Dataset Information

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer.

ABSTRACT: In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random forest classification allowed the identification of pairs of pathways with high cross-talk. We assessed miRNAs regulating the identified gene pathways by a mutual information analysis. A Fisher test was applied to demonstrate their significance in the regulated pathways. Our results suggest interesting networks of pathways that could be key regulatory of target genes in BC, including stem cell pluripotency, coagulation, and hypoxia pathways and miRNAs that control these networks could be potential biomarkers for diagnostic, prognostic, and therapeutic development in BC. This work shows that standard methods of predicting normal and tumor classes such as differentially expressed miRNAs or transcription factors could lose intrinsic features; instead our approach revealed the responsible molecules of the disease.

SUBMITTER: Colaprico A

PROVIDER: S-EPMC4512830 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer.

Colaprico Antonio A Cava Claudia C Bertoli Gloria G Bontempi Gianluca G Castiglioni Isabella I

BioMed research international 20150709

In this work an integrated approach was used to identify functional miRNAs regulating gene pathway cross-talk in breast cancer (BC). We first integrated gene expression profiles and biological pathway information to explore the underlying associations between genes differently expressed among normal and BC samples and pathways enriched from these genes. For each pair of pathways, a score was derived from the distribution of gene expression levels by quantifying their pathway cross-talk. Random f ...[more]

PMID: 26240829

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Project description:More than one pathway is involved in disease development and progression, and two or more pathways may be interconnected to further affect the disease onset, as functional proteins participate in multiple pathways. Thus, identifying cross-talk among pathways is necessary to understand the molecular mechanisms of multiple myeloma (MM). Based on this, this paper looked at extracting potential pathway cross-talk in MM through an integrative approach using Monte Carlo cross-validation analysis. The gene expression library of MM (accession number: GSE6477) was downloaded from the Gene Expression Omnibus (GEO) database. The integrative approach was then used to identify potential pathway cross-talk, and included four steps: Firstly, differential expression analysis was conducted to identify differentially expressed genes (DEGs). Secondly, the DEGs obtained were mapped to the pathways downloaded from an ingenuity pathways analysis (IPA), to reveal the underlying relationship between the DEGs and pathways enriched by these DEGs. A subset of pathways enriched by the DEGs was then obtained. Thirdly, a discriminating score (DS) value for each paired pathway was computed. Lastly, random forest (RF) classification was used to identify the paired pathways based on area under the curve (AUC) and Monte Carlo cross-validation, which was repeated 50 times to explore the best paired pathways. These paired pathways were tested with another independently published MM microarray data (GSE85837), using in silico validation. Overall, 60 DEGs and 19 differential pathways enriched by DEGs were extracted. Each pathway was sorted based on their AUC values. The paired pathways, inhibition of matrix metalloproteases and EIF2 signaling pathway, indicated the best AUC value of 1.000. Paired pathways consisting of IL-8 and EIF2 signaling pathways with higher AUC of 0.975, were involved in 7 runs. Furthermore, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) exhibited the best AUC values and higher frequency of validation. Two paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) were used to accurately classify MM and control samples. These paired pathways may be potential bio-signatures for diagnosis and management of MM.

Dataset Information

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer.

Publications

Integrative Analysis with Monte Carlo Cross-Validation Reveals miRNAs Regulating Pathways Cross-Talk in Aggressive Breast Cancer.

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