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

Abstract

Abstract 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.

Keywords

• aging clock
• circulating sncRNAs
• human aging
• machine learning