Idiopathic inflammatory myopathy (IIM) comprises a heterogeneous group of diseases, collectively referred to as myositis, that are characterized by autoimmune muscle inflammation and are frequently accompanied by extramuscular manifestations that affect the skin, lungs, and joints [1,2]. IIM encompasses dermatomyositis (DM), polymyositis (PM), inclusion body myositis (IBM), immune-mediated necrotizing myopathy (NM), and antisynthetase syndrome (AS) [2,3]. The identification of different histopathological features in muscle tissues and discovery of myositis-specific autoantibodies (MSAs) have helped in understanding the immunological background of IIM and classifying the IIM subtypes [3,4]. In addition, the pathogenic mechanisms of immune-mediated and non-immune-mediated pathways have been partially elucidated [5,6]. However, MSAs are present only in up to 60% of patients with IIM, and certain MSAs are more closely associated with extramuscular manifestations than with myositis itself [3]. The divergent and shared mechanistic features of each IIM subtype need to elaborated.

Gene-expression profiling has been used to gain insight into the pathogenesis of autoimmune rheumatic diseases, such as rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, and Sjögren's syndrome. Recent gene-expression profile data obtained with muscle biopsies from patients with IIM revealed novel differentially expressed genes (DEGs) and dysregulated myositis-related modules in the IIM biological network and offer promise for a developing contextualized approach for IIM [7,8]. Integrated analysis of multiple datasets provides advantages over individual studies because the sampling bias is reduced and general dynamic relationships can be examined during gene regulation [9]. In this study, we systematically searched the biomedical literature and public data repositories for transcriptomic datasets obtained using muscle tissues from patients with IIM and then performed an integrated analysis consisting of expression-driven cellular and molecular characterization. We probed pathologically enriched processes and used an algorithm to identify disease-network modules in order to prioritize novel disease-associated genes for each IIM. We then delineated the unifying and versatile molecular signatures across IIMs and narrowed the mechanistic process to targets with therapeutic value against IIMs.