Differential Regulation of Lacto-/Neolacto- Glycosphingolipid Biosynthesis Pathway Reveals Transcription Factors as Potential Candidates in Triple-Negative Breast Cancer

Abstract

Triple-negative breast cancer (TNBC) is an aggressive breast cancer with limited treatment options. Glycosylation has been implicated in cancer development, but TNBC-specific glycosylation pathways have not been examined. Here, we applied bioinformatic analyses on public datasets to discover TNBC-specific glycogenes and pathways, as well as their upstream regulatory mechanisms. Unsupervised clustering of 345 glycogene expressions in breast cancer datasets revealed a relative homogenous expression pattern in basal-like TNBC subtype. Differential expression analyses of the 345 glycogenes between basal-like TNBC (hereafter termed TNBC) and other BC subtypes, or normal controls, revealed 84 differential glycogenes in TNBC. Pathway enrichment showed two common TNBC-enriched pathways across all three datasets, cell cycle and lacto-/neolacto- glycosphingolipid (GSL) biosynthesis, while a total of four glycosylation-related pathways were significantly enriched in TNBC. We applied a selection criterion of the top 50% differential anabolic/catabolic glycogenes in the enriched pathways to define 34 TNBC-specific glycogenes. The lacto-/neolacto- GSL biosynthesis pathway was the most highly enriched, with seven glycogenes all up-regulated in TNBC. This data led us to investigate the hypothesis that a common upstream mechanism in TNBC up-regulates the lacto-/neolacto-GSL biosynthesis pathway. Using public multi-omic datasets, we excluded the involvement of copy-number alteration and DNA methylation, but identified three transcription factors (AR, GATA3 and ZNG622) that each target three candidate genes in the lacto-/neolacto- GSL biosynthesis pathway. Interestingly, a subset of TNBC has been reported to express AR and GATA3, and AR antagonists are being trialed for TNBC. Our findings suggest that AR and GATA3 may contribute to TNBC via GSL regulation, and provide a list of candidate glycogenes for further investigation.

Bridging Glycomics and Genomics: New Uses of Functional Genetics in the Study of Cellular Glycosylation

“…Systematically dissecting transcriptomic data across a broad range of tissues and disease states creates opportunities for identifying cell and tissue-specific regulators of glycosylation. In one work, for example, Zeng et al analyzed TCGA data to identify glycosphingolipid biosynthesis genes that are differentially expressed in triple negative breast cancer (TNBC) patients ( Zeng et al, 2021 ). Further dissection of ChIP-seq databases identified the transcription factors AR and GATA3 as putative regulators of glycogene expression in TNBC ( Zeng et al, 2021 ).…”

“…In one work, for example, Zeng et al analyzed TCGA data to identify glycosphingolipid biosynthesis genes that are differentially expressed in triple negative breast cancer (TNBC) patients ( Zeng et al, 2021 ). Further dissection of ChIP-seq databases identified the transcription factors AR and GATA3 as putative regulators of glycogene expression in TNBC ( Zeng et al, 2021 ).…”