Abstract Background Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. Methods An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. Results A total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues. Conclusions Analysis indicated that there were common pathological clusters that contributed to the pathogenesis of SSc in different tissues. Moreover, it seems that the common pathways in distinct tissues stem from a diverse set of genes. Keywords: Systemic sclerosis, Functional analysis, Common pathway, Integrative gene expression analysis Background Systemic sclerosis (SSc) is a rare, multisystemic, autoimmune disease that involves the skin and various internal organs, including the lungs, gastrointestinal tract, heart, and kidneys. The exact pathogenesis of SSc remains unknown, but it seems that vascular abnormalities, inflammation, dysregulation of immune system, and extracellular matrix (ECM) deposition can lead to progressive connective tissue fibrosis. Organ failures that arise from fibrosis are the most significant causes of mortality in SSc patients [[39]1, [40]2]. Although the etiopathogenesis of SSc has not been well identified, accumulated evidence suggests that multiple genes and their interactions with environmental factors play important roles in this context [[41]3, [42]4]. Traditional researches have been performed in order to demonstrate the involvement of a particular gene or protein in SSc physiopathology [[43]5, [44]6]. Although these studies generate invaluable data, they provide a small amount of evidence that is insufficient to clarify the complex interactions between multiple genes or proteins simultaneously. Consequently, it is essential to utilize new approaches for realizing the alterations of different genes and pathways in complicated pathological conditions, like SSc [[45]7, [46]8]. These approaches could have a major role in the holistic understanding of complex disease patterns and developing effective therapies. Microarrays have been extensively applied for understanding biological mechanisms, discovering new drug targets, and evaluating drug responses [[47]9, [48]10]. In addition, results obtained from microarray technology might be helpful in generating abundant complex datasets that mostly address the same biological inquiries [[49]11–[50]17]. Integration of relevant gene expression datasets can improve the reliability of the outputs and facilitate the identification of altered molecular pathways and complex disease pathogeneses [[51]8, [52]18, [53]19]. Skin involvement is one of the most common clinical manifestations of SSc and is known to be a key marker of disease activity [[54]20]. The lung is frequently involved in SSc, and such condition is known as the major cause of death among SSc patients [[55]21]. PBMC is a valuable resource for investigating the immune responses involved in autoimmune diseases like SSc [[56]22]. The involvement of multiple organs makes it difficult to recognize the SSc pathogenesis. Moreover, it is not yet clearly understood what pathways may affect SSc development in different organs [[57]23]. Consequently, the present study accomplished an integrative analysis of microarray gene expression data of PBMC as well as the lungs and skin of SSc patients to identify the shared and tissue-specific pathways involved in different tissues. Methods Methods flowchart The method procedures and steps are illustrated in Fig. [58]1. Fig. 1. Fig. 1 [59]Open in a new tab Flowchart of methods Gene expression dataset selection Gene Expression Omnibus (GEO) ([60]https://www.ncbi.nlm.nih.gov/geo/) was searched for gene expression datasets regarding SSc [[61]24]. Datasets containing case and control samples were selected. In addition, only SSc patients who had received no treatment were included. A total of 10 datasets possessed the selection criteria and were selected for this study. Three datasets for lung tissue (accession number: [62]GSE81292, [63]GSE48149, and [64]GSE76808), three datasets for PBMC (accession number: [65]GSE19617, [66]GSE22356, and [67]GSE33463), and four datasets for skin tissue (accession number: [68]GSE32413, [69]GSE45485, [70]GSE9285, and [71]GSE76807) were selected. The selected datasets comprised 69 (52 cases and 17 controls), 186 (125 cases and 61 controls), and 88 (30 cases and 58 controls) samples for lung, PBMC, and skin, respectively. Table [72]1 provides detailed information of each dataset and highlights the first author, tissue type, accession number, and references.