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

• DOI: 10.1016/j.heliyon.2022.e08892
• Publication Date: 2022-02-08T22:38:05Z
• Authors: Md Khairul Islam; Md. Habibur Rahman; Md Rakibul Islam; Md Zahidul Islam; Md Mainul Islam M...; A.K.M. Azad; Mohammad Ali Moni
• Keywords: FOS: Computer and information sciences; 0301 basic medicine; Gene set enrichment analysis; Science (General); Bioinformatics; Systemic Sclerosis; Comorbidity; Associations; Gene; Comorbidities; Pathology and Forensic Medicine; Computational biology; Q1-390; 03 medical and health sciences; Biochemistry, Genetics and Molecular Biology; Health Sciences; Genetics; Disease; Molecular Biology; Biology; Internal medicine; Pathogenesis and Treatment of Systemic Sclerosis; Cancer; H1-99; Life Sciences; Correlation; Analysis of Gene Interaction Networks; 3. Good health; Social sciences (General); Genomic Studies and Association Analyses; FOS: Biological sciences; Medicine; Systems biology; Research Article

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