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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

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:35902862
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2026-11-01T18:14:59Z
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title
KLF9 and EPYC acting as feature genes for osteoarthritis and their association with immune infiltration.
doi
10.1186/s13018-022-03247-6
pmid
35902862
authors array · 7 items
[0]
Jiayin Zhang
[1]
Shengjie Zhang
[2]
Yu Zhou
[3]
Yuan Qu
[4]
Tingting Hou
[5]
Wanbao Ge
[6]
Shanyong Zhang
journal
Journal of orthopaedic surgery and research
year
2022
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.
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Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation
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Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation [Jena]
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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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