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. 2023 Oct 30;15(20):11672-11689.
doi: 10.18632/aging.205155. Epub 2023 Oct 30.

Identification of key circadian rhythm genes in skin aging based on bioinformatics and machine learning

Xiao Xiao  1 , Hao Feng  1 , Yangying Liao  1 , Hua Tang  1 , Lan Li  1 , Ke Li  1 , Feng Hu  1
Affiliations

Identification of key circadian rhythm genes in skin aging based on bioinformatics and machine learning

Xiao Xiao et al. Aging (Albany NY). .

Abstract

Skin aging is often accompanied by disruption of circadian rhythm and abnormal expression of circadian rhythm-related genes. In this study, we downloaded skin aging expression datasets from the GEO database and utilized bioinformatics and machine learning methods to explore circadian rhythm genes and pathways involved in skin aging, revealing the pathological and molecular mechanisms of skin aging. Results showed that 39 circadian rhythm-related genes (CRGs) were identified in skin aging, and these CRGs were enriched in signaling pathways such as glucagon signaling pathway, insulin resistance, thyroid hormone signaling pathway, and adipocytokine signaling pathway. Three key skin aging-related CRGs, SIRT1, ARNTL, and ATF4, were identified based on machine learning. Additionally, we found that skin aging was associated with infiltration of immune cells including NK cells activated, Macrophages M1, Mast cells resting, T cells CD4 memory activated, and Macrophages M2, and the expression of the three key skin aging-related CRGs was correlated with these immune cells. Finally, SIRT1, ARNTL, and ATF4 were all down-regulated in skin aging and had a good ability to distinguish young skin tissue from aging skin tissue. In conclusion, three key CRGs, including SIRT1, ARNTL, and ATF4, which are closely related to skin aging, were obtained based on bioinformatics and machine learning technology screening. These three key CRGs were potential risk genes for skin aging and also associated with changes in the immune microenvironment in skin aging. The language used in this paragraph follows the guidelines for scientific writing specified by SCI, making it clear and concise.

Keywords: bioinformatics; circadian rhythm; immune infiltration; machine learning; skin aging.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

Figures

Figure 1
Figure 1
Identification of differentially expressed genes in skin aging. (A) Volcanic map of differential expression analysis. (B) The expression heat map of DEGs.
Figure 2
Figure 2
Weighted gene co-expression network analysis (WGCNA) of DEGs in GSE85358 dataset. (A) Scale independence as a function of soft threshold power. (B) Mean connectivity as a function of soft threshold power. (C) Cluster dendrogram. Each color represents a specific co-expression module. (D) Heat map plot showing the connectivity of module eigengenes (MEs). (E) Heat map of the correlation between MEs and clinical traits (Skin aging).
Figure 3
Figure 3
Identification of hub genes highly associated with skin aging from co-expressed gene modules. (A–I) Scatter plot analysis of the module of black, blue, brown, cyan, darkturquoise, greenyellow, grey, pink and royalblue, respectively.
Figure 4
Figure 4
Functional enrichment analysis of hub genes. (A) Molecular function (MF) analysis. (B) Cellular component (CC) analysis. (C) Biological processes (BP) analysis. (D) KEGG pathway enrichment analysis.
Figure 5
Figure 5
Gene set enrichment analysis (GSEA) of GSE85358 dataset. (A) Biological processes associated with circadian rhythms. (B) KEGG signaling pathways associated with circadian rhythms.
Figure 6
Figure 6
Screening of skin aging-related CRGs. (A) Skin aging-related CRGs identification. (B) GO annotation of skin aging-related CRGs. (C) KEGG pathway enrichment analysis of skin aging-related CRGs. (D) The interaction of KEGG signaling pathways and their associated CRGs. (E) The PPI network of skin aging-related CRGs.
Figure 7
Figure 7
Screening for key CRGs by machine learning. (A) LASSO regression model screened the potential key CRGs. (B) SVM-RFE algorithm screened the potential key CRGs. (C) The Venn diagram showed the overlap of key CRGs between the above two machine learning algorithms.
Figure 8
Figure 8
Immune infiltration analysis of GSE85358 dataset. (A) Heat map of immune cell infiltration in skin tissue of old and young groups. (B) Bar graph of immune cell infiltration in skin tissue of old and young groups. (C) Correlation between 22 characteristic immune cells in skin samples.
Figure 9
Figure 9
Comparison of the infiltration of 22 immune cell types in the skin tissue of the old and young groups. Red indicated the old group and blue indicated the young group; p < 0.05 was considered a significant difference.
Figure 10
Figure 10
Correlation of key CRGs in skin tissue with immune cell infiltration. (A) SIRT1. (B) ARNTL. (C) ATF4.
Figure 11
Figure 11
Expression of SIRT1, ARNTL and ATF4. (A) Expression of SIRT1, ARNTL and ATF4 in training set GSE85358. (B) Expression of SIRT1, ARNTL and ATF4 in validation set GSE39170. (C) Expression of SIRT1, ARNTL and ATF4 in validation set GSE120783.
Figure 12
Figure 12
Receiver operating characteristic (ROC) analysis of SIRT1, ARNTL and ATF4. (A) ROC curves of SIRT1, ARNTL and ATF4 in training set GSE85358. (B) ROC curves of SIRT1, ARNTL and ATF4 in validation set GSE39170. (C) ROC curves of SIRT1, ARNTL and ATF4 in validation set GSE120783.
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