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Important miRs of pathways in different tumor types.

ABSTRACT: We computationally determined miRs that are significantly connected to molecular pathways by utilizing gene expression profiles in different cancer types such as glioblastomas, ovarian and breast cancers. Specifically, we assumed that the knowledge of physical interactions between miRs and genes indicated subsets of important miRs (IM) that significantly contributed to the regression of pathway-specific enrichment scores. Despite the different nature of the considered cancer types, we found strongly overlapping sets of IMs. Furthermore, IMs that were important for many pathways were enriched with literature-curated cancer and differentially expressed miRs. Such sets of IMs also coincided well with clusters of miRs that were experimentally indicated in numerous other cancer types. In particular, we focused on an overlapping set of 99 overall important miRs (OIM) that were found in glioblastomas, ovarian and breast cancers simultaneously. Notably, we observed that interactions between OIMs and leading edge genes of differentially expressed pathways were characterized by considerable changes in their expression correlations. Such gains/losses of miR and gene expression correlation indicated miR/gene pairs that may play a causal role in the underlying cancers.

SUBMITTER: Wuchty S

PROVIDER: S-EPMC3554575 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Important miRs of pathways in different tumor types.

Wuchty Stefan S Arjona Dolores D Bauer Peter O PO

PLoS computational biology 20130124 1

We computationally determined miRs that are significantly connected to molecular pathways by utilizing gene expression profiles in different cancer types such as glioblastomas, ovarian and breast cancers. Specifically, we assumed that the knowledge of physical interactions between miRs and genes indicated subsets of important miRs (IM) that significantly contributed to the regression of pathway-specific enrichment scores. Despite the different nature of the considered cancer types, we found stro ...[more]

PMID: 23358700

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Project description:Chronic kidney disease (CKD) is a highly heterogeneous nephrosis that occurs when the structure and function of the kidney is damaged. Gene expression studies have been widely used to elucidate various biological processes; however, the gene expression profile of CKD is currently unclear. The present study aimed to identify diagnostic biomarkers and therapeutic targets using renal biopsy sample data from patients with CKD. Gene expression data from 30 patients with CKD and 21 living donors were analyzed by weighted gene co‑expression network analysis (WGCNA), in order to identify gene networks and profiles for CKD, as well as its specific characteristics, and to potentially uncover diagnostic biomarkers and therapeutic targets for patients with CKD. In addition, functional enrichment analysis was performed on co‑expressed genes to determine modules of interest. Four co‑expression modules were constructed from the WGCNA. The number of genes in the constructed modules ranged from 269 genes in the Turquoise module to 60 genes in the Yellow module. All four co‑expression modules were correlated with CKD clinical traits (P<0.05). For example, the Turquoise module, which mostly contained genes that were upregulated in CKD, was positively correlated with CKD clinical traits, whereas the Blue, Brown and Yellow modules were negatively correlated with clinical traits. Functional enrichment analysis revealed that the Turquoise module was mainly enriched in genes associated with the 'defense response', 'mitotic cell cycle' and 'collagen catabolic process' Gene Ontology (GO) terms, implying that genes involved in cell cycle arrest and fibrogenesis were upregulated in CKD. Conversely, the Yellow module was mainly enriched in genes associated with 'glomerulus development' and 'kidney development' GO terms, indicating that genes associated with renal development and damage repair were downregulated in CKD. The hub genes in the modules were acetyl‑CoA carboxylase α, cyclin‑dependent kinase 1, Wilm's tumour 1, NPHS2 stomatin family member, podocin, JunB proto‑oncogene, AP‑1 transcription factor subunit, activating transcription factor 3, forkhead box O1 and v‑abl Abelson murine leukemia viral oncogene homolog 1, which were confirmed to be significantly differentially expressed in CKD biopsies. Combining the eight hub genes enabled a high capacity for discrimination between patients with CKD and healthy subjects, with an area under the receiver operating characteristic curve of 1.00. In conclusion, this study provided a framework for co‑expression modules of renal biopsy samples from patients with CKD and living donors, and identified several potential diagnostic biomarkers and therapeutic targets for CKD.

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Important miRs of pathways in different tumor types.

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Important miRs of pathways in different tumor types.

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