银屑病
计算生物学
基因
转录组
基因表达
生物
遗传学
免疫学
作者
Yue-Min Zou,Man-Ning Wu,Ya-Nan Jiang,Dongmei Zhou
出处
期刊:Research Square - Research Square
日期:2023-04-27
标识
DOI:10.21203/rs.3.rs-2837234/v1
摘要
Abstract PANoptosis is a pivotal process in the pathway of cell death, which affects various cell types, including keratinocytes, and is linked to several autoimmune disorders. While apoptosis, necroptosis, and pyroptosis have been investigated in psoriasis, the precise involvement of PANoptosis in this condition remains largely unexplored. We gathered psoriasis-related data and PANoptosis-related genetic information from authoritative sources such as the GeneCards and Gene Expression Omnibus (GEO). In this particular study, we employed the reliable technique of robust rank aggregation to detect any notable alterations in gene expression (PEGs) between individuals with psoriasis and control subjects. Our approach involved the integration of six distinct gene expression datasets of PANoptosis. TCN1, S100A12, PRKCQ, and ABCC1 in four PRGs were subsequently identified as marker genes with tolerable diagnostic ability by LASSO and SVM-RFE. Following the analysis, it was revealed that the identified marker genes may potentially contribute to the cause of psoriasis by facilitating the regulation of various pathways, such as cell cycle, immune response, and several other pathways associated with this condition. In addition, the differentiated expressions of the marker gene in psoriasis and normal samples were confirmed by the validation set. And the enrichment of marker genes in keratin-forming cells was verified by single-cell validation. Ultimately, the validated genes were employed to prognosticate the efficacious pharmaceutical treatments for psoriasis by utilizing the DGIdb/CMap database. Herb database were used to find relevant natural agents. We have conceived a model that exhibits significant diagnostic efficacy and has yielded valuable insights for exploring the underlying mechanisms of psoriasis. However, additional research is necessary to verify its diagnostic potential for psoriasis before its implementation in clinical settings.
科研通智能强力驱动
Strongly Powered by AbleSci AI