Identifification and validation of ferroptosis signatures and immune infifiltration characteristics associated with intervertebral disc degeneration

Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration

Background

Low back pain resulting from intervertebral disc degeneration (IVDD) represents a significant global social problem. There are notable differences in the distribution of lymphatic vessels (LV) in normal and pathological intervertebral discs. Nevertheless, the molecular mechanisms of lymphatics-associated genes (LAGs) in the development of IVDD remain unclear. An in-depth exploration of this area will help to reveal the biological and clinical significance of LAGs in IVDD and may lead to the search for new therapeutic targets for IVDD.

Methods

Data sets were obtained from the Gene Expression Omnibus (GEO) database. Following quality control and normalization, the datasets (GSE153761, GSE147383, and GSE124272) were merged to form the training set, with GSE150408 serving as the validation set. LAGs from GeneCards, MSigDB, Gene Ontology, and KEGG database. The Venn diagram was employed to identify differentially expressed lymphatic-associated genes (DELAGs) that were differentially expressed in the normal and IVDD groups. Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. The receiver operating characteristic (ROC) curve, nomogram, and Decision Curve Analysis (DCA) were used to evaluate the model effect. In addition, we constructed a potential drug regulatory network and competitive endogenous RNA (ceRNA) network for key LAGs.

Results

A total of 15 differentially expressed LAGs were identified. By comparing four machine learning methods, the top five genes of importance in the XGB model (MET, HHIP, SPRY1, CSF1, TOX) were identified as lymphatics-associated gene diagnostic signatures. This signature was used to predict the diagnosis of IVDD with strong accuracy and an area under curve (AUC) value of 0.938. Furthermore, the diagnostic model was validated in an external dataset (GSE150408), with an AUC value of 0.772. The nomogram and DCA further prove that the diagnosis model has good performance and predictive value. Additionally, drug regulatory networks and ceRNA networks were constructed, revealing potential therapeutic drugs and post-transcriptional regulatory mechanisms.

Conclusion

We developed and validated a lymphatics-associated genes diagnostic model by machine learning algorithms that effectively identify IVDD patients. These five key LAGs may be potential therapeutic targets for IVDD patients.

Identification and validation of ferroptosis-related biomarkers in intervertebral disc degeneration

Introduction

Ferroptosis plays a significant role in intervertebral disc degeneration (IDD). Understanding the key genes regulating ferroptosis in IDD could reveal fundamental mechanisms of the disease, potentially leading to new diagnostic and therapeutic targets.

Methods

Public datasets (GSE23130 and GSE70362) and the FerrDb database were analyzed to identify ferroptosis-related genes (DE-FRGs) involved in IDD. Single-cell RNA sequencing data (GSE199866) was used to validate the specific roles and expression patterns of these genes. Immunohistochemistry and Western blot analyses were subsequently conducted in both clinical samples and mouse models to assess protein expression levels across different tissues.

Results

The analysis identified seven DE-FRGs, including MT1G, CA9, AKR1C1, AKR1C2, DUSP1, CIRBP, and KLHL24, with their expression patterns confirmed by single-cell RNA sequencing. Immunohistochemistry and Western blot analysis further revealed that MT1G, CA9, AKR1C1, AKR1C2, DUSP1, and KLHL24 exhibited differential expression during the progression of IDD. Additionally, the study highlighted the potential immune-modulatory functions of these genes within the IDD microenvironment.

Discussion

Our study elucidates the critical role of ferroptosis in IDD and identifies specific genes, such as MT1G and CA9, as potential targets for diagnosis and therapy. These findings offer new insights into the molecular mechanisms underlying IDD and present promising avenues for future research and clinical applications.

Experimental validation and comprehensive analysis of m6A methylation regulators in intervertebral disc degeneration subpopulation classification

Intervertebral disc degeneration (IVDD) is one of the most prevalent causes of chronic low back pain. The role of m6A methylation modification in disc degeneration (IVDD) remains unclear. We investigated immune-related m6A methylation regulators as IVDD biomarkers through comprehensive analysis and experimental validation of m6A methylation regulators in disc degeneration. The training dataset was downloaded from the GEO database and analysed for differentially expressed m6A methylation regulators and immunological features, the differentially regulators were subsequently validated by a rat IVDD model and RT-qPCR. Further screening of key m6A methylation regulators based on machine learning and LASSO regression analysis. Thereafter, a predictive model based on key m6A methylation regulators was constructed for training sets, which was validated by validation set. IVDD patients were then clustered based on the expression of key m6A regulators, and the expression of key m6A regulators and immune infiltrates between clusters was investigated to determine immune markers in IVDD. Finally, we investigated the potential role of the immune marker in IVDD through enrichment analysis, protein-to-protein network analysis, and molecular prediction. By analysising of the training set, we revealed significant differences in gene expression of five methylation regulators including RBM15, YTHDC1, YTHDF3, HNRNPA2B1 and ALKBH5, while finding characteristic immune infiltration of differentially expressed genes, the result was validated by PCR. We then screen the differential m6A regulators in the training set and identified RBM15 and YTHDC1 as key m6A regulators. We then used RBM15 and YTHDC1 to construct a predictive model for IVDD and successfully validated it in the training set. Next, we clustered IVDD patients based on the expression of RBM15 and YTHDC1 and explored the immune infiltration characteristics between clusters as well as the expression of RBM15 and YTHDC1 in the clusters. YTHDC1 was finally identified as an immune biomarker for IVDD. We finally found that YTHDC1 may influence the immune microenvironment of IVDD through ABL1 and TXK. In summary, our results suggest that YTHDC1 is a potential biomarker for the development of IVDD and may provide new insights for the precise prevention and treatment of IVDD.

ATG9A as a potential diagnostic marker of intervertebral disc degeneration: Inferences from experiments and bioinformatics analysis incorporating sc-RNA-seq data

BACKGROUND

Disfunctional autophagy plays a pivotal role in Intervertebral Disc Degeneration (IDD) progression. however, the connection between Autophagy-related gene 9A (ATG9A) and IDD has not been reported.

METHODS

Firstly, transcriptome datasets from the GEO and Autophagy-related genes (ARGs) from GeneCards were carried out using R. Following this, IDD-specific signature genes were identified through methods such as least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM) analyses. Validation of these findings proceeded through in vitro experiments, evaluation of independent datasets, and analysis of receiver operating characteristic (ROC) curves. Subsequent steps incorporated co-expression analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), and construction of competing endogenous RNA (ceRNA) network. The final section established the correlation between immune cell infiltration, ATG9A, and IDD utilizing the CIBERSORT algorithm and single-cell RNA (scRNA) sequencing data.

RESULTS

Research identified 87 differentially expressed genes, with only ATG9A noted as an IDD signature gene. Analysis of in vitro experiments and independent datasets uncovered a decrease in ATG9A expression within the degeneration group. The area under the curve (AUC) of ATG9A exceeded 0.8 following ROC analysis. Furthermore, immune cell infiltration and scRNA sequencing data analysis elucidated the substantial role of immune cells in IDD progression. A ceRNA network was constructed, centered around ATG9A, included 4 miRNAs and 22 lncRNAs.

CONCLUSION

ATG9A was identified as a diagnostic gene for IDD, indicating its viability as a effective target for therapy disease.