Screening and Predicting Multi-omics T-ALL Core Genes Based on PU Learning

T-cell acute lymphoblastic leukemia (T-ALL) is a malignant neoplastic disease. Accurate identification of core genes helps to explore the pathogenesis of T-ALL and develop relevant targeted drugs. In this paper, we first screened the RNA-seq, CTCF ChIP-seq and DNA methylation datasets of T-ALL for intersecting differentially expressed genes (DEGs) using bioinformatics software. As such, candidate genes screening is transformed into a semi-supervised classification problem given all known T-ALL related genes. It is clear that there are no labelled negative samples but a few labelled positive samples in the dataset, motivating us to employ the PU bagging method based on Positive-unlabeled (PU) learning to discover the T-ALL related candidate genes, in which a multi-layer perceptron (MLP) classifier was employed to accomplish the classification task. On this ground, a protein-protein interaction (PPI) network was built with the candidate genes, and core genes were screened. Finally, the core genes were subjected to GO and KEGG functional enrichment analysis, search of CTD and review of relevant literature to validate the proposed method. The validation results showed that the proposed method is able to effectively predict T-ALL related core genes, and all core genes have the potential to become candidates in T-ALL biomarker studies in the future.