Volume 7 Supplement 3
Intra-relation reconstruction from inter-relation: miRNA to gene expression
- Dokyoon Kim†1, 2, 3,
- Hyunjung Shin†4Email author,
- Je-Gun Joung1, 2, 5,
- Su-Yeon Lee1, 2 and
- Ju Han Kim1, 2Email author
© Kim et al.; licensee BioMed Central Ltd. 2013
Published: 16 October 2013
In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge.
Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the cancer clinical outcome prediction. However, the limitation of previous work was that we integrated with multi-layers of genomic data without considering of inter-relationship information between genomic features. In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study.
In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) was adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression.
In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.
DNA microarrays have already been widely used for the classification of tumor subtypes or clinical outcomes for the diagnosis, treatment, or prognosis of cancer for many years [1–6]. In addition to gene expression, there have been attempts at cancer clinical outcome prediction using different levels of genomic data such as copy number, DNA methylation, or miRNA [7–11]. Despite these efforts, however, the elucidation of cancer phenotypes remains problematic since the cancer genome is neither simple nor independent but is complicated and dysregulated by multiple mechanisms in the biological system [12, 13]. Previously, we have proposed a graph-based framework that integrates with multi-layers of genomic data, copy number alteration, DNA methylation, gene expression, and miRNA expression, for the prediction of clinical outcomes in glioblastoma multiforme (GBM) and serous cystadenocarcinoma . The strengths of our approach were also highlighted as initiating its application using multiple scales and computation efficiency .
In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as from gene expression or from microRNA (miRNA) and many such researches have been successful [14, 16, 17]. However, intra-relations are not fully explaining complex cancer mechanisms because the inter-relation information between different levels of genomic data is missing, e.g. miRNA and its target genes. The 'inter-relation' between different levels of genomic data can be constructed from biological experimental data as well as genomic knowledge.
There are possible inter-relationships between the genomic features belonging to different levels of genomic data such as 'miRNA-target genes,' 'copy number alteration region-genes located in the altered region,' 'DNA methylation site-specific genes regulated by promoter regions,' etc. However, the limitation of previous work was that we integrated with multi-layers of genomic data for cancer clinical outcome prediction without considering of inter-relationship information between genomic features . We assume that accuracy of prediction model increase when considering of inter-relationship between different levels of genomic data because of incorporation of information fused over genomic dataset and genomic knowledge, providing an enhanced global view on interplays in cancer mechanisms [12, 18]. Therefore, when integrating multi-layers of genomic data, it will be desirable that a framework will be capable of containing the inter-relationships between genomic features belonging to different layers of the biological system.
In this paper, we propose a new integrative framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the clinical outcome prediction as a pilot study. miRNAs are involved in the post-transcriptional regulation of genes either by inducing degradation of the transcript of their multiple targets or by repressing the translation of mRNA into protein [19, 20]. In addition, miRNAs regulate many genes associated with different biological processes such as development, stress response, apoptosis, proliferation, and tumorigenesis [21–25]. In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in GBM was adopted as a base task. GBM is the most common and aggressive primary brain tumor in adults , and notorious for its tendency to recur . Despite recent advances in the molecular pathology of GBM, the underling molecular mechanisms associated with clinical outcome are still poorly understood .
The remainder of the paper is organized as follows. Data description and methods for prediction based on intra-relation among mRNAs and prediction based on inter-relation from miRNA to mRNA are explained in the Materials and Methods section. In the Results section, experimental results and biological implications are provided to demonstrate the validity and effectiveness of our proposed approach. Finally, we discuss the meaning of our study and future works in the last section.
Materials and methods
Normalized datasets were retrieved from the Cancer Genome Atlas (TCGA) data portal (http://tcga-data.nci.nih.gov/). A binary classification problem was set using the survival information from patient. In the classification of short-term or long-term survival, 'long-term' represents samples derived from patients who survived longer than 24 months . The total 82 patients' records were available across the miRNA and gene expression data sets (N = 82), in which 54 were short-term survival while the remaining were long-term survival.
Retrieving mRNA targets of miRNA
There is a many-to-many relationship between miRNAs and mRNAs since a single miRNA targets multiple mRNAs or a single mRNA is targeted by multiple miRNAs. In order to get target relations between miRNA and mRNA, we used miRecords which is integrated resources of miRNA that store target interactions produced by 11 established miRNA target prediction programs . We created 10 variations for predicted target pairs between miRNA and genes by considering the number of positive voters from the included algorithms by miRecords (Additional file 1). Since most of the evaluation results from these variations were largely comparable, the most representative variation # 6 in Additional file 1 was used to describe the overall study results in the following sections.
