Hebei North University, Zhangjiakou, Hebei, 075000, China.
Department of orthopaedic surgery, Huabeiyiliao Jiankangjituan Fengfeng Zongyiyuan, Handan, Hebei, 056000, China.
BMC Musculoskelet Disord. 2024 Aug 15;25(1):647. doi: 10.1186/s12891-024-07758-7.
Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.
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.
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.
HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.
本研究旨在鉴定骨关节炎（OA）的潜在特异性生物标志物，并评估其与免疫浸润的关系。
我们利用 GEO 数据库中的 GSE117999、GSE51588 和 GSE57218 数据作为训练集，GSE114007 作为验证集。首先，进行加权基因共表达网络分析（WGCNA）和功能富集分析，以鉴定基因的枢纽模块和潜在功能。然后，我们使用机器学习方法在枢纽模块的差异表达基因（DEGs）中筛选潜在的 OA 生物标志物。验证候选基因的诊断准确性。此外，进行单基因分析和 ssGSEA。然后，我们探讨了生物标志物与免疫细胞之间的关系。最后，我们采用 RT-PCR 验证我们的结果。
WGCNA 结果表明，蓝色模块与 OA 相关性最强，与细胞外基质（ECM）相关术语的功能相关。我们的分析确定了 ALB、HTRA1、DPT、MXRA5、CILP、MPO 和 PLAT 作为潜在的生物标志物。值得注意的是，HTRA1、DPT 和 MXRA5 在训练和验证队列中均表现出 OA 的表达增加，具有稳健的诊断潜力。ssGSEA 结果表明，DCs、NK 细胞、Tfh、Th2 和 Treg 细胞的异常浸润可能导致 OA 进展。HTRA1、DPT 和 MXRA5 与免疫细胞浸润有显著相关性。RT-PCR 结果也证实了这些发现。
HTRA1、DPT 和 MXRA5 是 OA 的有前途的生物标志物。它们的过表达与 OA 进展和免疫细胞浸润密切相关。
