Abstract
Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an application of the RH-SAC algorithm on miRNA and mRNA expression data for identification of potential miRNA-mRNA modules. First, a set of miRNA rules was generated using the RH-SAC algorithm. The mRNA targets of the selected miRNAs were identified using the miRTarBase database. Next, the expression values of target mRNAs were used to generate mRNA rules using the RH-SAC. Then all miRNA-mRNA rules have been integrated for generating networks. The RH-SAC algorithm unlike other existing methods selects a group of co-expressed miRNAs and mRNAs that are also differentially expressed. In total 17 miRNAs and 141 mRNAs were selected. The enrichment analysis of selected mRNAs revealed that our method selected mRNAs that are significantly associated with colorectal cancer. We identified novel miRNA/mRNA interactions in colorectal cancer. Through experiment, we could confirm that one of our discovered miRNAs, hsa-miR-93-5p, was significantly up-regulated in 75.8% CRC in comparison to their corresponding non-tumor samples. It could have the potential to examine colorectal cancer subtype specific unique miRNA/mRNA interactions.
Highlights
MicroRNA are a class of short approximately 22-nucleotide non-coding RNAs processed from hairpin precursors of ~70 nt, extracted, in turn, from primary transcripts found in many plants and animals
We investigated whether the combined elevated expression of 29 selected genes in colorectal adenocarcinoma patient samples, extracted from TCGA, was related to the prognosis of patients with colorectal cancer
The main contribution of this paper lies in identification of potential miRNA-mRNA regulatory modules in colorectal cancer using corresponding miRNA and mRNA expression data
Summary
MicroRNA (miRNAs) are a class of short approximately 22-nucleotide non-coding RNAs processed from hairpin precursors of ~70 nt (pre-miRNA), extracted, in turn, from primary transcripts (pri-miRNA) found in many plants and animals. Connecting rule-based method has been employed on miRNA and mRNA expression data to identify miRNA-mRNA modules in an HCV data set[25]. Rough sets were used to design clustering algorithms[32,33] to identify groups of co-expressed genes from gene microarray data sets They were used to design methods to select differentially expressed miRNAs31,34 and to clustering functionally similar miRNAs35. We present a computational approach to identify miRNA-mRNA modules in CRC It is a two step approach, at first miRNA rules/clusters were generated using the rough hypercuboid based supervised clustering algorithm[36] (RH-SAC). Instead of selecting single miRNAs or mRNAs based on certain criteria as described by Fu et al.[20] the RH-SAC algorithm generates clusters of functionally similar miRNAs/mRNAs whose coherent expression further classifies the samples efficiently. The most important network motif involving hsa-miR-27a-3p and p53/CDKN1A genes has been analysed and discussed in detail
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