Abstract

   Osteoarthritis (OA) is a progressive cartilage degradation disease,
   concomitant with synovitis, osteophyte formation, and subchondral bone
   sclerosis. Over 37% of the elderly population is affected by OA, and
   the number of cases is increasing as the global population ages.
   Therefore, the objective of this study was to identify and analyze the
   hub genes of OA combining with comprehensive bioinformatics analysis
   tools to provide theoretical basis in further OA effective therapies.
   Two sample sets of [32]GSE46750 contained 12 pairs OA synovial membrane
   and normal samples harvested from patients as well as [33]GSE98918
   including 12 OA and non-OA patients were downloaded from the Gene
   Expression Omnibus database (GEO) database. Differentially expressed
   genes (DEGs) were identified using Gene Expression Omnibus 2R (GEO2R),
   followed by functional enrichment analysis, protein–protein interaction
   networks construction. The hub genes were identified and evaluated. An
   OA rat model was constructed, hematoxylin and eosin staining, safranin
   O/fast green staining, cytokines concentrations of serum were used to
   verify the model. The hub genes expression level in the knee OA samples
   were verified using RT-qPCR. The top 20 significantly up-regulated and
   down-regulated DEGs were screened out from the two datasets,
   respectively. The top 18 GO terms and 10 KEGG pathways were enriched.
   Eight hub genes were identified, namely MS4A6A, C1QB, C1QC, CD74,
   CSF1R, HLA-DPA1, HLA-DRA and ITGB2. Among them, the hub genes were all
   up-regulated in in vivo OA rat model, compared with healthy controls.
   The eight hub genes identified (MS4A6A, C1QB, C1QC, CD74, CSF1R,
   HLA-DPA1, HLA-DRA and ITGB2) were shown to be associated with OA. These
   genes can serve as disease markers to discriminate OA patients from
   healthy controls.

   Subject terms: Biological techniques, Biotechnology, Cell biology,
   Genetics

Introduction

   Osteoarthritis (OA) is characterised by progressive cartilage
   degradation, synovitis, osteophyte formation, and subchondral bone
   sclerosis^[34]1. Knee OA is one of the most common types of OA,
   affecting 37% of persons aged 60 years or older^[35]2. The prevalence
   of OA is expected to increase due to global aging^[36]3. In many
   previous studies of OA, synovitis plays no role or only severe forms of
   synovitis increase risk in OA, which resulted in a serious lack of
   understanding of the inflammatory conditions of the OA synovial
   fluids^[37]4–[38]6. In fact, synovitis has been shown to be an
   independent risk factor for OA^[39]7 and is one of the important
   pathological factors of the vicious cycle perpetuating OA^[40]8.
   Compared with healthy synovial fluids, OA synovial fluids are known to
   be rich in inflammatory mediators, such as chemokines, cytokines, and
   complement components^[41]9, including tumor necrosis factor α (TNF-α),
   Interleukin-1β (IL-1β), Interleukin-6 (IL-6), matrix
   metalloproteinase-1 and matrix metalloproteinase-13, as well as nitric
   oxide (NO), prostaglandin E2, granulocyte macrophage colony-stimulating
   factor (GM-CSF), and vascular cell adhesion molecule-1^[42]9. Synovitis
   causes articular cartilage and meniscus degeneration^[43]10, and the
   expression and activation of complement components, in particular,
   contributes to the progression of chondropathy^[44]11. The alternative
   pathway of complement seems to play a crucial role in the pathogenesis
   of OA. The C3 and its activating peptide C3a have been shown to belong
   to the alternative pathway of complement, as they have been detected at
   high levels in OA cartilage, synovitis membrane tissues, and cultured
   chondrocytes^[45]12.

   There are currently no effective methods to screen for OA at the early,
   asymptomatic stage of the disease. New disease markers are urgently
   needed in order to identify OA in patients before disease progression.
   Bioinformatics-based methods offer great advantages in the discovery of
   new disease markers. Xia et al. conducted an in-depth bioinformatics
   analysis of OA synovitis samples and found seven unreported hub genes
   in the ferroptosis signalling pathway that could be used as
   OA-associated markers^[46]13. Liu et al. reported SLC3A2 to be a
   ferroptosis signalling pathway protein with clinical value and as a
   potential therapeutic target in treatment of OA, based on
   bioinformatics analysis^[47]14. In another bioinformatics study, Wang
   et al. discovered four cuproptosis-related hub genes that were closely
   related to OA inflammatory microenvironments and had clinical
   application values as markers of OA^[48]15.

   The objective of this study was to identify potential hub genes,
   biomarkers and molecular processes involved in OA progression combining
   comprehensive bioinformatics analysis, which further verified in vivo
   OA rat model. The study may contribute to providing new references for