- Open Access
Integrated analysis of microRNA-target interactions with clinical outcomes for cancers
© Joung et al.; licensee BioMed Central Ltd. 2014
- Published: 8 May 2014
Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.
Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.
We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.
This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.
- Survival Analysis
- miRNA-mRNA interaction
- miRNA Expression
- Gene Expression Profile
- Cancer Genes
As patterns of gene expression correlate with disease phenotype and patient outcome, mRNA expression profiling has been used to classify disease risks as well as prediction of outcome[1–4]. In addition, survival analysis with gene expression profiles is beneficial for identifying new prognostic targets of diverse diseases.
Many miRNAs have been also found to be correlated with clinical outcome in specific cancer types [5–10]. miRNAs are a class of small and endogenous RNA molecules that regulate their target mRNAs through translational repression or mRNA degradation . In tumors, many miRNAs can be aberrantly expressed, leading to potentially abnormal regulation of their target mRNAs. Although over 1,000 human miRNAs may be encoded in human genome, the potential therapeutic markers provided by miRNAs for a diverse spectrum of diseases are still unexplored.
Recently, several investigations have put emphasis on the integrated analysis of miRNAs and mRNAs in clinical outcomes [12–17]. In general, there are different approaches for the joint analysis of miRNA and mRNA data. For example, several miRNAs associated with survival rate can be extracted by survival analysis and then their relationship of inverse correlation can be identified based on analyzing miRNA and mRNA expression profiles. Most approaches do not test the clinical outcome by considering miRNA expression and mRNAs expression simultaneously. At minimum, this analysis requires the size of the cohort to be large enough for statistically significant measurement outcomes and the paired samples with both mRNA and miRNA expressions should be given. The Cancer Genome Atlas (TCGA)  provides different types of genomic datasets and we systematically integrated multi-omics data for cancer clinical outcome prediction .
Here, we present the survival analysis considering the regulatory relationships of miRNAs and their target genes. We tested clinical outcomes with the patient survival information, miRNA expression profiles and gene expression profiles for ovarian cancer and glioblastoma multiforme (GBM) through the integrated analysis of miRNA-mRNA interaction pairs. We found the miRNA-mRNA pairs with an inversely correlated expression profile that have significant survival differences between two patient groups. The results presented here suggest that this analysis can help to discover expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.
miRNA target dataset
We obtained the largest collection of human miRNA-mRNA target relationships from the TarBase 6.0 . All targets in this collection were manually curated and experimentally validated. We extracted a total of 12,879 interactions consisting of 229 miRNAs and 6,699 target genes.
Gene and miRNA expression profile
The miRNA and gene expression data sets in ovarian cancer and GBM are acquired from the Cancer Genome Atlas (TCGA) . Ovarian cancer and GBM data sets consist of mRNA expression profiles and miRNA expression profiles for 496 and 425 patients, respectively. We transformed the expression values into a Z-score for each miRNA or each gene. If the Z-score of miRNA (or mRNA) is greater (less) than zero, its expression level is defined to be high (low). We excluded miRNA-mRNA pairs from the expression datasets that were not listed in the TarBase in order to avoid unnecessary calculations. Expression matrices for ovarian cancer composed of 137 miRNAs × 496 patients and 5,707 target genes × 496 patients. Among all possible 776,016 miRNAs and mRNA pairs, the number of validated interactions presented in TarBase 6.0 is 10,574 composed from 137 miRNAs and 5,707 mRNAs. Expression matrices for GBM consist of 144 miRNAs × 425 patients and 6,700 target genes × 425 patients. Among all possible 964,800 miRNA-mRNA pairs, the number of validated interactions is 9,073 from 144 miRNAs and 6,700 mRNAs.
Survival analysis of miRNA and target mRNA
We carried out the survival analysis of each miRNA-mRNA pair for six possible pairwise sets (HL:LH, HH:LL, HL:LH, HH:HL, LL:LH and LL:HL). Among these sets, our focus was on the survival test between LH and HL (Figure 1(B)) as they are inversely related to the level of expression. The group HL indicates that high expression of miRNA causes low expression of mRNA through the mechanism that a miRNA represses or cleaves its target mRNA, while the group LH states that the low expression of miRNA results in the high expression of mRNA.
Comparison of the number of significant pairs.
Num of Significant Pairs (p-value < 0.01)
Top 20 ranked miRNA-mRNA interaction pairs (OV).
