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Aberrant glycosylation in tumours stem from altered glycosyltransferase (GT) gene expression but can the expression profiles of these signature genes be used to classify cancer types and lead to cancer subtype discovery? The differential structural changes to cellular glycan structures are predominantly regulated by the expression patterns of GT genes and are a hallmark of neoplastic cell metamorphoses. We found that the expression of 210 GT genes taken from 1893 cancer patient samples in The Cancer Genome Atlas (TCGA) microarray data are able to classify six cancers; breast, ovarian, glioblastoma, kidney, colon and lung. The GT gene expression profiles are used to develop cancer classifiers and propose subtypes. The subclassification of breast cancer solid tumour samples illustrates the discovery of subgroups from GT genes that match well against basal-like and HER2-enriched subtypes and correlates to clinical, mutation and survival data. This cancer type glycosyltransferase gene signature finding provides foundational evidence for the centrality of glycosylation in cancer.

The family Rosaceae includes a range of important fruit trees, most of which have the S-RNase-based self-incompatibility (SI). Several models have been developed to explain how pollen (SLF) and pistil (S-RNase) components of the S-locus interact. It was discovered in 2010 that additional SLF proteins are involved in pollen specificity, and a Collaborative Non-Self Recognition model has been proposed for SI in Solanaceae; however, the validity of such model remains to be elucidated for other species. The results of this study support the divergent evolution of the S-locus genes from two Rosaceae subfamilies, Prunoideae/Amygdaloideae and Maloideae, The difference identified in the selective pressures between the two lineages provides evidence for positive selection at specific sites in both the S-RNase and the SLF proteins. The evolutionary findings of this study support the role of multiple SLF proteins leading to a Collaborative Non-Self Recognition model for SI in the Maloideae. Furthermore, the identification of the sites responsible for SI specificity determination and the mapping of these sites onto the modelled tertiary structure of ancestor proteins provide useful information for rational functional redesign and protein engineering for the future engineering of new functional alleles providing increased diversity in the SI system in the Maloideae.

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