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pmid:40474128

Exploring heat shock protein-related biomarkers and subgroups in osteoarthritis using Gene Expression Omnibus data sets.

Weixin Cai · Boxin Yan · Hanbing Song · Jiaxing Liu · Geqiang Wang · Yichang Jiang

BMC musculoskeletal disorders · 2025 doi:10.1186/s12891-025-08827-1

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

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:40474128
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2026-11-01T20:30:35Z
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2026-11-01T22:56:37Z
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paper object · 10 fields
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title
Exploring heat shock protein-related biomarkers and subgroups in osteoarthritis using Gene Expression Omnibus data sets.
doi
10.1186/s12891-025-08827-1
pmid
40474128
authors array · 6 items
[0]
Weixin Cai
[1]
Boxin Yan
[2]
Hanbing Song
[3]
Jiaxing Liu
[4]
Geqiang Wang
[5]
Yichang Jiang
journal
BMC musculoskeletal disorders
year
2025
abstract
Osteoarthritis (OA) is a degenerative joint disease marked by cartilage deterioration. Recent studies suggest that heat shock proteins (HSPs) may play a role in the pathophysiology of OA. This study explores the role of heat shock protein-related genes (HSRs) in OA using GEO datasets (GSE12021, GSE55457, GSE55235). Differential expression analysis identified 34 HSRs with significant changes in OA. Protein-protein interaction (PPI) network analysis highlighted central HSRs, including HSPA5, HSPA6, HSPA1L, HSPA12 A, and HSPH1, indicating their key roles in OA. Enrichment analysis revealed that HSRs are involved in protein folding, chaperone activities, and endoplasmic reticulum stress. A nomogram developed from HSR expression profiles demonstrated high accuracy in predicting OA risk. Clustering analysis identified two distinct HSR-related subgroups with unique gene expression and immune profiles. Variations in immune cell infiltration and immune pathway activities were observed between these subgroups. Overall, the study underscores the significance of HSRs in OA pathogenesis and suggests potential personalized therapeutic strategies based on distinct HSR-related subgroups.
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We employed the limma package to identify differentially expressed genes (DEGs) between the healthy controls (HC) and OA groups.
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sections[4] "Identification of genes with differential expression" paragraph 2
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We employed the limma package to identify differentially expressed genes (DEGs) between the healthy controls (HC) and OA groups.
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The data utilized in this study were sourced from the GEO database, accessible at https://www.ncbi.nlm.nih.gov/geo/. The corresponding accession numbers are: GSE55457, GSE12021, and GSE55235.
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Integrated analysis of three GEO datasets identified 34 heat shock-related genes with significant changes in osteoarthritis.
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Differential expression analysis identified 34 HSRs with significant changes in OA.
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sections[3] "Data acquisition and data preparation" paragraph 2
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The data sets for our research were sourced from the NCBI Gene Expression Omnibus (GEO) database.
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sections[3] "Data acquisition and data preparation" paragraph 2
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We specifically employed three OA data sets (GSE12021, GSE55457, and GSE55235) [16, 17].
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Integrated analysis of three GEO datasets identified 34 heat shock-related genes with significant changes in osteoarthritis.
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Identification of inter-individual and gene-specific variances in mRNA expression profiles in the RA SM
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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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2026-11-01T15:39:54Z
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We employed the limma package to identify differentially expressed genes (DEGs) between the healthy controls (HC) and OA groups.
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Following this, qRT-PCR was performed on the Real-Time PCR System (Applied Biosystems, USA) utilizing S
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We employed the limma package to identify differentially expressed genes (DEGs) between the healthy controls (HC) and OA groups.
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Complete file
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This study explores the role of heat shock protein-related genes (HSRs) in OA using GEO datasets (GSE12021, GSE55457, GSE55235). Differential expression analysis identified 34 HSRs with significant changes in OA. Protein-protein interaction (PPI) network analysis highlighted central HSRs, including HSPA5, HSPA6, HSPA1L, HSPA12 A, and HSPH1, indicating their key roles in OA. Enrichment analysis revealed that HSRs are involved in protein folding, chaperone activities, and endoplasmic reticulum stress. A nomogram developed from HSR expression profiles demonstrated high accuracy in predicting OA risk. Clustering analysis identified two distinct HSR-related subgroups with unique gene expression and immune profiles. Variations in immune cell infiltration and immune pathway activities were observed between these subgroups. 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