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Integration of metabolomic and transcriptomic data reveals metabolic pathway alteration in breast cancer and impact of related signature on survival
Author(s) -
Luo Xiao,
Yu Hong,
Song Yan,
Sun Tong
Publication year - 2019
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.27973
Subject(s) - kegg , transcriptome , metabolomics , biology , metabolic pathway , gene , breast cancer , gene expression profiling , gene expression , computational biology , cancer research , cancer , bioinformatics , genetics
Objective Breast cancer (BC) is a malignant tumor which threat to women's physical and mental health. However, the mechanism of metabolism alteration in BC remains unclear. This study is intended to figure out the relationship between the alternation of metabolism and the progression of BC. Methods In this study, metabolites of plasma in 60 BC patients and 40 healthy volunteers were detected using liquid chromatography mass spectrometer (LC‐MS). Transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed to enrich the pathways. Results A total of 97 metabolites have been identified and measured, of which 17 compounds exhibited the differential expression between tumor group and control group ( p < 0.05; FDR < 0.05). Metabolites set enrichment analysis (MSEA) displayed that there were 12 significantly enriched pathways in all. Through the KEGG database, 382 genes were found closely correlated with the altered metabolic pathways. TCGA and GEO transcriptomic profiling revealed that 5,018 genes significantly changed between tumor group and control group. Integrating these genes with the transcriptomic data from the corresponding KEGG data set, we identified most of the differential expressed genes were related to purine metabolism. A total of 28 different expression genes were hub genes, wherein AMPD1 and RRM2 were significantly effective in the prediction of survival of BC patients, with 0.04 and 0.02, respectively. Conclusions Combining with the transcriptomic and metabolomics data, we found that the dysregulation of purine metabolism pathway might affect the progression of BC.