Protein Kinase C And Casein Kinase Substrate In Neurons Protein
Solute carrier family 43, member 3
Eyes Absent (Drosophila) Homolog 4
Growth arrest-specific 1
TAF7 RNA Polymerase II, TATA Box Binding Protein (TBP)-Associated Factor
Stromal Cell Derived Factor 2 Like Protein 1
Solute carrier family 35, member D2
Pleomorphic adenoma gene-like 1
Stress-associated endoplasmic reticulum protein 1
Leucine-Rich Repeat Containing G Protein-Coupled Receptor 4
Amyloid beta (A4) precursor protein-binding, family B, member 2
Fibronectin type III domain containing 3A
Microtubule associated tumor suppressor 1
CDK5 regulatory subunit associated protein 1-like 1
Zinc finger, BED-type containing 4
FCH and double SH3 domains 2
E74-Like Factor 4 (Ets Domain Transcription Factor)
Guanine Nucleotide Binding Protein (G Protein) Alpha 12
Interferon gamma receptor 1
Among top 20 ranked miRNA-mRNA interaction pairs, the roles of four targets including PLAGL1, MTUS1, MEF and IFNGR1 have been reported in ovarian cancer (Table 2). miR-148a was down-regulated in ovarian cancer cell lines and might be involved in the carcinogenesis of ovarian cancer [21, 22]. Previous research reported that overexpression of miR-148b in ovarian cancer tissues was not associated with any of the pathological features of patients with ovarian cancer. It suggested that miR-148b might be involved in the early stage of ovarian carcinogenesis . Although other miRNAs including miR-196a, miR-374b, miR-124, and miR-98 have not been reported for associations with ovarian cancer, they are related with the oncogenic phenotype or their expression of other cancers [24–27].
Several miRNAs are dominated in significant miRNA-mRNA pairs. miR-98 and miR-148b-3p have more than 446 and 187 target genes, respectively. miR-98 or miR-148b-3p itself shows a significant survival difference between high and low expression (pvalue < 0.0077 and pvalue < 0.0014), while miR-124-3p with 979 targets genes shows a borderline significance (pvalue < 0.0051).
LOT1(PLAGL1/ZAC1) is known to possess anti-proliferative effects and is frequently silenced in ovarian cancer and breast cancer . Previous studies suggest that a shortage of the PLAGL1 protein might impair its role in regulating the cell cycle and interfere with apoptosis. Consequently, cells may grow and divide too quickly in an uncontrolled manner. Mitochondrial tumor suppressor 1 (MTUS1) is a newly identified candidate tumor suppressor gene . Previous studies have shown that MTUS1 expression levels are down-regulated in cancers of the colon, ovary, pancreas, head and neck, and breast cancer . MEF (myeloid ELF1-like factor, also known as ELF4) is expressed in a significant proportion of ovarian carcinomas . The oncogenic activity of MEF was shown by the ability of MEF to transform NIH3T3 cells, and induce the formation of tumors in nude mice. The expression level of IFNGR1 in a typical ovarian cancer population varies, with 22% of them displaying a complete loss of the IFNγ receptor . Low levels of receptor expression seem to have a negative effect on survival and are unrelated to other pathologic variables. Therefore, low expression of IFNGR1 could be regarded as an independent prognostic marker in ovarian cancer. Although we have found only several previously reported functional roles related to ovarian cancer, they hint at the possibility that other target genes might be associated with ovarian cancer development and progression.
Top 20 ranked miRNA-mRNA interaction pairs (GBM).
G Patch Domain Containing 8
CAP-GLY domain containing linker protein 1
Ctr9, Paf1/RNA polymerase II complex component, homolog (S. cerevisiae)
Histone cluster 3, H2a
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
Family with sequence similarity 3, member C
Protein kinase, AMP-activated, alpha 1 catalytic subunit
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
Transcription factor AP-2 alpha (activating enhancer binding protein 2 alpha)
Ras and Rab interactor 2
C-type lectin domain family 5, member A
Proteasome (prosome, macropain) 26S subunit, non-ATPase, 9
Cyclin-dependent kinase inhibitor 1A (p21, Cip1)
V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog
Ataxia telangiectasia mutated
Previous quantitative real-time PCR revealed overexpression of CDKN1A in primary GBM. This result suggests that CDKN1A expression is regarded as a putative marker to distinguish primary GBM from secondary GBM. The gene for methylthioadenosine phosphorylase (MTAP) is located closely to the gene CDKN2A. MTAP-deficiency in many tumors that have been most resistant to treatment occurs commonly. Especially 70% of glioblastoma lack MTAP. Amplification of KIT in 17 (4.4%) glioblastomas was reveled from screening of 390 glioblastomas. A borderline positive association (p=0.0579) between KIT amplification and TP53 mutation was also observed. ATM expression is correlated with radioresistance in primary GBM cells in culture. Genes encoding components of the DNA-damage response (DDR) pathway are frequently altered in human GBM patients and the ATM/Chk2/p53 cascade suppresses GBM formation.
We have introduced the integrated analysis of survival test with datasets of miRNA and mRNA expression profiles, as well as clinical information in ovarian cancer and GBM. We have seen that the combined expression patterns between miRNAs and mRNAs can distinguish between risk groups related to co-regulation of both. In addition, we have presented supporting evidence for functional roles of miRNA and their targets in specific cancer from the literature. Our approach can be utilized to detect clinical and therapeutic miRNAs and their targets related to outcome of several cancers.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631); Ministry of Health and Welfare (HI13C2164).
Funding for open access charge: National Research Foundation of Korea (NRF).
This article has been published as part of BMC Medical Genomics Volume 7 Supplement 1, 2014: Selected articles from the 3rd Translational Bioinformatics Conference (TBC/ISCB-Asia 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcmedgenomics/supplements/7/S1.
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