Dysregulated module approach identifies disrupted genes and pathways associated with acute myelocytic leukemia

To identify disrupted genes and pathways involved in acute myelocytic leukemia (AML) by systematically tracking the dysregulated modules across normal and AML conditions.

In this study, we firstly integrated the protein interaction data and expression profiles to infer and reweight the normal and AML networks using Pearson correlation coefficient (PCC). Next, clustering-based on maximal cliques (CMC) approach and a maximum weight bipartite matching method were implemented to infer the condition-specific modules and capture the disturbed modules, respectively, from two conditional networks. Then, the gene compositions and functional enrichment analysis were performed to identify the dysregulated genes and pathways. Finally, reverse transcription polymerase chain reaction (RT-PCR) was implemented to study the expression level of several key genes in AML patients.

In two conditional-specific networks, universal changes of gene correlations were revealed, making the differential correlation density among disrupted module pairs. In this work, a total of 84 altered modules were identified by comparing modules in normal and AML networks. Functional enrichment analysis showed that genes in altered modules mainly involved in cell cycle, nucleic acids and cancer signaling process, and differentially expressed genes (DEGs) and changed gene correlations were mainly participated in natural killer cell-mediated cytotoxicity and acute myeloid leukemia pathway. The key genes, such as MYC, EGFR, MAPK1 and CCNA1, were all significantly differentially expressed in AML patients.

This module approach effectively identifies dysregulated pathways and genes associated with AML. The considerable differences of gene correlations yield to these dysfunctional modules, and the coordinated disruption of these very modules contributes to leukemogenesis.