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Identification of HTRA1, DPT and MXRA5 As Potential Biomarkers Associated with Osteoarthritis Progression and Immune Infiltration

Overview
Publisher Biomed Central
Specialties Orthopedics
Physiology
Date 2024 Aug 15
PMID 39148085
Authors
Affiliations
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Abstract

Background: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.

Methods: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.

Results: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.

Conclusions: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

References
1.
Wang J, Zhuo Z, Wang Y, Yang S, Chen J, Wang Y . Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia. Front Cell Dev Biol. 2022; 9:800267. PMC: 8814441. DOI: 10.3389/fcell.2021.800267. View

2.
Wen B, Liu M, Qin X, Mao Z, Chen X . Identifying immune cell infiltration and diagnostic biomarkers in heart failure and osteoarthritis by bioinformatics analysis. Medicine (Baltimore). 2023; 102(26):e34166. PMC: 10313258. DOI: 10.1097/MD.0000000000034166. View

3.
Yu G, Wang L, Han Y, He Q . clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16(5):284-7. PMC: 3339379. DOI: 10.1089/omi.2011.0118. View

4.
Tracy L, Minasian R, Caterson E . Extracellular Matrix and Dermal Fibroblast Function in the Healing Wound. Adv Wound Care (New Rochelle). 2016; 5(3):119-136. PMC: 4779293. DOI: 10.1089/wound.2014.0561. View

5.
Oka C, Saleh R, Bessho Y, Reza H . Interplay between HTRA1 and classical signalling pathways in organogenesis and diseases. Saudi J Biol Sci. 2022; 29(4):1919-1927. PMC: 9072889. DOI: 10.1016/j.sjbs.2021.11.056. View