Comprehensive Network Analysis Identified SIRT7, NTRK2, and CHI3L1 as New Potential Markers for Intervertebral Disc Degeneration. (12th February 2022)

• Record Type: Journal Article
• Authors: Li, Haoxi; Li, Wenhao; Zhang, Li; He, Jicheng; Tang, Lin; Li, Zhuhai; Chen, Feng; Fan, Qie; Wei, Jianxun
• Other Names: Wang Fu Academic Editor.
• Is Part Of: Journal of oncology. Volume 2022(2022)
• Publication Date: 2022-02-12
• Subjects: Oncology -- Research -- Periodicals; Tumors -- Periodicals; Neoplasms; Oncology -- Research; Tumors; Periodicals; Periodicals; 616.994
• DOI: 10.1155/2022/4407541
• Languages: English
• ISSNs: 1687-8450

Abstract

Abstract : Intervertebral disc degeneration (IDD) is considered the basis of serious clinical symptoms, especially for low back pain (LBP). Therefore, it is essential to explore the regulatory role and diagnostic performance of dysregulated genes and potential drugs in IDD. Through WGCNA co-expression analysis, 36 co-expression modules were obtained. Among them, MidnightBlue and Red modules were the most related to IDD. Functional enrichment analysis showed that the Red module was mainly related to neutrophil activation and regulation of cytokine-mediated signaling pathway and apoptosis, whereas the MidnightBlue module was mainly related to extracellular matrix organization, bone development, extracellular matrix, extracellular matrix component, and other extracellular matrices. Furthermore, 356 genes highly related to the module were screened to construct a protein interaction network. Network degree distribution analysis showed that the known IDD-related genes had a higher degree of distribution. Enrichment analysis demonstrated that these genes were enriched in MAPK_SIGNALING_PATHWAY (FDR = 0.012), CHEMOKINE_SIGNALING_PATHWAY, and some other pathways. By constructing a disease-gene interaction network, three disease-specific genes were finally identified. Through combining with the drug-target gene interaction network, two potential therapeutic drugs, entrectinib and larotrectinib, were determined. Finally, based on these genes, the diagnostic model in the training dataset, test dataset, and verification dataset all showed a high diagnostic performance. The findings of this study contributed to the diagnosis of IDD and personalized treatment of IDD.