Int. J. Environ. Res. Public Health 2012, 9 1942 Of the 202 AML patients, 21 were categorized with complex aberrant karyotype, while 181 were categorized with normal karyotype or other abnormalities. This differential karyotype was included in the subsequent analysis. To note, all samples were obtained from untreated patients at the time of diagnosis, as part of the collaborative Microarray Innovations in Leukemia (MILE) study [22,23]. Total RNA from bone marrow mononuclear cells was extracted and hybridized to microarrays, as described previously [22,24]. Gene expression data derived from probe set intensity signals from 202 AML and 69 non-leukemic patients were used. To identify genes that were differentially expressed in AML patients, statistical analyses were performed as follows. Data were filtered for removal of background (<abs [100]) in 20% of the samples. Differential expression was defined as a significant difference in transcript levels between AML versus non-leukemic patients, where three statistical requirements were set: (i) a fold change of ≥2.5 or ≤−2.5 (AML versus non-leukemic); (ii) p value <0.01 (ANOVA); and (iii) a false discovery rate corrected q-value <0.01. Analysis of variance (ANOVA) p values were calculated using Partek® Genomics SuiteTM software, version 6.5 [25]. To control the rate of false positives, q-values were calculated as the minimum “positive false discovery rate” that can occur when identifying significant hypotheses [26]. This criteria was set based on our published methods [27,28], where statistical stringency is employed to allow the identification of genes significantly associated with disease status, while allowing minimal false positives. The genes that met these statistical requirements were identified as significantly differentially expressed in AML patients, and represent the AML-associated gene expression signature. 2.2. Identifying AML-Associated miRNAs and Their Transcriptional Targets A set of miRNAs modulated in AML patients was identified through an extensive literature review. The miRNAs were selected based on their altered expression levels in AML versus normal cells and their relevance to leukemia development. The studies included were Bousquet et al. [29], Cammarata et al. [30], Garzon et al. [31], Han et al. [32], O’Connell et al. [33], and Wang et al. [34]. In order to understand the effects that the AML-associated miRNAs (n = 7) may cause at the gene expression level, computational predictions of the transcriptional targets of the AML-associated miRNAs were carried out. Here, TargetScanHuman [35] algorithms were employed to identify potential matches between the 3’-untranslated mRNA regions and miRNA seed sequences for each of the miRNAs [36]. The resulting predicted miRNA-mRNA interactions were filtered for the probability of preferentially conserved targeting (PCT) ≥0.5. This PCT filter controlled for background conservation across mammals by accounting for mutational biases, dinucleotide conservation rates, and individual untranslated region conservation rates [10]. 2.3. Network, Pathway, and Functional Enrichment Analysis Network analysis was performed to identify biological pathways that are targets for miRNA-mediated control in AML. For this analysis, a list of AML-associated genes predicted to be targeted by at least one of the AML-associated miRNAs was overlaid onto a global interaction network. Networks were algorithmically constructed based on connectivity, as enabled through Ingenuity Pathway Analysis [37]. Canonical pathways within the constructed networks were then identified. Over-represented pathways were defined as pathways that contain more AML-associated genes than