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ORTHOPEDICS AND TRAUMATOLOGY / CLINICAL RESEARCH
Unveiling the immune-related ceRNA network in osteoarthritis: key biomarkers and therapeutic targets
1
Department of Orthopedic, Huashan Hospital Affiliated to Fudan University, Shanghai, China
Submission date: 2024-06-14
Final revision date: 2024-09-09
Acceptance date: 2024-10-24
Online publication date: 2024-11-02
Corresponding author
Xin Ma
Huashan Hospital Affiliated to Fudan University No. 12 Urumqi Middle Road Jing ‘an District Shanghai 200040, China
Huashan Hospital Affiliated to Fudan University No. 12 Urumqi Middle Road Jing ‘an District Shanghai 200040, China
KEYWORDS
osteoarthritislong non-coding RNAcompetitive endogenous RNAimmune cell infiltrationbiomarkerstherapeutic targets
TOPICS
ABSTRACT
Introduction:
The aim of this study was to explore the immune-related competitive endogenous RNA (ceRNA) network in osteoarthritis (OA), focusing on identifying differentially expressed long non-coding RNAs (lncRNAs), constructing a classification model, and examining the associations between these lncRNAs and immune cell subsets in OA.
Material and methods:
Microarray data from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs) in synovial tissue of OA patients. A classification model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with selected lncRNAs. Computational methods such as CIBERSORT were employed to quantify immune cell infiltration patterns, and weighted gene co-expression network analysis (WGCNA) was conducted to delineate co-expression modules. Finally, a ceRNA network was constructed to elucidate the regulatory interactions among lncRNAs, miRNAs, and mRNAs.
Results:
We identified 5927 DEGs, among which 47 were differentially expressed long non-coding RNAs (DELs). Seven DELs (DGCR11, FAM215A, HCG9, PART1, FAM106A, NOP14-AS1, PRORY) formed the basis of a classification model with an area under the receiver operating characteristic (ROC) curve of 1. We also examined the infiltration patterns of immune cells in OA tissues and found significant differences compared to healthy controls, indicating a strong immunological component in OA pathogenesis. WGCNA identified a turquoise module showing a strong correlation with OA traits.
Conclusions:
The study highlights the importance of ceRNA networks in understanding the complex pathogenesis of OA and offers a comprehensive framework for future research into potential diagnostic biomarkers and therapeutic targets.
The aim of this study was to explore the immune-related competitive endogenous RNA (ceRNA) network in osteoarthritis (OA), focusing on identifying differentially expressed long non-coding RNAs (lncRNAs), constructing a classification model, and examining the associations between these lncRNAs and immune cell subsets in OA.
Material and methods:
Microarray data from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs) in synovial tissue of OA patients. A classification model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with selected lncRNAs. Computational methods such as CIBERSORT were employed to quantify immune cell infiltration patterns, and weighted gene co-expression network analysis (WGCNA) was conducted to delineate co-expression modules. Finally, a ceRNA network was constructed to elucidate the regulatory interactions among lncRNAs, miRNAs, and mRNAs.
Results:
We identified 5927 DEGs, among which 47 were differentially expressed long non-coding RNAs (DELs). Seven DELs (DGCR11, FAM215A, HCG9, PART1, FAM106A, NOP14-AS1, PRORY) formed the basis of a classification model with an area under the receiver operating characteristic (ROC) curve of 1. We also examined the infiltration patterns of immune cells in OA tissues and found significant differences compared to healthy controls, indicating a strong immunological component in OA pathogenesis. WGCNA identified a turquoise module showing a strong correlation with OA traits.
Conclusions:
The study highlights the importance of ceRNA networks in understanding the complex pathogenesis of OA and offers a comprehensive framework for future research into potential diagnostic biomarkers and therapeutic targets.
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| eISSN: | 1896-9151 |
| ISSN: | 1734-1922 |
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