Prediction of MicroRNA and Gene Target in Synovium-Associated Pain of Knee Osteoarthritis Based on Canonical Correlation Analysis

Abstract

Inflammation plays a central role in knee osteoarthritis (OA) pathogenesis (C. R. Scanzello, 2017). The synovial membrane inflammation is associated with disease progression and represents a primary source of agony in knee OA (L. A. Stoppiello et al., 2014). Many inflammatory mediators may have biomarker utility. To identify synovium related to knee OA pain biomarkers, we used canonical correlation analysis to analyze the miRNA-mRNA dual expression profiling data and extracted the miRNAs and mRNAs. After identifying miRNAs and mRNAs, we built an interaction network by integrating miRWalk2.0. Then, we extended the network by increasing miRNA-mRNA pairs and identified five miRNAs and four genes (TGFBR2, DST, TBXAS1, and FHLI) through the Spearman rank correlation test. For miRNAs involved in the network, we further performed the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, whereafter only those mRNAs overlapped with the Online Mendelian Inheritance in Man (OMIM) genetic database were analyzed. Receiver operating characteristic (ROC) curve and support vector machine (SVM) classification were taken into the analysis. The results demonstrated that all the recognized miRNAs and their gene targets in the network might be potential biomarkers for synovial-associated pain in knee OA. This study predicts the underlying risk biomarkers of synovium pain in knee OA.

Identification of miRNA–mRNA–TFs regulatory network and crucial pathways involved in asthma through advanced systems biology approaches

“…Another miRNA, hsa-miR-335-5p, has been implicated in several cancers, including colorectal cancer [ 75 ], gastric cancer [ 76 ], and uterine sarcoma [ 77 ]. The hsa-miR-335-5p is reported as a biomarker for inflammation related to knee osteoarthritis [ 78 ]. ATE1 is a target of hsa-miR-335-5p ( Table 4 ), which is an arginyl-transferase and has been linked to higher metabolic rate and fat [ 79 ], while in asthma, obesity and the high body mass index (BMI) are considered as risk factors [ 80 ].…”

Integration of transcriptomics and metabolomics to reveal the effect of ginsenoside Rg3 on allergic rhinitis in mice

“…CCA was performed to mine the correlation between sets of variables of multiomics data. 32 The correlation of DEGs and differential metabolites was measured by using CCA. The content levels of differential metabolites and the expression levels of DEGs were imported into software Canoco 5.0 (https://www.canoco5.com/) and the OmicShare database (https://omicshare.com/) with default parameters for CCA.…”

“…DEGs and differential metabolites from CCA were analyzed by the Pearson correlation coefficient. 32 Only the gene-metabolite interaction with Pearson correlation coefficient >0.6 and p value <0.05 was chosen to be significant, 80,81 and the genes and metabolites were considered as the potential hub genes and metabolites. The expression of potential hub genes was then analyzed by qRT-PCR as follows: total RNA was obtained from the mouse nasal mucosa by using TRIzol reagent.…”

“…Canonical correlation analysis (CCA) could show the correlation between two groups of data as a whole through the comprehensive analysis of variables. 32 In the present study, a total of 115 DEGs were converted using the R package and 8 differential metabolites were analyzed by CCA. The total explained variation of the explanatory variable matrix (metabolites) to the response variable matrix (genes) was 94.0% (CCA1 + CCA2), indicating that the analysis had high reliability.…”