IDENTIFICATION OF KLF9 AND FOSL2 AS ENDOPLASMIC RETICULUM STRESS SIGNATURE GENES IN OSTEOARTHRITIS - With Multiple Machine Learning Approaches

Q: What was the study’s primary objective regarding osteoarthritis?

To screen OA endoplasmic reticulum (ER) stress signature genes using machine learning, aiming to provide new insights and methods for OA treatment.

Q: Which datasets and bioinformatic resources were used to discover candidate genes?

GSE55235 and GSE98918 from GEO were analyzed, and ER stress-related genes were identified from the GeneCard database, followed by differential expression analysis and enrichment analyses.

Q: How were the signature genes selected and validated?

LASSO regression, SVM-RFE, and WGCNA were used to screen intersecting candidates, then validation was performed using an OA meniscus dataset and RT-qPCR in human chondrocytes.

Q: What were the key expression findings for KLF9 and FOSL2 in OA?

KLF9 expression was higher in the OA group than in the control group, while FOSL2 expression was lower, with statistical significance (P < 0.05).

Aug 5, 2026

Upload by River Wang

Objective: This study screens osteoarthritis (OA) endoplasmic reticulum (ER) stress signature genes using machine learning to support new therapeutic insights. GSE55235 and GSE98918 datasets from GEO were integrated with GeneCard-derived ER stress genes, followed by batch correction, differential analysis, GO/KEGG enrichment, and GSEA. LASSO, SVM-RFE, and WGCNA were applied to identify candidate signatures, then validated using an OA meniscus dataset and RT-qPCR in human chondrocytes. KLF9 and FOSL2 were confirmed as OA ER stress signature genes with opposite expression trends.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

1 INTRODUCTION

Computing and Informatics, Vol. 43, 2024, 777–796, doi: 10.31577/cai 2024 4 777 IDENTIFICATION OF KLF9 AND FOSL2 AS ENDOPLASMIC RETICULUM STRESS SIGNATURE GENES IN OSTEOARTHRITIS WITH MULTIPLE MACHINE LEARNING APPROACHES Wenfei Xu, Qijie Mei, Kan Duan The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning, China Chunyu Ming Ruikang Affiliated Hospital of Guangxi Medical University Nanning, China Yanhong Li Guangxi Traditional Chinese Medical University Nanning, China Jinrong Guo The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning, China Qi Hu Guangxi Traditional Chinese Medical University Nanning, China Chao Zeng, Changshen Yuan∗ The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning, China e-mail: changshen [yuan45@outlook. com](yuan45@outlook. com) 778 W. Xu, Q. Mei, K. Duan, C. Ming, Y. Li, J. Guo, Q. Hu, C. Zeng, C. Yuan Abstract. Objective: This study aims to screen osteoarthritis (OA) endoplasmic reticulum (ER) stress signature genes using a machine learning approach to provide new insights and methods for OA treatment. Methods: We obtained GSE55235 and GSE98918 datasets from the gene expression omnibus (GEO) database and identified ER stress-related genes from the GeneCard database. We used R software to perform data batch correction, extract OA endoplasmic reticulum stress-related genes, and conduct differential analysis. We performed functional Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, and gene set enrichment analysis (GSEA) on differentially expressed genes (DEGs) . Additionally, we used machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, SVMRFE, and weighted gene co-expression network analysis (WGCNA), to screen OA endoplasmic reticulum stress signature genes. Human chondrocytes were selected for OA model establishment, cells without any treatment were served as the control. Results: We obtained 236 DEGs related to OA ER stress. GO and KEGG enrichment analysis showed that these genes were mainly involved in the positive regulation of leukocyte activation, collagen-containing extracellular matrix, phagosome, and other biological functions or signaling pathways. GSEA-GO analysis revealed that ER stress genes were significantly enriched in the negative regulation in metabolic processes of nucleobase-containing compounds (NES = −2 .50, P < 0.001), while OA ER stress genes were significantly enriched in the processing and presentation of peptide antigens (NES = 2 .40, P < 0.001) . Using WGCNA analysis, LASSO regression analysis, and SVM-RFE analysis of intersection, we identified KLF9 and FOSL2 as potential OA endoplasmic reticulum stress signature genes, which were found to be more accurate as OA signature genes after validation. KLF9 expression in OA group was higher than that in control group, while FOSL2 expression was lower (P < 0.05) . Conclusion: Machine learning and co-expression network analysis can effectively identify the genes and potential factors characteristic of ER stress in OA, which can help elucidate its pathogenesis and provide a new direction for better clinical treatment. Keywords: Osteoarthritis, endoplasmic reticulum stress, machine learning 1 INTRODUCTION Osteoarthritis (OA) is a chronic, degenerative joint disease characterized by cartilage degeneration, joint pain, and functional impairment. The knee joint is the one most frequently impacted by osteoarthritis (OA) . The protective cartilage that covers the ends of bones in a joint begins to break down in osteoarthritis, a degenerative joint condition. With an aging society, the incidence of OA is on the rise, affecting about 1/3 of the elderly population over 65 years old who suffer from OA [1] . Furthermore, ∗ Corresponding author Identification of KLF9 and FOSL2 as Endoplasmic Reticulum Stress 779 59–87%

FAQs

What was the study’s primary objective regarding osteoarthritis?

To screen OA endoplasmic reticulum (ER) stress signature genes using machine learning, aiming to provide new insights and methods for OA treatment.

Which datasets and bioinformatic resources were used to discover candidate genes?

GSE55235 and GSE98918 from GEO were analyzed, and ER stress-related genes were identified from the GeneCard database, followed by differential expression analysis and enrichment analyses.

How were the signature genes selected and validated?

LASSO regression, SVM-RFE, and WGCNA were used to screen intersecting candidates, then validation was performed using an OA meniscus dataset and RT-qPCR in human chondrocytes.

What were the key expression findings for KLF9 and FOSL2 in OA?

KLF9 expression was higher in the OA group than in the control group, while FOSL2 expression was lower, with statistical significance (P < 0.05).

Aug 5, 2026

Upload by River Wang