytokine-receptor-interaction pathway. Conclusion The signature based on aging related genes had a good performance on predicting the prognosis of breast cancer patients.

Key words: Breast cancer, Bioinformatics, Aging genes, Prognosis