Transcriptomic and Metagenomic Biomarkers in Peri-Implantitis: A Systematic Review, Diagnostic Meta-Analysis, and Functional Meta-Synthesis.

IF 4.4 Q1 Medicine
Carlos M Ardila, Eliana Pineda-Vélez, Anny M Vivares-Builes
{"title":"Transcriptomic and Metagenomic Biomarkers in Peri-Implantitis: A Systematic Review, Diagnostic Meta-Analysis, and Functional Meta-Synthesis.","authors":"Carlos M Ardila, Eliana Pineda-Vélez, Anny M Vivares-Builes","doi":"10.3390/medsci13030187","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background/Objectives:</b> Evidence from transcriptomic and histopathologic studies has revealed that peri-implantitis lesions are characterized by deeper inflammatory infiltration, increased immune cell accumulation, and distinctive molecular signatures. This systematic review aimed to evaluate the diagnostic and pathophysiological potential of transcriptomic, metagenomic, and bioinformatic biomarkers in peri-implantitis by integrating findings from bioinformatics and machine learning-based studies. The dual objective was to identify biologically relevant markers and assess the accuracy of predictive models, addressing diagnostic gaps in peri-implant disease management. <b>Methods:</b> Eligible designs included cross-sectional, case-control, and cohort studies. Literature searches were conducted across PubMed, EMBASE, Scielo, and Scopus, with independent screening, data extraction, and quality assessment. Functional meta-synthesis was used to thematically organize biomarkers and pathways, while diagnostic meta-analysis pooled ROC-AUC values to assess model performance. <b>Results:</b> Eleven studies met the inclusion criteria. Functional synthesis revealed five recurring biomarker themes: innate and adaptive immune responses, immune cell infiltration, fibroblast activation, and ceRNA regulation. A meta-analysis of six studies reported a pooled AUC of 0.91 (95% CI: 0.88-0.93) with I<sup>2</sup> = 0%, indicating no heterogeneity, supporting the reliability of ML-based models in distinguishing peri-implantitis from healthy conditions. Sources of variation included differences in validation strategies and data preprocessing. <b>Conclusions:</b> Integrating transcriptomic, metagenomic, and bioinformatic biomarkers with machine learning may enable earlier and more accurate diagnosis of peri-implantitis. The identified biomarkers highlight molecular and microbial pathways linked to inflammation and tissue remodeling, underscoring their potential as diagnostic indicators and therapeutic targets with translational relevance.</p>","PeriodicalId":74152,"journal":{"name":"Medical sciences (Basel, Switzerland)","volume":"13 3","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12452457/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical sciences (Basel, Switzerland)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/medsci13030187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

Background/Objectives: Evidence from transcriptomic and histopathologic studies has revealed that peri-implantitis lesions are characterized by deeper inflammatory infiltration, increased immune cell accumulation, and distinctive molecular signatures. This systematic review aimed to evaluate the diagnostic and pathophysiological potential of transcriptomic, metagenomic, and bioinformatic biomarkers in peri-implantitis by integrating findings from bioinformatics and machine learning-based studies. The dual objective was to identify biologically relevant markers and assess the accuracy of predictive models, addressing diagnostic gaps in peri-implant disease management. Methods: Eligible designs included cross-sectional, case-control, and cohort studies. Literature searches were conducted across PubMed, EMBASE, Scielo, and Scopus, with independent screening, data extraction, and quality assessment. Functional meta-synthesis was used to thematically organize biomarkers and pathways, while diagnostic meta-analysis pooled ROC-AUC values to assess model performance. Results: Eleven studies met the inclusion criteria. Functional synthesis revealed five recurring biomarker themes: innate and adaptive immune responses, immune cell infiltration, fibroblast activation, and ceRNA regulation. A meta-analysis of six studies reported a pooled AUC of 0.91 (95% CI: 0.88-0.93) with I2 = 0%, indicating no heterogeneity, supporting the reliability of ML-based models in distinguishing peri-implantitis from healthy conditions. Sources of variation included differences in validation strategies and data preprocessing. Conclusions: Integrating transcriptomic, metagenomic, and bioinformatic biomarkers with machine learning may enable earlier and more accurate diagnosis of peri-implantitis. The identified biomarkers highlight molecular and microbial pathways linked to inflammation and tissue remodeling, underscoring their potential as diagnostic indicators and therapeutic targets with translational relevance.

植入物周围炎的转录组和宏基因组生物标志物:系统综述、诊断荟萃分析和功能荟萃综合。
背景/目的:来自转录组学和组织病理学研究的证据表明,种植体周围病变的特征是更深的炎症浸润、免疫细胞积累增加和独特的分子特征。本系统综述旨在通过整合生物信息学和基于机器学习的研究结果,评估转录组学、宏基因组学和生物信息学生物标志物在种植体周围炎中的诊断和病理生理潜力。双重目标是确定生物学相关标记物并评估预测模型的准确性,解决种植体周围疾病管理中的诊断差距。方法:符合条件的设计包括横断面、病例对照和队列研究。通过PubMed、EMBASE、Scielo和Scopus进行文献检索,并进行独立筛选、数据提取和质量评估。功能元合成用于按主题组织生物标志物和途径,而诊断元分析汇总ROC-AUC值来评估模型的性能。结果:11项研究符合纳入标准。功能合成揭示了五个反复出现的生物标志物主题:先天和适应性免疫反应、免疫细胞浸润、成纤维细胞激活和ceRNA调节。6项研究的荟萃分析报告了合并AUC为0.91 (95% CI: 0.88-0.93), I2 = 0%,表明无异质性,支持基于ml的模型在区分种植体周围炎和健康状况方面的可靠性。变异的来源包括验证策略和数据预处理的差异。结论:将转录组学、宏基因组学和生物信息学生物标志物与机器学习相结合,可以更早、更准确地诊断种植体周围炎。已确定的生物标志物突出了与炎症和组织重塑相关的分子和微生物途径,强调了它们作为诊断指标和具有翻译相关性的治疗靶点的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.00
自引率
0.00%
发文量
0
审稿时长
6 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信