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Abstract：
Osteoarthritis (OA) progression involves multiple factors, including cartilage erosion as the basic pathological mechanism of degeneration, and is closely related to chondrocyte apoptosis. To analyze the correlation between apoptosis and OA development, we selected apoptosis genes from the differentially expressed genes (DEGs) between OA and normal samples from the Gene Expression Omnibus (GEO) database, used lasso regression analysis to identify characteristic genes, and performed consensus cluster analysis to further explore the pathogenesis of this disease.
The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed.
A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively.
ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.
The Gene expression profile datasets of OA samples, GSE12021 and GSE55235, were downloaded from GEO. The datasets were combined and analyzed for DEGs. Apoptosis-related genes (ARGs) were collected from the GeneCards database and intersected with DEGs for apoptosis-related DEGs (ARDEGs). Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to obtain characteristic genes, and a nomogram was constructed based on these genes. A consensus cluster analysis was performed to divide the patients into clusters. The immune characteristics, functional enrichment, and immune infiltration statuses of the clusters were compared. In addition, a protein-protein interaction network of mRNA drugs, mRNA-transcription factors (TFs), and mRNA-miRNAs was constructed.
A total of 95 DEGs were identified, of which 47 were upregulated and 48 were downregulated, and 31 hub genes were selected as ARDEGs. LASSO regression analysis revealed nine characteristic genes: growth differentiation factor 15 (GDF15), NAMPT, TLR7, CXCL2, KLF2, REV3L, KLF9, THBD, and MTHFD2. Clusters A and B were identified, and neutrophil activation and neutrophil activation involved in the immune response were highly enriched in Cluster B, whereas protein repair and purine salvage signal pathways were enriched in Cluster A. The number of activated natural killer cells in Cluster B was significantly higher than that in Cluster A. GDF15 and KLF9 interacted with 193 and 32 TFs, respectively, and CXCL2 and REV3L interacted with 48 and 82 miRNAs, respectively.
ARGs could predict the occurrence of OA and may be related to different degrees of OA progression.
摘要：
■骨关节炎（OA）的进展涉及多种因素，包括软骨侵蚀作为退化的基本病理机制，与软骨细胞凋亡密切相关。分析细胞凋亡与OA发生发展的相关性,我们从基因表达综合（GEO）数据库中的OA和正常样本之间的差异表达基因（DEG）中选择了凋亡基因，使用Lasso回归分析来识别特征基因，进行共识聚类分析,进一步探讨本病的发病机制。
■OA样本的基因表达谱数据集，GSE12021和GSE55235是从GEO下载的。将数据集合并并分析DEG。从GeneCards数据库中收集凋亡相关基因（ARG），并与DEGs相交以获得凋亡相关的DEGs（ARDEG）。进行最小绝对收缩和选择算子(LASSO)回归分析以获得特征基因，根据这些基因构建了列线图。进行共识聚类分析以将患者分成簇。免疫特性，功能富集，并比较各组的免疫浸润状态。此外,mRNA药物的蛋白质-蛋白质相互作用网络，mRNA转录因子（TFs），并构建了mRNA-miRNA。
■总共确认了95个DEG，其中47个上调，48个下调，并选择31个hub基因作为ARDEGs。LASSO回归分析显示9个特征基因：生长分化因子15(GDF15)、NAMPT,TLR7、CXCL2、KLF2、REV3L、KLF9,THBD,和MTHFD2。确定了集群A和B，中性粒细胞活化和参与免疫反应的中性粒细胞活化在B组中高度富集，而蛋白修复和嘌呤补救信号通路在簇A中富集，激活的自然杀伤细胞在簇B中的数量明显高于簇A中的数量。GDF15和KLF9与193和32TF相互作用，分别,CXCL2和REV3L与48和82个miRNA相互作用，分别。
■ARGs可以预测OA的发生，可能与OA进展的不同程度有关。
■OA样本的基因表达谱数据集，GSE12021和GSE55235是从GEO下载的。将数据集合并并分析DEG。从GeneCards数据库中收集凋亡相关基因（ARG），并与DEGs相交以获得凋亡相关的DEGs（ARDEG）。进行最小绝对收缩和选择算子(LASSO)回归分析以获得特征基因，根据这些基因构建了列线图。进行共识聚类分析以将患者分成簇。免疫特性，功能富集，并比较各组的免疫浸润状态。此外,mRNA药物的蛋白质-蛋白质相互作用网络，mRNA转录因子（TFs），并构建了mRNA-miRNA。
■总共确认了95个DEG，其中47个上调，48个下调，并选择31个hub基因作为ARDEGs。LASSO回归分析显示9个特征基因：生长分化因子15(GDF15)、NAMPT,TLR7、CXCL2、KLF2、REV3L、KLF9,THBD,和MTHFD2。确定了集群A和B，中性粒细胞活化和参与免疫反应的中性粒细胞活化在B组中高度富集，而蛋白修复和嘌呤补救信号通路在簇A中富集，激活的自然杀伤细胞在簇B中的数量明显高于簇A中的数量。GDF15和KLF9与193和32TF相互作用，分别,CXCL2和REV3L与48和82个miRNA相互作用，分别。
■ARGs可以预测OA的发生，可能与OA进展的不同程度有关。
