{"title":"基于基因表达谱的银屑病自噬相关关键基因筛选","authors":"Suo Mo, Chunyan Cao, Xiaoyue Dai, Zhiwen Ding, Yajuan Zuo, Chuchu Song, Xianfeng Cheng","doi":"10.5114/ada.2024.145618","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Autophagy is necessary for the progression of psoriasis.</p><p><strong>Aim: </strong>This study aimed to recognize possible autophagy-related genes in psoriasis via bioinformatics study to present a better standard for the clinical treatment and management of psoriasis.</p><p><strong>Material and methods: </strong>The GEO dataset was utilized to derive the mRNA expression profile of the database GSE78097. R software was utilized to find autophagy-associated genes that may be expressed in psoriasis. Then, a protein-protein interaction (PPI) correlation study of the differentially expressed autophagy-associated genes was carried out, and GO and KEGG enrichment analysis was used to investigate any potential signalling pathways linked.</p><p><strong>Results: </strong>We identified a total of 156 autophagy-related genes in 27 psoriasis and 6 healthy skin tissue samples. The PPI network diagram findings demonstrate interactions among these autophagy-associated genes. Autophagy, protein processing, apoptosis, and mitochondria processes may be crucial in the development and occurrence of psoriasis suggested by KEGG and GO enrichment analysis.</p><p><strong>Conclusions: </strong>Utilizing bioinformatics methods to recognize differentially expressed autophagy-related genes has further enhanced our understanding of psoriasis and provided new thinking for the study of the pathogenesis and therapeutic targets of psoriasis.</p>","PeriodicalId":54595,"journal":{"name":"Postepy Dermatologii I Alergologii","volume":"41 6","pages":"577-583"},"PeriodicalIF":1.4000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770576/pdf/","citationCount":"0","resultStr":"{\"title\":\"Screening of key genes related to autophagy in psoriasis based on gene expression profiling.\",\"authors\":\"Suo Mo, Chunyan Cao, Xiaoyue Dai, Zhiwen Ding, Yajuan Zuo, Chuchu Song, Xianfeng Cheng\",\"doi\":\"10.5114/ada.2024.145618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Autophagy is necessary for the progression of psoriasis.</p><p><strong>Aim: </strong>This study aimed to recognize possible autophagy-related genes in psoriasis via bioinformatics study to present a better standard for the clinical treatment and management of psoriasis.</p><p><strong>Material and methods: </strong>The GEO dataset was utilized to derive the mRNA expression profile of the database GSE78097. R software was utilized to find autophagy-associated genes that may be expressed in psoriasis. Then, a protein-protein interaction (PPI) correlation study of the differentially expressed autophagy-associated genes was carried out, and GO and KEGG enrichment analysis was used to investigate any potential signalling pathways linked.</p><p><strong>Results: </strong>We identified a total of 156 autophagy-related genes in 27 psoriasis and 6 healthy skin tissue samples. The PPI network diagram findings demonstrate interactions among these autophagy-associated genes. Autophagy, protein processing, apoptosis, and mitochondria processes may be crucial in the development and occurrence of psoriasis suggested by KEGG and GO enrichment analysis.</p><p><strong>Conclusions: </strong>Utilizing bioinformatics methods to recognize differentially expressed autophagy-related genes has further enhanced our understanding of psoriasis and provided new thinking for the study of the pathogenesis and therapeutic targets of psoriasis.</p>\",\"PeriodicalId\":54595,\"journal\":{\"name\":\"Postepy Dermatologii I Alergologii\",\"volume\":\"41 6\",\"pages\":\"577-583\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11770576/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Postepy Dermatologii I Alergologii\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.5114/ada.2024.145618\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ALLERGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Postepy Dermatologii I Alergologii","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.5114/ada.2024.145618","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/18 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ALLERGY","Score":null,"Total":0}
Screening of key genes related to autophagy in psoriasis based on gene expression profiling.
Introduction: Autophagy is necessary for the progression of psoriasis.
Aim: This study aimed to recognize possible autophagy-related genes in psoriasis via bioinformatics study to present a better standard for the clinical treatment and management of psoriasis.
Material and methods: The GEO dataset was utilized to derive the mRNA expression profile of the database GSE78097. R software was utilized to find autophagy-associated genes that may be expressed in psoriasis. Then, a protein-protein interaction (PPI) correlation study of the differentially expressed autophagy-associated genes was carried out, and GO and KEGG enrichment analysis was used to investigate any potential signalling pathways linked.
Results: We identified a total of 156 autophagy-related genes in 27 psoriasis and 6 healthy skin tissue samples. The PPI network diagram findings demonstrate interactions among these autophagy-associated genes. Autophagy, protein processing, apoptosis, and mitochondria processes may be crucial in the development and occurrence of psoriasis suggested by KEGG and GO enrichment analysis.
Conclusions: Utilizing bioinformatics methods to recognize differentially expressed autophagy-related genes has further enhanced our understanding of psoriasis and provided new thinking for the study of the pathogenesis and therapeutic targets of psoriasis.