Altered gene expression in glycolysis–cholesterol synthesis axis correlates with outcome of triple-negative breast cancer. Issue 5 (March 2021) - Zhong, Peng-Cheng; Shu, Rong; Wu, Hui-Wen; Liu, Zhi-Wen; Shen, Xiao-Ling; Hu, Ying-Jie
Identification of molecular subtypes of clinically resectable triple-negative breast cancer (TNBC) is of great importance to achieve better clinical outcomes. Inter- and intratumor metabolic heterogeneity improves cancer survival, and the interaction of various metabolic pathways may affect treatment outcome of TNBC. We speculated that TNBC can be categorized into prognostic metabolic subtype according to the expression changes of glycolysis and cholesterol synthesis. The genome, transcriptome, and clinical data were downloaded from the Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium and subsequently analyzed by integrated bioinformatics methods. Four subtypes, namely, glycolytic, cholesterogenic, quiescent, and mixed, were classified according to the normalized median expressions of the genes involved in glycolysis and cholesterol synthesis. In the four subtypes, the cholesterogenic type was correlated with the shortest median survival (log rank P = 0.044), while patients with high-expressed glycolytic genes tended to have a longer survival. Tumors with PIK3CA amplification and dynein axonemal heavy chain 2 deletion exhibited higher expressions of cholesterogenic genes than other mutant oncogenes. The expressions of mitochondrial pyruvate carrier MPC1 and MPC2 were the lowest in quiescent tumor, and MPC2 expression was higher in cholesterogenic tumor compared with glycolytic or quiescent tumor ( t -test P < 0.001). Glycolytic and cholesterogenic gene expressions were related to the expressions of prognostic genes in some other types of cancers. Classification of glycolytic and cholesterogenic pathways according to metabolic characteristics provides a new understanding to previously identified subtypes of TNBC and could improve personalized treatments based on tumor metabolic profiles.