KLF9 and EPYC acting as feature genes for osteoarthritis and their association with immune infiltration

Abstract

Background: Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level.

Methods: From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed.

Conclusion: EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration.

Keywords: Bioinformatics; EPYC; Immune infiltration; KLF9; Machine learning; Osteoarthritis.

Fig. 1

Screening of differentially expressed genes. a Volcano map of DEGs; red represents up-regulated differential genes, black represents no significant difference genes, and green represents down-regulated differential genes. b The thermal map of expression level of different genes in every synovial tissue sample, the redder the color, the higher the expression, the bluer the color, the lower the expression

Fig. 2

Gene ontology (GO), disease ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs. a GO enrichment analysis, where the horizontal axis represents the number of DEGs under the GO term. b DO enrichment analysis, where the horizontal axis represents the number of DEGs under the DO term. c KEGG enrichment analysis, where the horizontal axis represents the number of DEGs under the KEGG term

Fig. 3

Gene GO and KEGG enrichment analysis of all normal genes and all OA genes. a GSEA-GO enrichment analysis on all normal genes, saved the top five enriched pathways. b GSEA-GO enrichment analysis on all OA genes, saved the top five enriched pathways. c GSEA-KEGG enrichment analysis on all normal genes, saved the top five enriched pathways. d GSEA-KEGG enrichment analysis on all OA genes, saved the top five enriched pathways

Fig. 4

Screening of diagnostic markers. a Least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to screen diagnostic markers. b Support vector machine–recursive feature elimination (SVM-RFE) algorithm to screen diagnostic markers. c Venn diagram shows the intersection of diagnostic markers obtained by the two algorithms

Fig. 5

ROC curve of KLF9 (a) and EPYC (b) genes in the training and validation  set

Fig. 6

Box diagram of difference analysis of the expression levels of KLF9 (a) and EPYC (b) in the validation set. The blue marks represent the normal; the red marks represent the OA

Fig. 7

Evaluation and visualization of immune cell infiltration. a Content of different immune cells in each sample. b Correlation heat map of 22 types of immune cells. Red represents a positive correlation; blue represents a negative correlation. The darker the color, the stronger the correlation. c Violin diagram of the proportion of 22 types of immune cells. The red marks represent the difference in infiltration between the two groups of samples

Fig. 8

Correlation between KLF9 gene expression and different immune cells infiltrating

Fig. 9

Correlation between EPYC gene expression and different immune cells infiltrating

Fig. 10

Correlation between KLF9, EPYC, and infiltrating immune cells. a Correlation between KLF9 and infiltrating immune cells. b Correlation between EPYC and infiltrating immune cells. The size of the dots represents the strength of the correlation between genes and immune cells; the larger the dots, the stronger the correlation, and the smaller the dots, the weaker the correlation. The color of the dots represents the p value; the greener the color, the lower the p value, and the yellower the color, the larger the p value. p value < 0.05 was considered statistically significant