Reproducible combinatorial regulatory networks elucidate novel oncogenic microRNAs in non-small cell lung cancer

Abstract

While previous studies reported aberrant expression of microRNAs (miRNAs) in non-small cell lung cancer (NSCLC), little is known about which miRNAs play central roles in NSCLC's pathogenesis and its regulatory mechanisms. To address this issue, we presented a robust computational framework that integrated matched miRNA and mRNA expression profiles in NSCLC using feed-forward loops. The network consists of miRNAs, transcription factors (TFs), and their common predicted target genes. To discern the biological meaning of their associations, we introduced the direction of regulation. A network edge validation strategy using three independent NSCLC expression profiling data sets pinpointed reproducible biological regulations. Reproducible regulation, which may reflect the true molecular interaction, has not been applied to miRNA-TF coregulatory network analyses in cancer or other diseases yet. We revealed eight hub miRNAs that connected to a higher proportion of targets validated by independent data sets. Network analyses showed that these miRNAs might have strong oncogenic characteristics. Furthermore, we identified a novel miRNA-TF co-regulatory module that potentially suppresses the tumor suppressor activity of the TGF-β pathway by targeting a core pathway molecule (TGFBR2). Follow-up experiments showed two miRNAs (miR-9-5p and miR-130b-3p) in this module had increased expression while their target gene TGFBR2 had decreased expression in a cohort of human NSCLC. Moreover, we demonstrated these two miRNAs directly bind to the 3 ′ untranslated region of TGFBR2. This study enhanced our understanding of miRNA-TF co-regulatory mechanisms in NSCLC. The combined bioinformatics and validation approach we described can be applied to study other types of diseases.

MicroRNA-130a and -130b enhance activation of hepatic stellate cells by suppressing PPARγ expression: A rat fibrosis model study

“…TGF-b1 upregulates the expression of miR-130a and miR-130b in systemic sclerosis (SSc) skin fibroblasts and renal mesangial cells [19,29] and downregulates their expression in human gastric cancer cells [30]. Conversely, miR-130a and miR-130b regulate the expression of target genes by TGF-b1 signaling in granulocytes and non-small cell lung cancer (NSCLC) cells [31,32]. In the current study, in rat HSC-T6 cells treated with TGF-b1, the expression of miR-130a and miR-130b was significantly upregulated, and that of PPARg was decreased.…”

The oncogenic and prognostic potential of eight microRNAs identified by a synergetic regulatory network approach in lung cancer

“…Secondly, literature-based evidence, along with an extended analysis using 170 high-quality NSCLC patient samples, revealed the importance of six miRNAs in lung cancer patient survival. In summary, the combined results of our previous (Mitra et al, 2014) and current study provided an adequate foundation for lung cancer investigators to conduct in-depth experiments to uncover the therapeutic potential of these miRNAs in the treatment of NSCLC.…”

“…Recently we uncovered the presence of miRNA-TF co-regulatory networks in non-small cell lung cancer (NSCLC) (Mitra et al, 2014). To minimise false-positives, we investigated the regulator-target relationships that were reproducible or preserved in multiple independent NSCLC data sets.…”

Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation

“…However, due to the limited number of datasets, the comparative conclusion may not be generalizable to future cases. Of note, genes contained in dataset Lung-I were more discriminable from the DE perspective than from the DCE perspective, as genes with borderline DE features were not included (see more details in [18]); accordingly, dataset Lung-II was also biased towards the DE feature. Indeed, from Lung-I to Lung-II, we observed significant consistency in the DEG/non-DEG classification (Fisher's exact test, P -value<2.2×10 −6 ), but no significant consistency in the DCG/non-DCG classification.…”

“…In a differential expression analysis described elsewhere [18], we identified 1504 DEGs and 3627 non-DEGs, which meant DEGs accounted for 29.3% of the combined set. In another aspect, the expression data containing the total 5131 genes (1504+3627) was analyzed by the differential co-expression analysis method DCe for discriminating DCGs and non-DCGs.…”