Xiaofeng Dai1, Rong Ma1,2, Xijiang Zhao3, Fengfeng Zhou4. 1. Wuxi School of Medicine, Jiangnan University, Wuxi 214122, PR China. 2. Department of Biochemistry, School of Medicine, McGill University, Montreal H3A0G4, Canada. 3. Affiliated Hospital of Jiangnan University, Wuxi 214062, PR China. 4. BioKnow Health Informatics Lab, College of Computer Science & Technology, & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Jilin, Changchun 130012, PR China.
Abstract
Aim: Triple-negative breast cancers (TNBCs) contain a higher percentage of breast cancer stem cells (BCSCs) than the other subtypes and lack effective yet safe-targeted therapies. We would like to unveil genes relevant to the therapeutic control of breast cancer stemness at the epigenetic level. Methods: We sequenced the transcriptome of BCSCs isolated from TNBCs, identified genes differentially expressed in these cells and subjected to DNA methylation and established the Bayesian network as well as interactions out of them. Results & conclusion: We presented a core epigenetic BCSC gene panel consisting of eight genes that can be used for BCSCs and TNBCs identification, and revealed the dominant roles of FOXA1 and GATA3 in orchestrating breast cancer heterogeneity and stemness.
Aim: Triple-negative breast cancers (TNBCs) contain a higher percentage of breast cancer stem cells (BCSCs) than the other subtypes and lack effective yet safe-targeted therapies. We would like to unveil genes relevant to the therapeutic control of breast cancer stemness at the epigenetic level. Methods: We sequenced the transcriptome of BCSCs isolated from TNBCs, identified genes differentially expressed in these cells and subjected to DNA methylation and established the Bayesian network as well as interactions out of them. Results & conclusion: We presented a core epigenetic BCSC gene panel consisting of eight genes that can be used for BCSCs and TNBCs identification, and revealed the dominant roles of FOXA1 and GATA3 in orchestrating breast cancer heterogeneity and stemness.
Entities:
Keywords:
Bayesian network; DNA methylation; breast cancer stemness; cancer epigenetics; gene expression; triple-negative breast cancer