Integrative systematic review meta-analysis and bioinformatics identifies MicroRNA-21 and its target genes as biomarkers for colorectal adenocarcinoma.

Abstract

Advanced colorectal has poor survival and are difficult to treat. Therefore, there is an urgent need for biomarkers to diagnose this cancer at earlier manageable stages. Micro-RNAs (miRNAs) are amongst the most significant biomarkers that have shown promise in improving management and early detection of different types of cancers. However, since MiRNAs are non-coding, the main limitation of using them as biomarkers is that they do not have associated phenotype and therefore difficult to validate using other techniques. This makes it difficult to understand the mechanism of miRNA is disease initiation and progression, therefore any methodology that can provide semantics to miRNA expression would enhance the understanding of the role of miRNA in disease. Here we report an integrative meta-analysis and bioinformatics methodology that showed microRNA-21 and its associated target mRNA to be the most significant predictive biomarkers for colorectal adenoma and adenocarcinoma. After drawing key inferences by meta-analysis, the authors then developed a bioinformatics method to identify mir-21 gene targeting in a specific tissue using two different bioinformatics approaches; absolute GSEA (Gene Set Enrichment Analysis) and LIMMA (Linear Models for MicroArray data) to identify differentially expressed genes of miRNA-21. Results from GSEA intersection with mir-21 gene targets was a subset of longer gene list that was obtained from the GEO2R intersect. In our study, both of longer GEO2R gene target list and the more focused GSEA list established the fact that mir-21 target numerous functional pathways that are mostly interconnected. Our three steps bioinformatics approach identified ABCB1, HPGD, BCL2, TIAM1, TLR3, and PDCD4 as common targets for mir-21 in both of adenoma as well as adenocarcinoma suggesting they are biomarkers for early CRC. The approach in this study proposed combining the big data from the scientific literature together with novel bioinformatics to bring about a methodology that can be used to first identify which microRNAs are involved in a specific disease, and then to identify a panel of biomarkers derived from the microRNAs target genes, and from these target genes the functional significance of these microRNAs can be inferred providing better clinical value for the surgeon.