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pmid:35902862
KLF9 and EPYC acting as feature genes for osteoarthritis and their association with immune infiltration.
Jiayin Zhang · Shengjie Zhang · Yu Zhou · Yuan Qu · Tingting Hou · Wanbao Ge · Shanyong Zhang
Journal of orthopaedic surgery and research · 2022 doi:10.1186/s13018-022-03247-6
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Experiment
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Data
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Analysis
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Execution
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Complete file
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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.\nMETHODS: 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.\nCONCLUSION: EPYC and KLF9 can be viewed as feature genes for osteoarthritis. 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