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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.
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.
摘要：
背景：我们的研究旨在确定骨关节炎（OA）的潜在特异性生物标志物，并评估其与免疫浸润的关系。
方法：我们使用来自GSE117999、GSE51588和GSE57218的数据作为训练集，当GSE114007用作验证集时，全部从GEO数据库获得。首先,进行加权基因共表达网络分析(WGCNA)和功能富集分析,以确定基因的枢纽模块和潜在功能。我们随后使用机器学习方法在集线器模块的差异表达基因(DEG)内筛选潜在的OA生物标志物。验证了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,我们使用RT-PCR来验证我们的结果。
结果：WGCNA结果表明，蓝色模块与OA最相关，并且在功能上与细胞外基质（ECM）相关术语相关。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,MXRA5在训练和验证队列中一致表现出OA中表达增加，显示出强大的诊断潜力。ssGSEA结果显示DCs的异常浸润,NK细胞,Tfh,Th2和Treg细胞可能有助于OA进展。HTRA1,DPT,MXRA5与免疫细胞浸润显著相关。RT-PCR结果也证实了这些发现。
结论：HTRA1、DPT、MXRA5是有前途的OA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。
方法：我们使用来自GSE117999、GSE51588和GSE57218的数据作为训练集，当GSE114007用作验证集时，全部从GEO数据库获得。首先,进行加权基因共表达网络分析(WGCNA)和功能富集分析,以确定基因的枢纽模块和潜在功能。我们随后使用机器学习方法在集线器模块的差异表达基因(DEG)内筛选潜在的OA生物标志物。验证了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,我们使用RT-PCR来验证我们的结果。
结果：WGCNA结果表明，蓝色模块与OA最相关，并且在功能上与细胞外基质（ECM）相关术语相关。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,MXRA5在训练和验证队列中一致表现出OA中表达增加，显示出强大的诊断潜力。ssGSEA结果显示DCs的异常浸润,NK细胞,Tfh,Th2和Treg细胞可能有助于OA进展。HTRA1,DPT,MXRA5与免疫细胞浸润显著相关。RT-PCR结果也证实了这些发现。
结论：HTRA1、DPT、MXRA5是有前途的OA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。
