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

doi:10.3969/j.issn.1674-8115.2024.02.001

Abstract

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

CLC Number:

R684.3

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.

Figures/Tables 13

Tab 1 Sequences for siRNA

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

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

Fig 1 Batch effect removal and PCA

Fig 2 DEGs expression analysis

Fig 3 GO and KEGG enrichment analysis for NMRDEGs

Fig 4 GSEA for training dataset

Fig 5 Intersection of LASSO and SVM models

Fig 6 Diagnostic model of OA

Fig 7 Validation analysis of OA diagnostic model

Fig 8 GSEA based on high-low risk stratification

Fig 9 Training dataset immune infiltration analysis by ssGSEA algorithm

Fig 10 mRNA interactome network and drug prediction

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

TrendMD

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