Bioinformatics-based analysis of the relationship between plasminogen regulatory genes and photoaging.

BACKGROUND

Ultraviolet radiation causes skin photoaging by producing a variety of enzymes, which impact both skin health and hinder beauty. Currently, the early diagnosis and treatment of photoaging remain a challenge. Bioinformatics analysis has strong advantages in exploring core genes and the biological pathways of photoaging.

AIMS

To screen and validate key risk genes associated with plasminogen in photoaging and to identify potential target genes for photoaging.

METHODS

Two human transcriptome datasets were obtained by searching the Gene Expression Omnibus (GEO) database, and the mRNAs in the GSE131789 dataset were differentially analyzed, and then the weighted gene co-expression network analysis (WGCNA) was performed to find out the strongest correlations. Template genes, interaction analysis of differentially expressed genes (DEGs), modular genes with the most WGCNA correlations, and genecard database genes related to plasminogen were performed, and further Kyoto genes and Genome Encyclopedia (KEGG) pathway analysis. Two different algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE), were used to find key genes. Then the data set (GSE206495) was validated and analyzed. Real-time PCR was performed to validate the expression of key genes through in vitro cellular experiments.

RESULTS

IFI6, IFI44L, HRSP12, and BMP4 were screened from datasets as key genes for photoaging and further analysis showed that these genes have significant diagnostic value for photoaging.

CONCLUSION

IFI6, IFI44L, HRSP12, and BMP4 play a key role in the pathogenesis of photoaging, and serve as promising potential predictive biomarkers for photoaging.