Screening and validation for core genes in osteoarthritic cartilage based on weighted gene co-expression network analysis

S.-Q. Wang, W.-P. Xie, L. Yue, Y.-L. Cai

First Clinical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China. 280647730@qq.com

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• Bioinformatics

OBJECTIVE: Osteoarthritis (OA) has the highest disability rate among chronic diseases. The burden on patients and public health care resources is increasingly evident due to increasing obesity rates and aging populations. So, there is still a lack of early diagnosis and treatment for OA.

MATERIALS AND METHODS: A total of three OA cartilage tissue datasets (GSE1919, GSE32317, and GSE5235) were obtained from the Gene Expression Omnibus (GEO) database. Screening of differentially expressed genes and WGCNA of overlapping genes were performed using the R language package. Functional and immune infiltration analyses of overlapping genes were also carried out while hub genes were screened through LASSO regression analysis method and ROC curve. Finally, experimental validation was carried out through PCR and Western Blot analysis of rat cartilage.

RESULTS: A total of 149 differentially expressed genes were screened, and they were mainly enriched in the cytokine-cytokine receptor interaction, rheumatoid arthritis, and interleukin (IL-17) signaling pathways. Four co-expression modules were obtained, of which the blue module was the most substantial morbidity associated with OA. Thirteen overlapping genes were identified based on significant module network topology analysis and differential genes, upon which their validation through LASSO regression analysis method and ROC curve was performed. From these, five signature genes were determined, before three potential core genes were finally identified after confirmation using the validation set.

CONCLUSIONS: ATF3, FOSL2, and GADD45B may be hub genes to the osteochondropathy, and they are expected to be new biomarkers and drug targets in OA research.

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