烟酰胺代谢相关基因与骨关节炎的关系探索

doi:10.3969/j.issn.1674-8115.2024.02.001

上海交通大学学报（医学版） ›› 2024, Vol. 44 ›› Issue (2): 145-160.doi: 10.3969/j.issn.1674-8115.2024.02.001

• 论著 · 基础研究 •

烟酰胺代谢相关基因与骨关节炎的关系探索

邓青松¹^,²^,³(), 张长青¹^,²^,³, 陶诗聪¹^,²()

^1.上海交通大学医学院附属第六人民医院骨科，上海 200233
^2.上海交通大学医学院附属第六人民医院临床医学院，上海 200233
^3.上海交通大学医学院附属第六人民医院上海市四肢显微外科研究所，上海 200233

收稿日期:2023-08-07 接受日期:2023-11-30 出版日期:2024-02-28 发布日期:2024-03-25
通讯作者: 陶诗聪 E-mail:dqs1229@163.com;sctao@shsmu.edu.cn
作者简介:邓青松（1998—），男，硕士生；电子信箱：dqs1229@163.com。
基金资助:
国家自然科学基金(81802226);上海市浦江人才计划(2019PJD038);上海市“医苑新星”青年医学人才培养资助计划;上海交通大学医学院“双百人”项目(20220017);上海市第六人民医院优秀人才培育项目(ynyq202101)

Exploration of the relationship between nicotinamide metabolism-related genes and osteoarthritis

DENG Qingsong¹^,²^,³(), ZHANG Changqing¹^,²^,³, TAO Shicong¹^,²()

^1.Department of Orthopedic Surgery, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
^2.Clinical Medical College of Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
^3.Institute of Microsurgery on Extremities, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China

Received:2023-08-07 Accepted:2023-11-30 Online:2024-02-28 Published:2024-03-25
Contact: TAO Shicong E-mail:dqs1229@163.com;sctao@shsmu.edu.cn
Supported by:
National Natural Science Foundation of China(81802226);Shanghai Pujiang Program(2019PJD038);Shanghai “Rising Stars of Medical Talent” Youth Development Program (Youth Medical Talents-Specialist Program);“Two-hundred Talents” Program of Shanghai Jiao Tong University School of Medicine(20220017);Shanghai Sixth People's Hospital Excellent Young Scientist Development Program(ynyq202101)

摘要/Abstract

摘要：

目的·利用生物信息学方法探索骨关节炎与烟酰胺代谢相关基因之间的关系，找到具有诊断价值和治疗潜力的关键基因。方法·以“Osteoarthritis”为检索词，在GEO数据库中获取GSE12021、GSE55235和GSE55457数据集，将GSE55457作为验证集。去除GSE12021和GSE55235数据集的批次效应后，得到标准化的合并数据集，将其作为训练集，并在训练集中筛选出差异表达基因（differentially expressed genes，DEGs）。在GeneCards数据库和MSigDB数据库中获取所有烟酰胺代谢相关基因（nicotinamide metabolism-related genes，NMRGs）。将DEGs与NMRGs取交集，得到烟酰胺代谢相关差异表达基因（nicotinamide metabolism-related differentially expressed genes，NMRDEGs）。对训练集进行基因集富集分析（gene set enrichment analysis，GSEA），对NMRDEGs进行基因本体（Gene Ontology，GO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）分析。通过LASSO（least absolute shrinkage and selection operator）和支持向量机（support vector machine，SVM）分析筛选出NMRDEGs关键基因，构建骨关节炎诊断模型，并用验证集GSE55457进行验证。通过单样本基因集富集分析（single sample gene set enrichment analysis，ssGSEA）分析免疫细胞的浸润类型。通过DGIdb数据库、ENCORI数据库和CHIPBase数据库对关键基因的mRNA进行相互作用网络和药物小分子预测。通过干扰小RNA（small interfering RNA，siRNA）敲降软骨细胞内NMRDEGs关键基因，用实时荧光定量聚合酶链反应（real-time fluorogenic quantitative polymerase chain reaction，RT-qPCR）检测关键基因敲降对软骨形成相关基因表达的影响。结果·发现了NAMPT、TIPARP等7个NMRDEGs。GO和KEGG分析富集到核因子κB信号通路和正向调节白细胞介素-1介导的信号通路等。GSEA富集到缺氧诱导因子-1转录因子通路（Hif1 Tfpathway）和多配体蛋白聚糖1（syndecan 1）通路等信号通路。LASSO分析和SVM分析共同筛选得到NPAS2、TIPARP和NAMPT关键基因并构建了骨关节炎诊断模型，验证集检验提示诊断模型诊断效果具有高准确度。ssGSEA免疫浸润分析的结果显示，巨噬细胞等15种免疫细胞存在显著差异（均P<0.05）。找到了7个针对关键基因的潜在药物小分子，19种与关键基因相互作用且上游基因与下游基因数量之和大于10的miRNA，19种与关键基因结合且上游基因与下游基因数量之和大于7的转录因子，27个聚类数>19的RNA结合蛋白。RT-qPCR结果显示，关键基因敲降会降低软骨形成相关基因的表达。结论·NPAS2、TIPARP和NAMPT为烟酰胺代谢相关的关键基因，可据此构建骨关节炎诊断模型。

