Autoimmunity is defined as the inability to regulate immunological activities in the body, especially in response to external triggers, leading to the attack of the tissues and organs of the host. Outcomes include the onset of autoimmune diseases whose effects are primarily due to dysregulated immune responses. In past years, there have been cases that show an increased susceptibility to other autoimmune disorders in patients who are already experiencing the same type of disease. Research in this field has started analyzing the potential molecular and cellular causes of this interconnectedness, bearing in mind the possibility of advancing drugs and therapies for the treatment of autoimmunity. With that, this study aimed to determine the correlation of four autoimmune diseases, which are type 1 diabetes (T1D), psoriasis (PSR), systemic sclerosis (SSc), and systemic lupus erythematosus (SLE), by identifying highly preserved co-expressed genes among datasets using WGCNA. Functional annotation was then employed to characterize these sets of genes based on their systemic relationship as a whole to elucidate the biological processes, cellular components, and molecular functions of the pathways they are involved in. Lastly, drug repurposing analysis was performed to screen candidate drugs for repositioning that could regulate the abnormal expression of genes among the diseases. A total of thirteen modules were obtained from the analysis, the majority of which were associated with transcriptional, post-transcriptional, and post-translational modification processes. Also, the evaluation based on KEGG suggested the possible role of TH17 differentiation in the simultaneous onset of the four diseases. Furthermore, clomiphene was the top drug candidate for regulating overexpressed hub genes; meanwhile, prilocaine was the top drug for regulating under-expressed hub genes. This study was geared towards utilizing transcriptomics approaches for the assessment of microarray data, which is different from the use of traditional genomic analyses. Such a research design for investigating correlations among autoimmune diseases may be the first of its kind.
