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Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers.
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- Additional Information
- Source:
Publisher: BioMed Central Country of Publication: England NLM ID: 100968565 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2474 (Electronic) Linking ISSN: 14712474 NLM ISO Abbreviation: BMC Musculoskelet Disord Subsets: MEDLINE
- Publication Information:
Original Publication: London : BioMed Central, [2000-
- Subject Terms:
- Abstract:
Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Ethical Committee of Heilongjiang University of Traditional Chinese Medicine. All procedures were conducted in accordance with the Declaration of Helsinki. Prior to participation, patients provided their written informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
Background: Osteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.
Methods: We employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.
Results: We identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.
Conclusion: Our study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.
Clinical Trial Number: Not applicable.
(© 2024. The Author(s).)
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- Contributed Indexing:
Keywords: Bacterial lipopolysaccharide; Immune cell infiltration; Nomogram; Osteoarthritis
- Accession Number:
0 (Biomarkers)
0 (CCN1 protein, human)
0 (Cysteine-Rich Protein 61)
- Publication Date:
Date Created: 20241205 Date Completed: 20241206 Latest Revision: 20241208
- Publication Date:
20250114
- Accession Number:
PMC11619307
- Accession Number:
10.1186/s12891-024-08124-3
- Accession Number:
39639239
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