Prediction based on intra-relation among mRNAs
We used a graph-based semi-supervised learning (SSL) as a classification algorithm, which is a halfway learning scheme between supervised and unsupervised learning [31–34]. The graph-based SSL takes advantage of computational efficiency and representational ease for the biological system. The learning time of graph-based SSL is nearly linear with the number of graph edges while the accuracy remains comparable to the kernel-based methods that suffer from the relative disadvantage of a longer learning time [16, 35]. In addition, the interpretation of biological phenomena can be improved because of the graph structure [36–38], which naturally fits into the graph based SSL.
where I is the identity matrix.
Prediction based on inter-relationship from miRNA to mRNA
Original graph from gene expression (GO): We made an original graph from gene expression data where nodes depict patients and edges represent their possible relations.
Damaged graph from the original graph (GD): We randomly reduced the edges from the original graph, GO, in order to make the incomplete graph. GD50 means the gene expression graph with 50 percent of damaged edges.
Reconstructed graph via inter-relationship (GR): Reconstructed graph of gene expression was generated via inter-relationship between miRNA and gene expression.
Augmented graph (GA): An augmented graph was generated by combining damaged graph (GD) from the original graph and reconstructed graph (GR) from inter-relation.
Since genomic data sources are generally high dimensional and noisy, and contain many redundant features, which may incur computational difficulty and low accuracy, a Student t-test based feature selection method was used . Even though there are many feature selection techniques such as filter, wrapper, and embedded method , a simple univariate feature selection method was used in order to emphasize not the effect of feature selection but the effect of integration with inter-relationship between miRNAs and target mRNAs.
The receiver operating characteristic (ROC) curve plots sensitivity (true positive rate) as a function of 1-specificity (false positive rate) for a binary classifier system as its discrimination threshold is varied . For each problem, we calculated area under the curve (AUC) of ROC as a performance measure. Each experiment is repeated three times in order to estimate the variance of the measurement values and five-fold cross-validation was conducted in order to overcome over-fitting. The Wilcoxon signed-rank test was used to assess the significance level of difference in performance between the results of damaged graphs and augmented graphs .
As the percent of damaged edges in gene expression graph increased, the AUCs of damaged graph (GD) are getting decreased sharply compared to the original graph from gene expression data (GO) (Figure 3). However, the performances of the augmented graph (GA) showed robust results even though 90 percent of edges were reduced from the original graph. The performance of GA, a graph combining biological experimental data and genomic knowledge, is higher than the one of GO, an original graph from gene expression only, from 0 to 30 percent of damaged edges (Figure 3). This suggests that genomic knowledge is complementary to the prediction power of explaining cancer phenotype even though biological experimental data such as gene expression has incomplete information.
Significance test of the performances between GD and GA
Percent of damaged edges
AUC of GD
AUC of GA
For instance, three miRNAs, hsa-mir-20a, hsa-mir-106a, and hsa-mir-221, were also identified as miRNA signatures that predicts survival in Glioblastoma . Hsa-mir-20a and hsa-mir-106a miRNAs were classified into the protective class and hsa-mir-221 was classified into the risk class in the previous study as well . The protective miRNAs were expressed at a higher level in the long-term survival group compared to the short-term survival group while the risky miRNAs were expressed at a higher level in the short-term group than in the long-term group. The risky and protective class of these miRNAs supports the fact that their functions being either promoting or inhibitory, respectively. Under-expression of hsa-mir-106a has been shown to be associated with poor patient survival in colon cancer and glioma [45, 46]. Target genes of hsa-mir-106a, BDH1, UPP1, TUSC2, and KMO, were over-expressed in the short-term survival group, which is a reverse pattern of expression in hsa-mir-106a. These genes play important roles that affect metabolic process, cell cycle, or nucleotide catabolic process in several cancers [47–50]. The miRNA cluster, which contains hsa-mir-20a, was found to promote lung cancer growth in vitro, activated by c-myc and promote tumor angiogenesis . HFE, one of the selected target genes of hsa-mir-20a, has been found to be associated with immune response in GBM and ovarian cancer [50, 52]. Among selected miRNA and target gene pairs, other pairs were of interest because they could suggest some novel indirect mechanisms in GBM tumorigenesis.
