Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

Abstract

BackgroundThe distinct types of hematological malignancies have different biological mechanisms and prognoses. For instance, myelodysplastic syndrome (MDS) is generally indolent and low risk; however, it may transform into acute myeloid leukemia (AML), which is much more aggressive.MethodsWe develop a novel network analysis approach that uses expression of eigengenes to delineate the biological differences between these two diseases.ResultsWe find that specific genes in the extracellular matrix pathway are underexpressed in AML. We validate this finding in three ways: (a) We train our model on a microarray dataset of 364 cases and test it on an RNA Seq dataset of 74 cases. Our model showed 95% sensitivity and 86% specificity in the training dataset and showed 98% sensitivity and 91% specificity in the test dataset. This confirms that the identified biological signatures are independent from the expression profiling technology and independent from the training dataset.(b) Immunocytochemistry confirms that MMP9, an exemplar protein in the extracellular matrix, is underexpressed in AML. (c) MMP9 is hypermethylated in the majority of AML cases (n=194, Welch’s t-test p-value <10−138), which complies with its low expression in AML.Our novel network analysis approach is generalizable and useful in studying other complex diseases (e.g., breast cancer prognosis). We implement our methodology in the Pigengene software package, which is publicly available through Bioconductor.ConclusionsEigengenes define informative biological signatures that are robust with respect to expression profiling technology. These signatures provide valuable information about the underlying biology of diseases, and they are useful in predicting diagnosis and prognosis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-017-0253-6) contains supplementary material, which is available to authorized users.

Understanding the extracellular matrix in acute myeloid leukemia

“…They employed a sophisticated network analysis to identify the architecture of active gene regulatory networks in AML and found that the regulation of ECM modules is the most significant feature of AML transcriptional profile. 12 Of particular interest is the downregulation of matrix metalloproteinase 9 (MMP9) observed in both Foroushani et al's and our studies; a finding in contrast with the general idea that MMP9 is highly expressed in AML. 13 Yet, our systematic approach suggests a possible explanation to this.…”

C3HeB/FeJ Mice mimic many aspects of gene expression and pathobiological features of human hepatocellular carcinoma

“…Analysis of the gene set with HumanMine showed an unexpectedly high number of genes on chromosome 1 (Figure C). We identified the homologues of the genes of Module 5 in mouse and inferred the corresponding eigengene without changing the weights (Supplemental Note 2) . The inferred eigengene in the mouse data differentiated most mouse HCC and normal liver samples, but a few samples clustered with the wrong group (adjusted P ‐value = 0.001; Figure D).…”

“…| 315 of Module 5 in mouse and inferred the corresponding eigengene without changing the weights (Supplemental Note 2). 23 The inferred eigengene in the mouse data differentiated most mouse HCC and normal liver samples, but a few samples clustered with the wrong group (adjusted P-value = 0.001; Figure 6D). Therefore, this gene module was not as accurate for distinguishing tumor versus normal liver in C3HeB/FeJ mice.…”

“…Abdominal hair was removed by shaving then application of Nair™. The liver was visualized using the RVM716 scanhead (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) with the VisualSonics Vevo 770 highresolution device. Images of the largest diameter of tumors in the axial and sagittal planes were captured.…”

Towards integrated oncogenic marker recognition through mutual information‐based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

“…We have obtained several literature evidences [87,88] suggesting that there is association between PROS1 and the leukemia. In addition, we obtain several leukemia-related KEGG pathways (such as hsa04610: complement and coagulation cascades [89] having p- value = 5.68E-05) as well as Gene-Ontology-terms (viz., GO:BPs of negative regulation of wound healing (GO:0061045) [90,91] having p -value = 2.19E-09, regulation of response to wounding (GO:1903034) [90] having p -value = 2.78E-09, negative regulation of blood coagulation (GO:0030195) [92] ( p -value = 2.21E-08); GO:CCs of extracellular space (GO:0005615) [83] having p -value = 1.49E-33, extracellular region (GO:0005576) [93] having p -value = 3.91E-07, etc.) that are attached with PROS1.…”