Potential candidate biomarkers associated with osteoarthritis: Evidence from a comprehensive network and pathway analysis

Abstract

Osteoarthritis (OA) is one of the most common forms of arthritis world widely. Some key genes and diagnostic markers have been reported due to the development of modern molecular biology technologies. However, the etiology and pathogenesis of OA remains unknown. In this study, an integrated network and pathway analysis towards the biological function of OA‐associated genes was conducted to provide valuable information to further explore the etiology and pathogenesis of OA. A total of 2,548 genes which reported a statistically significant association with OA were screened. An integrated network and pathway analysis was performed to identify the pathways and genes most associated to OA. Moreover, OA‐specific protein–protein interaction (PPI) network was constructed by cytocluster based on the Molecular Complex Detection Algorithm (MCODE) to screen its candidate biomarkers. Quantitative real‐time polymerase chain reaction was used to confirm the expression levels and to validate the results of MCODE cluster analysis by six genes. The pathway networks suggested that extracellular matrix (ECM) organization, collagen degradation and collagen formation showed important associations with OA. In top two PPI clusters, 61 of the OA‐associated genes were included in the OA‐specific PPI network, which also included 23 candidate genes that are likely to be highly associated with OA based on MCODE clusters. Analysis of mRNA showed that the expression levels of COL9A1, COL9A2, ITGA3, COL9A3, ITGA2, and LAMA1 in the peripheral blood mononuclear cells of OA patients were significantly lower than those of the normal controls (p<0.005). To our knowledge, this is the first comprehensive and systematic report based on OA‐related genes demonstrating that the functional destruction of collagen in cartilage may be a very important contributing factor to OA. Quantitative detection of collagen synthesis may be of great help in early identification and prediction of OA. Maintaining the quality and quantity of collagen can be a potential target for clinical treatment of OA in the future practice.

Identifying Drug-Induced Liver Injury Associated With Inflammation-Drug and Drug-Drug Interactions in Pharmacologic Treatments for COVID-19 by Bioinformatics and System Biology Analyses: The Role of Pregnane X Receptor

“…The targets having degrees higher than the average value were defined as the key targets. Furthermore, to identify the core local sub-networks ( clusters ) with the high clustering scores, we conducted Molecular Complex Detection (MCODE) cluster analysis of candidate targets using the Cytoscape plugin MCODE clustering algorithm (node score cut-off value = 0.2, degree cut-off value = 2, K-core = 2, MAX depth value = 100) ( Zhang et al, 2019 ; Ahmed, 2020 ). The higher the clustering scores were, the more important the MOCDE sub-networks were in the regulatory network of DPTS-induced liver injury.…”

Osteoarthritis: Novel Molecular Mechanisms Increase Our Understanding of the Disease Pathology

“…In search of both predictive and descriptive OA biomarkers, other cartilage ECM components were identified in bioinformatic approaches. Co-expression network and pathway analysis of gene expression profiles from different OA datasets were performed [ 74 , 75 ]. The expression profile of genes clearly associated with OA (like e.g., MMP2, COL6A1, COL9A1-3) and the synthesis and degradation of the encoded proteins has to be evaluated in future studies.…”

Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis

“…The development of next-generation technologies such as largescale sequencing and multianalyte assay has recently facilitated the discovery of a slew of potential biomarkers for OA (Cong et al, 2017;Xu et al, 2019;Zhang et al, 2019;Hong et al, 2020). For example, Li et al (2017), identified several differentially expressed genes, including a disintegrin and metalloproteinase 8 (Adam8), arginase 1 (Arg1) and microRNAs (miRNAs) such as miRNA-200b, which might play key roles in the pathogenesis of mice OA based on microarray analysis.…”

“…Furthermore, functional disruption of collagen in cartilage has been shown to be a very important contributing factor to OA. Quantitative detection of collagen synthesis may facilitate early identification and prediction of OA (Zhang et al, 2019). However, none of these biomarkers is specific, which makes diagnosis and treatment of OA still a challenge.…”