Islam Md Khairul, Rahman Md Habibur, Islam Md Rakibul, Islam Md Zahidul, Mamun Md Mainul Islam, Azad A K M, Moni Mohammad Ali
Dept. of Information & Communication Technology, Islamic University, Kushtia-7003, Bangladesh.
Dept. of Computer Science & Engineering, Islamic University, Kushtia-7003, Bangladesh.
Heliyon. 2022 Feb 8;8(2):e08892. doi: 10.1016/j.heliyon.2022.e08892. eCollection 2022 Feb.
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.
系统性硬化症（SSc）是一种自身免疫性疾病，与皮肤结构变化相关，免疫系统会攻击身体。最近的一项荟萃分析报告称，SSc患者合并癌症（包括肺癌（LC）、白血病（LK）和淋巴瘤（LP））时癌症预后发生率较高，但其潜在机制细节尚未揭示。为了填补这一研究空白，开发了生物信息学方法来探索两种疾病之间的合并症相互作用。首先，从不同数据库收集了关于SSc及其合并症的适当基因表达数据集。然后，通过应用开发的流程确定了SSc与其癌症合并症之间的相互联系。该流程被设计为一个通用工作流程，以展示一种前提合并症情况，该情况整合了基因表达数据、组织/器官元数据、基因本体论（GO）、分子途径和其他在线资源，并使用基因集富集分析（GSEA）、途径富集和语义相似性（SS）对其进行分析。该流程在R中实现，可通过我们的Github仓库访问：https://github.com/hiddenntreasure/comorbidity。我们的结果表明，SSc及其癌症合并症共享差异表达基因、功能术语（基因本体论）和途径。这些发现有助于更好地理解疾病途径，我们开发的方法可能适用于任何一组疾病，以发现它们之间的任何关联。医生、研究人员、生物学家和其他人可能会使用这项研究。
