Development and validation of an age prediction model using mRNA markers

Area of Science:

• Genomics and Molecular Biology
• Aging Research
• Biomarker Discovery

Background:

• Messenger RNA (mRNA) profiles reflect age-associated molecular changes.
• mRNA analysis holds potential for accurate age estimation.
• Understanding age-related gene expression is crucial for gerontology.

Purpose of the Study:

• To identify age-related genes (ARGs) using mRNA sequencing data.
• To develop and validate a predictive model for chronological age estimation.
• To explore molecular mechanisms underlying aging processes.

Main Methods:

• RNA sequencing on peripheral blood from 127 healthy Chinese individuals (18-80 years).
• Differential gene expression analysis, KEGG, and GO enrichment analysis.
• Spearman correlation, Lasso regression, and machine learning models (Elastic Net) for ARG selection and age prediction.

Main Results:

• Identified 79 differentially expressed genes (DEGs), enriched in interferon response and cell adhesion pathways.
• Selected 34 candidate ARGs, including seven novel ARGs (ARHGEF4, ARF6, AMIGO1, FITM2, PLEKHG4, SLC5A10, HKR1).
• Developed an Elastic Net model with Mean Absolute Error (MAE) of 6.72 years (test set), demonstrating robust age prediction.

Conclusions:

• Age-associated mRNA expression patterns provide insights into aging mechanisms.
• A 34-mRNA signature offers a robust and accurate method for age prediction.
• The identified ARGs and predictive model contribute to aging research and potential clinical applications.