{"title":"Bioinformatics Analysis of Lactylation-related Biomarkers and Potential Pathogenesis Mechanisms in Age-related Macular Degeneration.","authors":"Chenwei Gui, Yan Gao, Rong Zhang, Guohong Zhou","doi":"10.2174/0113892029291661241114055924","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.</p><p><strong>Objectives: </strong>The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.</p><p><strong>Methods: </strong>Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.</p><p><strong>Results: </strong>A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including <i>HMGN2</i>, <i>TOP2B</i>, <i>HNRNPH1</i>, <i>SF3A1</i>, <i>SRRM2</i>, <i>HIST1H1C</i>, and <i>HIST1H2BD</i> were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.</p><p><strong>Conclusion: </strong>We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.</p>","PeriodicalId":10803,"journal":{"name":"Current Genomics","volume":"26 3","pages":"191-209"},"PeriodicalIF":1.8000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105335/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Genomics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/0113892029291661241114055924","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/2 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.
Objectives: The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.
Methods: Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.
Results: A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including HMGN2, TOP2B, HNRNPH1, SF3A1, SRRM2, HIST1H1C, and HIST1H2BD were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.
Conclusion: We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.
期刊介绍：
Current Genomics is a peer-reviewed journal that provides essential reading about the latest and most important developments in genome science and related fields of research. Systems biology, systems modeling, machine learning, network inference, bioinformatics, computational biology, epigenetics, single cell genomics, extracellular vesicles, quantitative biology, and synthetic biology for the study of evolution, development, maintenance, aging and that of human health, human diseases, clinical genomics and precision medicine are topics of particular interest. The journal covers plant genomics. The journal will not consider articles dealing with breeding and livestock.
Current Genomics publishes three types of articles including:
i) Research papers from internationally-recognized experts reporting on new and original data generated at the genome scale level. Position papers dealing with new or challenging methodological approaches, whether experimental or mathematical, are greatly welcome in this section.
ii) Authoritative and comprehensive full-length or mini reviews from widely recognized experts, covering the latest developments in genome science and related fields of research such as systems biology, statistics and machine learning, quantitative biology, and precision medicine. Proposals for mini-hot topics (2-3 review papers) and full hot topics (6-8 review papers) guest edited by internationally-recognized experts are welcome in this section. Hot topic proposals should not contain original data and they should contain articles originating from at least 2 different countries.
iii) Opinion papers from internationally recognized experts addressing contemporary questions and issues in the field of genome science and systems biology and basic and clinical research practices.
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