关键词: 骨关节炎, 烟酰胺代谢, 生物信息学, 诊断模型, 列线图

Abstract:

Objective ·To explore the relationship between osteoarthritis and nicotinamide metabolism-related genes using bioinformatics analysis, and identify key genes with diagnostic value and therapeutic potential. Methods ·By using "Osteoarthritis" as a search term, GSE12021, GSE55235, and GSE55457 were obtained from the GEO database, with GSE55457 being used as the validation set. After removing batch effects from the GSE12021 and GSE55235 datasets, the standardized combined dataset was obtained and used as the training dataset. Differentially expressed genes (DEGs) were identified from the training dataset. All nicotinamide metabolism-related genes (NMRGs) were obtained from the GeneCards and MSigDB databases. The intersection of DEGs and NMRGs was taken to obtain nicotinamide metabolism-related differentially expressed genes (NMRDEGs). Gene set enrichment analysis (GSEA) was performed on the training dataset, while gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on NMRDEGs. Key genes were selected by using least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) analysis in NMRDEGs to build an osteoarthritis diagnosis model which was validated by using the GSE55457 dataset. Single sample gene set enrichment analysis (ssGSEA) was used to analyze the immune cell infiltration type. Interactions networks and drug molecule predictions were obtained for these key genes' mRNA with the DGIdb, ENCORI, and CHIPBase databases. siRNA was used to knock down the key genes in chondrocytes, and then real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of chondrogenesis-related genes. Results ·Seven NMRDEGs, including NAMPT, TIPARP, were discovered. GO and KEGG analysis enriched some signaling pathways, such as nuclear factor-κB signaling pathway and positive regulation of interleukin-1-mediated signaling pathway. GSEA enriched pathways such as Hif1 Tfpathway and syndecan 1 pathway. Key genes NPAS2, TIPARP, and NAMPT were identified through LASSO and SVM analysis, and used to construct an osteoarthritis diagnostic model. The validated results showed that the diagnostic model had high accuracy. Immune infiltration analysis results obtained by ssGSEA showed significant differences (all P<0.05) in 15 types of immune cells, including macrophages. Seven potential small molecules targeting key genes were identified, along with 19 miRNAs with the sum of upstream and downstream >10, 19 transcription factors with upstream and downstream >7, and 27 RNA binding proteins with clusterNum >19. The results of RT-qPCR showed that knocking down key genes reduced the expression of chondrogenesis-related genes. Conclusion ·Through bioinformatics analysis, key genes related to nicotinamide metabolism, NPAS2, TIPARP, and NAMPT, are discovered, and an osteoarthritis diagnostic model is constructed.

Key words: osteoarthritis, nicotinamide metabolism, bioinformatics, diagnostic model, nomogram

中图分类号:

R684.3

邓青松, 张长青, 陶诗聪. 烟酰胺代谢相关基因与骨关节炎的关系探索[J]. 上海交通大学学报（医学版）, 2024, 44(2): 145-160.

DENG Qingsong, ZHANG Changqing, TAO Shicong. Exploration of the relationship between nicotinamide metabolism-related genes and osteoarthritis[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(2): 145-160.

