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Intervertebral disc degeneration (IDD) is a major cause of lower back pain. However, to date, the molecular mechanism of the IDD remains unclear. Gene expression profiles and clinical traits were downloaded from the Gene Expression Omnibus (GEO) database. Firstly, weighted gene coexpression network analysis (WGCNA) was used to screen IDD-related genes. Moreover, least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine (SVM) algorithms were used to identify characteristic genes. Furthermore, we further investigated the immune landscape by the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and the correlations between key characteristic genes and infiltrating immune cells. Finally, a competing endogenous RNA (ceRNA) network was established to show the regulatory mechanisms of characteristic genes. A total of 2458 genes were identified by WGCNA, and 48 of them were disordered. After overlapping the genes obtained by LASSO and SVM-RFE algorithms, genes including LINC01347, ASAP1-IT1, lnc-SEPT7L-1, B3GNT8, CHRNB3, CLEC4F, LOC102724000, SERINC2, and LOC102723649 were identified as characteristic genes of IDD. Moreover, differential analysis further identified ASAP1-IT1 and SERINC2 as key characteristic genes. Furthermore, we found that the expression of both ASAP1-IT1 and SERINC2 was related to the proportions of T cells gamma delta and Neutrophils. Finally, a ceRNA network was established to show the regulatory mechanisms of ASAP1-IT1 and SERINC2. In conclusion, the present study identified ASAP1-IT1 and SERINC2 as the key characteristic genes of IDD through integrative bioinformatic analyses, which may contribute to the diagnosis and treatment of IDD.

computational and mathematical methods in medicine

Identification of Characteristic Genes in Whole Blood of Intervertebral Disc Degeneration Patients by Weighted Gene Coexpression Network Analysis (WGCNA)