Description of the selected gene features between short-term and long-term survival group in GBM
Renal tumor antigen/threonine kinase activity/transferase activity
ATP binding/nucleotide binding
DNA binding/N-methyltransferase activity
Integral to membrane/kynurenine 3-monooxygenase activity
D-ribose metabolic process/ribokinase activity
Nucleus/regulation of progression through cell cycle
Extracellular space/sugar binding
Cytoplasm/nucleoside metabolic process/nucleotide catabolic process
3-hydroxybutyrate dehydrogenase activity/metabolic process/mitochondrial inner membrane/mitochondrial matrix
Antigen processing and presentation/ immune response/ protein complex assembly
Cell cycle/cell proliferation/cell-cell signalling/negative regulation of progression through cell cycle
The RAGE pathway may play an important role in STAT3 induction in glioma-associated microglia and macrophages, a process that might be mediated through S100B . In addition, the under-expression of ATAD3A may be involved in the chemosensitivity of oligodendrogliomas and the transformation pathway .
Comparison with other proposed methods for inter-relationship matrix
Despite the difficulty of developing an adequate measure to calculate the similarity matrix containing inter-relationship information between miRNA and gene expression, we implemented 4 measures, GR_1, GR_2, GR_3, and GR_4, and compared with the proposed method, GR_5, in order to assess the benefit of the proposed one. GR_1 was calculated by multiplication of correlation matrices from gene expression and miRNA expression. The method of GR_2 was generated through the simple addition of two vectors, genes and miRNAs, for containing inter-relationship. On the other hand, the method of GR_3 was calculated by removing miRNAs and genes, which were not belonging to the target relations, after simple addition of two vectors, genes and miRNAs. GR_4 was focused on a targeted gene and considered multiple miRNAs targeting the specific gene when calculating the inter-relationship. In contrast to GR_4, GR_5, the proposed method in our study, was focused on a miRNA and considered multiple target genes from the specific miRNA.
In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/long-term survival of GBM patients in order to provide a preliminary insight on the question that is how informative inter-relationship between miRNA and gene expression is when different levels of genomic dataset and valid genomic knowledge are available. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression. New evidence suggests that genomic knowledge is complementary to the prediction power of explaining cancer phenotype even though biological experimental data such as gene expression has incomplete information. In addition, our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype since miRNAs regulate many genes associated with different biological processes such as development, stress response, apoptosis, proliferation, and tumorigenesis.
The present study underpins our on-going work. It is expected that the next attempt will be more focused on how to utilize the information from 'intra-relation', the relation between different levels: from the genome level to epigenome, transcriptome, proteome, and further stretched to the phenome level. There might be other possible intra-relations between different layers of genomic data such as 'copy number alteration region - genes located in the alteration region,' 'DNA methylation site - specific genes regulated by promoter regions,' etc. Thus, when integrating multi-levels of genomic data, it might be valuable that a framework will be capable of containing the inter-relationships between genomic features belonging to different layers of the biological system as genomic knowledge. Even though this study is limited to the prediction of short-term/long-term survival in GBM as a base task, the proposed framework can be applied to other cancer types or other clinical outcomes such as grade, stage, metastasis, etc. In addition, we could apply the proposed method to another layer of 'intra-relation' based on miRNA expression profiles together with 'intra-relation' between mRNAs.
Recently, TCGA has been generating the additional cancer genomic data for about 20 to 25 tumor types as the second phase of the project. With abundance in different types of genomic, clinical data and valid genomic knowledge, our proposed framework will be valuable for explaining the underlying tumorigenesis, eventually leading to more effective screening strategies and therapeutic targets in many types of cancer.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631). DK's education grant was supported by the Ministry of Health and Welfare (A112020) and by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2012M3A9D1054622). HS would like to gratefully acknowledge support from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2013R1A1A3010440/2010-0028631). In addition, we gratefully acknowledge the TCGA Consortium and all its members for the TCGA Project initiative, for providing samples, tissues, data processing and making data and results available.
The publication cost for this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2010-0028631).
This article has been published as part of BMC Systems Biology Volume 7 Supplement 3, 2013: Twelfth International Conference on Bioinformatics (InCoB2013): Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/7/S3.
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