Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China; Artificial Joints Engineering and Technology Research Center of Jiangxi Province, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China; Artificial Joints Engineering and Technology Research Center of Jiangxi Province, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Gene. 2024 Nov 30;928:148798. doi: 10.1016/j.gene.2024.148798. Epub 2024 Jul 25.
This study aimed to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data to identify potential biomarkers and therapeutic targets for rheumatoid arthritis (RA).
Firstly, we obtained the synovial scRNA-seq data from the Immport database and bulk RNA-seq data from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene correlation network analysis (WGCNA) to screen for module genes most relevant to RA and intersected them with the differentially expressed genes (DEGs) obtained from scRNA-seq and bulk RNA-seq to obtain intersecting genes. Next, we constructed a protein-protein interaction (PPI) network of hub genes using the STRING database and Cytoscape software and validated its expression using external validation cohorts. Finally, we performed immune cell infiltration analysis using CIBERSORT and explored the expression and drug binding activity of key gene using clinical samples and molecular docking, respectively.
We identified six cellular subgroups through dimensionality reduction and clustering, and fibroblasts may be the most important cell cluster in RA based on pseudotime and cell-cell communication analyses. Subsequently, we intersected module genes with DEGs obtained from scRNA-seq and bulk RNA-seq and constructed a PPI network of hub genes (BGN, COL11A1, COL1A1, GUCY1A1, POSTN). In external validation cohorts, POSTN was highly expressed and demonstrated the highest diagnostic performance (AUC = 0.716). In subsequent analyses, we defined POSTN as a key gene and found that its expression level was positively correlated with M2 macrophages in immune cell infiltration analysis. Additionally, POSTN was upregulated in clinical samples and exhibited favorable binding activity with nine anti-rheumatoid arthritis drugs (affinity ≤ -6.0 kcal/mol).
Through bioinformatics analysis, clinical sample validation, and molecular docking, we found that POSTN was highly expressed in RA and stably bound to common anti-rheumatoid arthritis drugs, which will provide new insights into potential biomarkers and therapeutic targets for RA.
本研究旨在整合单细胞 RNA 测序（scRNA-seq）和批量 RNA-seq 数据，以鉴定类风湿关节炎（RA）的潜在生物标志物和治疗靶点。
首先，我们从 Immport 数据库中获取滑膜 scRNA-seq 数据，从基因表达综合数据库（GEO）中获取批量 RNA-seq 数据。然后，我们使用加权基因相关网络分析（WGCNA）筛选与 RA 最相关的模块基因，并将其与从 scRNA-seq 和批量 RNA-seq 中获得的差异表达基因（DEGs）相交，以获得相交基因。接下来，我们使用 STRING 数据库和 Cytoscape 软件构建了枢纽基因的蛋白质-蛋白质相互作用（PPI）网络，并使用外部验证队列验证其表达。最后，我们使用 CIBERSORT 进行免疫细胞浸润分析，并分别使用临床样本和分子对接探索关键基因的表达和药物结合活性。
通过降维和聚类，我们确定了六个细胞亚群，基于伪时间和细胞-细胞通讯分析，成纤维细胞可能是 RA 中最重要的细胞簇。随后，我们将模块基因与从 scRNA-seq 和批量 RNA-seq 中获得的 DEGs 相交，并构建了枢纽基因（BGN、COL11A1、COL1A1、GUCY1A1、POSTN）的 PPI 网络。在外部验证队列中，POSTN 表达较高，具有最高的诊断性能（AUC=0.716）。在后续分析中，我们将 POSTN 定义为关键基因，并发现其表达水平与免疫细胞浸润分析中的 M2 巨噬细胞呈正相关。此外，POSTN 在临床样本中上调，并与九种抗类风湿关节炎药物表现出良好的结合活性（亲和力≤-6.0 kcal/mol）。
通过生物信息学分析、临床样本验证和分子对接，我们发现 POSTN 在 RA 中高表达，并与常见的抗类风湿关节炎药物稳定结合，这将为 RA 的潜在生物标志物和治疗靶点提供新的见解。
