人类衰老过程中血浆和血清循环小非编码rna的特征

IF 2.2 Q3 GERIATRICS & GERONTOLOGY
Aging Medicine Pub Date : 2023-02-22 DOI:10.1002/agm2.12241
Ping Xiao, Zhangyue Shi, Chenang Liu, Darren E. Hagen
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引用次数: 0

摘要

衰老是一个复杂的过程,它通过微环境中的细胞间通讯触发与年龄相关的疾病易感性。虽然已知衰老相关分泌表型(SASP)的经典分泌组,包括可溶性因子、生长因子和细胞外基质重塑酶,在衰老过程中影响组织稳态,但新的SASP成分,细胞外小非编码rna (sncRNAs)对人类衰老的影响尚未得到很好的证实。方法利用细胞外RNA (exRNA) Atlas数据库中健康供者血浆和血清中的446个小RNA-seq样本,通过最大信息系数(MIC)关系确定循环sncRNAs表达与年龄之间的线性和非线性特征。年龄预测因子通过集成机器学习方法(自适应增强、梯度增强和随机森林)生成,并通过机器学习模型中的加权系数确定核心年龄相关sncrna。通过预测与年龄相关的mirna进行功能研究。结果观察到高表达转移rna (tRNAs)和microRNAs (miRNAs)的数量分别与年龄呈正相关和负相关。通过MIC检测了两变量(sncRNA表达和个体年龄)的关系,并建立了基于sncRNA的年龄预测因子,在三种集成机器学习方法中,所有R2值均大于0.96,均方根误差(RMSE)小于3.7年。此外,基于建模鉴定了重要的年龄相关sncrna,年龄相关mirna的生物学途径通过其预测靶标来表征,包括细胞间通讯、癌症和免疫调节的多种途径。总之,本研究为人类衰老过程中循环sncRNAs的表达动态提供了有价值的见解,并可能进一步阐明与年龄相关的sncRNAs功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Characteristics of circulating small noncoding RNAs in plasma and serum during human aging

Characteristics of circulating small noncoding RNAs in plasma and serum during human aging

Objective

Aging is a complicated process that triggers age-related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence-associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established.

Methods

Here, by utilizing 446 small RNA-seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) Atlas data repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age-related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age-related miRNAs.

Results

We observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two-variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs-based age predictors were established, resulting in a forecast performance where all R2 values were greater than 0.96 and root-mean-square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age-related sncRNAs were identified based on modeling and the biological pathways of age-related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation.

Conclusion

In summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age-related sncRNAs functions with further elucidation.

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来源期刊
Aging Medicine
Aging Medicine Medicine-Geriatrics and Gerontology
CiteScore
4.10
自引率
0.00%
发文量
38
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