Decoding Skin Aging Through Transcriptomic Clocks: Gene Expression Signatures, Associated Pathways, and Explainable...

Area of Science:

• Dermatology
• Genomics
• Computational Biology

Background:

• Skin aging is a complex process influenced by intrinsic and extrinsic factors.
• Chronological age is an insufficient measure of skin's biological aging state.
• Molecular biomarkers are needed to accurately assess skin aging variability.

Purpose of the Study:

• To review transcriptomic aging clocks in human skin.
• To emphasize gene-expression signatures, biological pathways, and computational modeling.
• To evaluate advancements in machine learning and artificial intelligence for skin aging.

Main Methods:

• Targeted literature search in PubMed and IEEE Xplore.
• Focus on gene-expression profiles and computational models.
• Analysis of transcriptomic frameworks like SkinAGE.

Main Results:

• Transcriptomic models capture coordinated changes in senescence, DNA damage, inflammation, and ECM remodeling.
• Gene-expression approaches quantify dynamic and biologically relevant skin aging states.
• Machine learning, including deep learning, enhances aging clock accuracy and interpretability.

Conclusions:

• Transcriptomic aging clocks provide dynamic and personalized insights into skin aging.
• Explainable AI is crucial for understanding and trusting these models.
• Future research will integrate multi-omics data and digital twins for predictive skin aging models.