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Identification and analysis of diverse cell death patterns in osteomyelitis via microarray-based transcriptome
Tianxuan Feng1,2, Peisheng Chen2, Fengfei Lin1,2
1Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Background:
Osteomyelitis (OM) is a debilitating infectious disease characterized by inflammation of the bone and bone marrow. Emerging evidence suggests that multiple forms of programmed cell death (PCD) contribute to its pathogenesis. However, the specific roles and interactions of these PCD types in OM remain largely undefined.
Methods:
Microarray-based transcriptome datasets related to OM were retrieved from the Gene Expression Omnibus (GEO) database. Thirteen PCD modalities were defined from the literature and specialized databases, including classical forms (e.g., apoptosis, autophagy) and non-classical forms (e.g., cuproptosis, entosis, ferroptosis). Gene Set Variation Analysis (GSVA) was used to evaluate pathway activities in OM, and their associations with immune infiltration, inflammation-related gene expression, and diagnostic value were systematically assessed. Weighted gene co-expression network analysis (WGCNA) was performed to identify essential modules and hub genes. A diagnostic model was constructed using machine learning with SHapley Additive exPlanations (SHAP), and candidate genes were validated in clinical peripheral blood samples using polymerase chain reaction (PCR).
Results:
Eight core PCD pathways were significantly associated with OM, mainly represented by apoptosis, autophagy, and non-classical forms such as cuproptosis and entosis. By integrating WGCNA with SHAP analysis, five hub genes (SORT1, KIF1B, TMEM106B, NPC1, and ATP6V0B) were identified as key diagnostic candidates. qPCR validation confirmed their significantly different expression between OM patients and healthy controls, supporting their utility as diagnostic biomarkers for early detection and treatment stratification.
Conclusions:
This study provides a comprehensive landscape of PCD involvement in OM, identifies novel diagnostic biomarkers, and highlights potential therapeutic targets for clinical intervention.
Insights
This study reveals programmed cell death (PCD) pathways are crucial in osteomyelitis (OM). Five genes (SORT1, KIF1B, TMEM106B, NPC1, ATP6V0B) are identified as potential biomarkers for early OM detection.
Area of Science:
- Biomedical Science
- Molecular Biology
- Immunology
Background:
- Osteomyelitis (OM) is a bone infection with poorly understood pathogenesis.
- Emerging evidence implicates programmed cell death (PCD) in OM development.
- The precise roles of various PCD types in OM require further elucidation.
Purpose of the Study:
- To comprehensively analyze the involvement of multiple programmed cell death (PCD) modalities in osteomyelitis (OM).
- To identify novel diagnostic biomarkers for early detection and treatment stratification of OM.
- To explore potential therapeutic targets based on PCD pathways in OM.
Main Methods:
- Retrieved and analyzed microarray-based transcriptome datasets for OM from the Gene Expression Omnibus (GEO) database.
- Defined thirteen PCD modalities and evaluated their pathway activities using Gene Set Variation Analysis (GSVA).
- Employed Weighted Gene Co-expression Network Analysis (WGCNA) and SHapley Additive exPlanations (SHAP) for hub gene identification and diagnostic model construction.
Main Results:
- Eight core PCD pathways, including apoptosis, autophagy, cuproptosis, and entosis, were significantly associated with OM.
- Five hub genes (SORT1, KIF1B, TMEM106B, NPC1, ATP6V0B) were identified as key diagnostic candidates.
- Quantitative PCR (qPCR) validated differential expression of these genes in OM patients, confirming their biomarker potential.
Conclusions:
- This research provides a detailed landscape of PCD involvement in osteomyelitis.
- Novel diagnostic biomarkers for OM have been identified, facilitating early detection.
- The study highlights potential therapeutic targets for clinical intervention in OM management.