图/表 13

表1 siRNA序列

Tab 1 Sequences for siRNA

siRNA	Sense (5'→3')	Antisense (5'→3')
siNPAS2	CAAAGGAAUUUCCAACUUAUGTT	CAUAAGUUGGAAAUUCCUUUGTT
siNAMPT	GCAGGACUUGCUCUAAUUAAATT	UUUAAUUAGAGCAAGUCCUGCTT
siTIPARP	CCAAGAGAACGGAAUUGAAAUTT	AUUUCAAUUCCGUUCUCUUGGTT

表2 RT-qPCR引物序列

Tab 2 Primer sequences for RT-qPCR

Primer	Forward primer (5'→3')	Reverse primer (5'→3')
β-actin	CCTCTATGCCAACACAGT	AGCCACCAATCCACACAG
ACAN	TGGAGACAAGGATGAGTTTCC	GGCGAAGCAGTACACATCATA
SOX9	AACACCTTGAGCCTTAAAACG	GATTTCATCTCCTTTGCTTGC

图1 去除批次效应和主成分分析Note： A. PCA plot of the merged datasets before batch effect removal. B. PCA plot of the merged dataset after batch effect removal.

Fig 1 Batch effect removal and PCA

图2 DEGs分析Note： A. Volcano plot of DEGs analysis in the OA group and the control group in the training dataset. B. Venn diagram of DEGs and NMRGs in the training dataset. C. Group comparison chart of NMRDEGs in the training dataset. D. Group comparison chart of NMRDEGs in the validation set GSE55457. ①P<0.05, ②P<0.01, ③P=0.000, comparison between the control group and the OA group.

Fig 2 DEGs expression analysis

图3 NMRDEGs的GO和KEGG富集分析Note： A. The results of GO enrichment analysis combined with difference analysis results of NMRDEGs logFC network diagram display. B. The pathway KEGG enrichment analysis results of NMRDEGs combined with the difference analysis results of logFC network diagram display.

Fig 3 GO and KEGG enrichment analysis for NMRDEGs

图4 训练集的GSEA分析Note： A. Glucocorticoid receptor pathway. B. Hif1 Tfpathway. C. Syndecan 1 pathway. NES—normalized enrichment score; FDR—false discovery rate.

Fig 4 GSEA for training dataset

图5 LASSO模型与SVM模型的交集Note： A. LASSO regressor trajectories for NMRDEGs. B. LASSO regression diagnostic model diagram. C. The number of genes with the highest accuracy rate obtained by the SVM algorithm. D. The number of genes with the lowest error rate obtained by the SVM algorithm. E. Venn diagram of the intersection of SVM and LASSO models. F. Boxplot of functional similarity analysis of key genes.

Fig 5 Intersection of LASSO and SVM models

图6 OA的诊断模型构建Note： A. The key genes are included in the forest plot of the single factor Logistic regression model. B. The key genes are included in the forest plot of the multi-factor Logistic regression model. C. The nomogram of the key genes and Logistic predictive scoring model in the training dataset. D. The nomogram of the key genes and Logistic predictive value scoring model in the validation set GSE55457.

Fig 6 Diagnostic model of OA

图7 OA诊断模型的验证分析Note： A. Calibration plot of the Logistic model in the training dataset. B. Calibration plot of the Logistic model in the validation set GSE55457. C. DCA plot of the Logistic model in the training dataset. D. DCA plot of the Logistic model in the validation set GSE55457. E. The ROC curve of the Logistic model in the training dataset. F. The ROC curve of the Logistic model in the validation set GSE55457. FPR—false positive rate.

Fig 7 Validation analysis of OA diagnostic model

图8 基于高低风险分层的GSEANote： A. Respiratory electron transport ATP synthesis by chemiosmotic coupling and heat production by uncoupling proteins. B. Interferon α/β signaling. C. Glycolysis and gluconeogenesis.

Fig 8 GSEA based on high-low risk stratification

图9 训练集的ssGSEA算法免疫浸润分析Note： Group comparison chart showing the differences in immune infiltration between the OA group and the control group in the training dataset. ①P<0.05, ②P<0.01, ③P=0.000.

Fig 9 Training dataset immune infiltration analysis by ssGSEA algorithm

图10 mRNA相互作用网络与药物预测Note： A. mRNA-drug interaction network between key genes and small molecules. B. mRNA-miRNA interaction network of key genes and miRNA. C. mRNA-TF interaction network of key genes and transcription factors. D. The mRNA-RBP interaction network of key genes and RBP.

Fig 10 mRNA interactome network and drug prediction

图11 关键基因敲降后软骨形成相关基因 ACAN 和 SOX9 的mRNA表达水平Note： ①P=0.000, ②P<0.01, compared with the control group.

Fig 11 The relative mRNA expression of ACAN and SOX9 after knocking down key genes

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Exploration of the relationship between nicotinamide metabolism-related genes and osteoarthritis

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