A panel of three microRNA signatures as a potential biomarker for CRC screening based on stages and functional prediction using bioinformatic analysis

Background: MicroRNA (miRNA) has been linked to colorectal cancer (CRC) tumorigenesis due to its post-transcriptional mechanism in targeting cancer-associated genes. Although miRNAs appear to be promising screening biomarkers, functional prediction analysis is required to shed light on their role in CRC tumorigenesis. Therefore, this study aims to identify the significantly deregulated miRNAs in CRC tumorigenesis. (2) Methods: Three upregulated miRNAs (hsa-miR-20a-5p, hsa-miR-21-5p, and hsa-miR210-3p) from 14 significant differentially expressed miRNAs (DEMs) were chosen from microarray profiling to be validated in plasma. Bioinformatics analyses showed that these miRNAs generally contributed to tumorigenesis, but only hsa-miR-20a-5p and hsa-miR-21-5p were specifically linked to CRC. Only two miRNAs showed a positive correlation when compared to their expression in plasma. However, further analysis showed that all three miRNAs in plasma were significantly difference between the early and advanced stages of CRC. ROC curve analysis was used to evaluate miRNAs’ diagnostic performance in the early and advanced stages. (3) Results: Collectively, hsa-miR-20a-5p showed the highest discriminative value (AUC= 0.82, sensitivity = 86%, and specificity= 88%) in discriminating early CRC, while both hsa-miR-21-5p and hsa-miR-210-3p give a perfect performance for advance CRC. In addition, the performance of all miRNAs’ combinations also gives a perfect performance for diagnosis in both early and advanced CRC, except the combination of hsa-miR-20a-5p and hsa-miR-210-3p. (4) Conclusions: A few potential miRNA panels as CRC biomarker is needed for better prediction of disease. The reflective circulating miRNAs can be contributed to by minimal invasive screening tools

Quantile Normalization for High Throughput Circulating MicroRNA Expression Study using TaqMan® Low Density Array Panels: Supporting Evidence

In searching for new biomarkers, high throughput technique has been widely used by researchers, including for gene expression study. However, the reliability and accuracy of results from high throughput study critically depends on appropriate data management, including normalization methods. Data driven normalization has been introduced as a normalization method for high throughput gene expression study. Thus, this study was conducted to evaluate the performance of various data driven and reference genes normalization methods using a high throughput circulating microRNA expression dataset. A quantification cycle (Cq)  dataset generated from a high throughput circulating microRNA study was used to test the normalization methods using HTqPCR package in R software. The normalized Cq generated from different methods were compared descriptively using box plot analysis and coefficient of variance. The box plot analysis showed that quantile normalization produced more homogenous Cq distribution, lesser outliers and reduced coefficient of variance as compared to other normalization methods in screening and validation phases. The overview on quantile normalized Cq showed consistency in its level of expression before and after 2-∆∆Cq calculation indicating the reliability of quantile normalized Cq. Quantile normalization is suggested to be used in high throughput miRNA expression study due to its performance in homogenizing the data, reduce outliers and coefficient of variance