An Integrative Computational Approach to Evaluate Genetic Markers for Chronic Lymphocytic Leukemia

Abstract Recent studies reported hundreds of genes linked to chronic lymphocytic leukemia (CLL). However, many of these candidate genes were lack of replication and results were not always consistent. Here, we proposed a computational workflow to curate and evaluate CLL-related genes. The method integrates large-scale literature knowledge data, gene expression data, and related pathways/network information for quantitative marker evaluation. Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using our approach, a scalable CLL genetic database was developed including CLL-related genes, pathways, diseases and information of supporting references. The CLL case/control classification supported the effectiveness of the four proposed metrics, which successfully identified nine well-studied CLL genes (i.e., TNF, BCL2, TP53, VEGFA, P2RX7, AKT1, SYK, IL4, and...