Analysis of AML genes in dysregulated molecular networks

Abstract

Background: Identifying disease causing genes and understanding their molecular mechanisms are essential to developing effective therapeutics. Thus, several computational methods have been proposed to prioritize candidate disease genes by integrating different data types, including sequence information, biomedical literature, and pathway information. Recently, molecular interaction networks have been incorporated to predict disease genes, but most of those methods do not utilize invaluable disease-specific information available in mRNA expression profiles of patient samples.

Results: Through the integration of protein-protein interaction networks and gene expression profiles of acute myeloid leukemia (AML) patients, we identified subnetworks of interacting proteins dysregulated in AML and characterized known mutation genes causally implicated to AML embedded in the subnetworks. The analysis shows that the set of extracted subnetworks is a reservoir rich in AML genes reflecting key leukemogenic processes such as myeloid differentiation.

Conclusion: We showed that the integrative approach both utilizing gene expression profiles and molecular networks could identify AML causing genes most of which were not detectable with gene expression analysis alone due to the minor changes in mRNA level.

Figure 1

Schematic overview of the subnetwork identification. Schematic overview of the subnetwork identification. The mRNA expression levels of each gene were overlaid on its corresponding protein in the network and subnetworks whose combined activities across the patients have high perturbation score were searched. An activity level (a_kj) for a subnetwork M_k in j^th sample was defined as the mean expression levels with the square-root of the number of participating genes in the denominator. The perturbation score S(M_k) for the subnetwork was then calculated as the mean over the standard deviation of the activity levels across patients.

Figure 2

Examples of subnetworks containing known AML mutation genes. Nodes and links represent human proteins and protein interactions, respectively. The color of each node shows the degree of mRNA level change in AML patients. Known AML mutation genes are marked with the diamond shape.

Figure 3

The enrichment of AML mutation genes in subnetworks. 18 out of 62 AML genes (29.03%) were found in 269 subnetworks including 859 genes, and their enrichment was significant (p-value P = 7.14e-6) through the hypergeometric test (the probability of 18 AML genes out of all 62 are found in the subnetworks including total 859 genes out of 9142 genes in the whole network). In contrast, only two AML genes (FLT3, JAK3) (3.23%) were found among 859 top differentially expressed genes in their mRNA levels (P = 0.04)

Figure 4

(a) Degrees and (b) mRNA expression changes of AML genes. Each figure shows node degrees and magnitudes of differential expression (DES) for AML-causing mutation genes found in subnetworks (AML_Network), all known AML mutation genes (AML), and all genes in the whole network. The bottom and top of each box are first and third quartiles, and the band near the middle of the box is the median. Whiskers extend to at most 1.5 times the inter-quartile range. Beyond the whiskers, all outliers are shown in open circles. The statistical significances for differences between two groups of genes (e.g. AML_Network vs. All genes) measured by non-parametric Wilcoxon rank-sum test are denoted below the labels of gene groups.