Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis.

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA-associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease-related networks based on 21756 gene expression correlation coefficients, hub-genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits-related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA-associated genes. Moreover, 310 OA-associated genes were found, and 34 of them were among hub-genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)-receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'-kinase (PI3K)-Akt signaling pathway (PI3K-AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.

Long noncoding RNA SBF2-AS1 contributes to the growth and metastatic phenotypes of NSCLC via regulating miR-338-3p/ADAM17 axis.

Non-small cell lung cancer (NSCLC) is a type of refractory malignant lung cancer with a high rate of metastasis and mortality. Currently, long non-coding RNA (lncRNA) SBF2 Antisense RNA 1 (SBF2-AS1) is considered as a biomarker for a variety of tumors. However, the function of SBF2-AS1 in the growth and metastasis of NSCLC needs to be further studied. In this study, we revealed that SBF2-AS1 was overexpressed in NSCLC tissues compared with that in normal tissues. SBF2-AS1 silencing restrained the growth and aggressive phenotypes of NSCLC cell in vitro. Consistently, SBF2-AS1 knockdown hindered the growth of NSCLC cell in nude mice. The following luciferase reporter gene assay and RNA immunoprecipitation (RIP) assay suggested the relationship between miR-338-3p and SBF2-AS1. The rescue experiments showed that miR-338-3p inhibitor abolished SBF2-AS1 silencing caused inhibition on the growth, migration and invasiveness of NSCLC cell. The luciferase reporter assay and immunoblotting assay validated that A Disintegrin and Metalloprotease 17 (ADAM17) was a target of miR-338-3p. In addition, SBF2-AS1 positively regulated the level of ADAM17 through sponging for miR-338-3p. Finally, we revealed that SBF2-AS1 contributed to the proliferation and metastatic phenotypes of NSCLC cell via regulating miR-338-3p/ADAM17 axis.