Bioinformatics Analysis of Biomarkers and Therapeutic Targets Related to Necroptosis in Intervertebral Disc Degeneration

Abstract

Necroptosis is a critical process in intervertebral disc degeneration (IDD). This research is aimed at identifying key genes regulating necroptosis in IDD to provide a theoretical basis for early diagnosis and treatment. Transcriptome data from patients with IDD and normal samples were obtained from the GSE34095 and GSE124272 datasets of the Gene Expression Omnibus (GEO) public database. Necroptosis-related genes (NRGs) were sourced from the GeneCards database and literature. Differentially expressed necroptosis-related genes (DE-NRGs) in IDD were identified by intersecting these sources. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene annotation analysis. The receiver operating characteristic (ROC) curve and nomogram analyses assessed the diagnostic efficiency of DE-NRGs. The miRWalk and starBase databases helped construct the competing endogenous RNA (ceRNA) regulatory network of DE-NRGs. We identified 517 differential genes in tissue and 2974 in blood, with 62 genes in common. DE-NRGs (AIFM1, CCT8, HNRNPA1, KHDRBS1, SERBP1) were identified by intersecting NRGs with these 62 common genes. The ROC curve showed an area under the curve (AUC) > 0.70 for DE-NRGs, and the nomogram indicated that a higher DE-NRG score correlates with a higher risk of IDD. CCT8, KHDRBS1, and AIFM1 emerged as potential therapeutic targets for IDD through target drug prediction. qRT-PCR (quantitative reverse transcription polymerase chain reaction), Western blot, and immunohistochemistry confirmed the expression of AIFM1, CCT8, HNRNPA1, KHDRBS1, and SERBP1 in patients' nucleus pulposus tissue, suggesting these genes as key targets for IDD risk assessment and drug therapy.

Keywords: ceRNA network; diagnostic markers; immune infiltration; intervertebral disc degeneration; necroptosis; therapeutic target.

Figure 1

The screen and function analysis of common DEGs. (a) The volcano map of DEGs in GSE34095. (b) The heat map of GSE34095 DEGs. (c) The volcano map of GSE124272 DEGs. (d) The heat map of GSE124272 DEGs. (e) The Venn analysis of GSE34095 and GSE124272 DEGs intersecting. (f) The GO enrichment results of common DEGs. (g) The GO functional connection of common DEGs. (h) The KEGG enrichment term of common DEGs. (i) The PPI interactivity network of common DEGs. Logistic regression (LR), random forest (RF), support vector machine (SVM).

Figure 2

The screen function analysis and evaluation of DE-NRGs. (a) The Venn map. (b) The coexpression network of DE-NRGs with others NRGs. (c) The GO enrichment bubble map of DE-NRGs and coexpression genes NRGs. (d) KEGG enrichment bubble map of DE-NRGs and coexpression NRGs. (e) The diagnosis ROC curve of DE-NRGs. (f) The ROC curve of multiple diagnostic models. (g) Nomogram of DE-NRGs. (h) The DCA curve to evaluate the clinical efficiency of the nomogram model. (i) The clinical influence curve of nomogram.

Figure 3

ceRNA regulated network where construct and treatment drugs were predicted of DEGs. (a) Volcano map of DE-miRNA in GSE116726. (b) Heat map of DE-miRNA in GSE116726. (c) Volcano map of DE-miRNA in GSE124272. (d) Heat map of DE-miRNA in GSE124272. (e) ceRNA regulated network of DE-NRGs. (f) A volcano map of DE-IFs. (g) A regulated network of DE-NRGs-IF.

Figure 4

The key gene–treatment drug network in database CLUE COMMAND.

Figure 5

Patients with severe illness have higher levels of necrotic apoptosis. (a) qRT-PCR detection of key gene expression. (b) Western blot detection of MLKL activity (randomly select three sets of samples). (c) IHC detection of necrotic apoptosis levels in nucleus pulposus tissue (randomly select three sets of samples).