Effects of Collagen-Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen-glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

Automated summary

In this study, the gene expression profiles of cell lines in contact with collagen-glycosaminoglycan mesh were compared to control cells, revealing genes with altered expression in the treated cell lines through principal component analysis-based feature extraction. The identified genes were found to be enriched in various biological terms, indicating the effectiveness of this method over traditional linear regression-based gene selection with time course analysis.