Proteins with at least one carbohydrate recognition domain are lectins that can identify and
reversibly interact with glycan moiety of glycoconjugates or a soluble carbohydrate. It has been
proved that lectins can play various vital roles in mediating signal transduction, cell-cell recognition
and interaction, immune defense, and so on. Most organisms can synthesize and secret lectins. A portion
of lectins closely related to diverse cancers, called cancerlectins, are involved in tumor initiation,
growth and recrudescence. Cancerlectins have been investigated for their applications in the laboratory
study, clinical diagnosis and therapy, and drug delivery and targeting of cancers. The identification
of cancerlectin genes from a lot of lectins is helpful for dissecting cancers. Several cancerlectin prediction
tools based on machine learning approaches have been established and have become an excellent
complement to experimental methods. In this review, we comprehensively summarize and expound
the indispensable materials for implementing cancerlectin prediction models. We hope that this review
will contribute to understanding cancerlectins and provide valuable clues for the study of
cancerlectins. Novel systems for cancerlectin gene identification are expected to be developed for clinical
applications and gene therapy.
Keywords: Cancerlectin, Non-cancerlectin, Feature extraction and selection, Machine learning method, PSSM, Prosite.
Rights & PermissionsPrintExport