Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.