Identification of Core Genes and Screening of Potential Targets in Intervertebral Disc Degeneration Using Integrated Bioinformatics Analysis

A diagnostic signatures for intervertebral disc degeneration using TNFAIP6 and COL6A2 based on single-cell RNA-seq and bulk RNA-seq analyses

OBJECTIVES

Intervertebral disc degeneration (IVDD) is a prevalent degenerative condition associated with a high incidence rate of low back pain and disability. This study aimed to identify potential biomarkers and signaling pathways associated with IVDD.

METHODS

Biomarkers were discerned through bulk-RNA and single-cell RNA sequencing (scRNA-Seq) investigations of IVDD cases from the Gene Expression Omnibus (GEO) database. Following this, two central genes were identified. Furthermore, gene set enrichment analysis (GSEA) and receiver operating characteristic (ROC) curve analysis were conducted. The transcriptional factor (TF) derived from nucleus pulposus (NP) cells was examined through the DoRothEA R package. RT-qPCR and IHC techniques were employed to confirm the expression of the two hub genes and their associated genes in tissue samples.

RESULTS

The proteins Tumor necrosis factor-inducible gene 6 protein (TNFAIP6) and collagen VI-α2 (COL6A2) were frequently analyzed using a combination of DEGs from datasets GSE70362, GSE124272, and scRNA-seq. Examination of gene expression across multiple datasets indicated significant differences in TNFAIP6 and COL6A2 levels in IVDD compared to control or normal groups (p < 0.05). These two central genes demonstrated strong diagnostic utility in the training cohort and reliable predictive value in the validation datasets. Our study verified the potential role of ZEB2 as a TF in regulating two key genes associated with IVDD. Furthermore, qPCR and IHC confirmed elevated expression levels of the hub genes and transcription factor.

CONCLUSION

We identified biomarkers, specifically TNFAIP6 and COL6A2, that have the potential to predict disease activity and aid in the diagnosis of IVDD.

The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches

Background

Some studies indicated that histone modification may be involved in depression disorder (DD). The maintenance of the histone acetylation state is the work of histone acetyltransferase (HAT) and histone deacetylase (HDAC), which is thought to be a potential diagnostic biomarker of depression. However, it is still unknown how histone acetylation-related genes (HAC-RGs) contribute to the onset and progression of DD.

Methods

GSE76826 and GSE98793were obtained from the Gene Expression Omnibus (GEO) database, HAC-RGs were acquired from the GeneCards database. Initially, the differentially expressed genes (DEGs) in GSE76826 were investigated. We used weighted gene co-expression network analysis (WGCNA) to screen key module genes. Candidate genes were selected by intersecting DEGs, key module genes, and HAC-RGs, followed by functional analysis. Two machine learning algorithms were used to identify hub genes, which were used for drug prediction, immunological infiltration studies, nomogram construction, and regulatory network building. The expression levels were verified using the GSE76826 and GSE98793 datasets. Hub gene expression levels in the clinical samples were verified using reverse transcription quantitative PCR (RT-qPCR).

Results

The 23 candidate genes were obtained by intersecting 2,316 DEGs, 1,010 HAC-RGs and 2,617 key module genes. Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. Additionally, the three hub genes were enriched in the kegg_ribosome. The 9 different immune cells were identified in GSE76826, which were associated with three hub genes. A hub gene-drug network (98 nodes, 106 edges) and an lncRNA-miRNA-mRNA network (56 nodes, 87 edges), were built using the database. The expression level verification indicated that, with the exception of the KPNB1 gene, the DD group had higher levels of JDP2 and ALOX5 and that the expression patterns in GSE76826 and GSE98793 were consistent, with RT-qPCR confirming higher ALOX5 and JDP2 expression in DD samples.

Conclusion

This study identified three hub genes (JDP2, ALOX5, and KPNB1) associated with histone acetylation, offering new insight into the diagnosis and treatment of DD.

Exploring the molecular mechanisms underlying intervertebral disc degeneration by analysing multiple datasets

The purpose of this study was to explore the genetic characteristics and immune cell infiltration related to intervertebral disc degeneration through multidataset analysis, predict potential therapeutic drugs, and provide a theoretical basis for clinical treatment. The gene expression profile data of the GSE70362, GSE186542, and GSE245147 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the hub genes were identified through differentially expressed gene analysis, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) functional annotation and Mendelian randomization analysis were performed. Hub genes and immune cells were identified. Infiltration status was determined through GSEA and GSVA to clarify the specific signalling pathways associated with key genes and explore the potential molecular mechanisms by which key genes affect disease progression. The key genes were reversely predicted using miRNA grid construction and transcription factor regulation, and genes related to disease regulation were obtained from the GeneCards database. Finally, the differentially expressed genes were used for drug prediction through the Connectivity Map database to identify potential drugs for the treatment of intervertebral disc degeneration. The feasibility of the predicted drugs was tested by molecular docking technology. Real-time quantitative PCR was used to confirm the expression of key genes in the tissue samples.A total of 126 differentially expressed genes were identified in the GEO database, and 4 differentially expressed hub genes (COL6A2, DCXR, GLRX, and PDGFRB) were identified through bioinformatics methods. Immune infiltration analysis revealed that NK cells, macrophages, and eosinophils were activated during IVDD, whereas mast cells and T cells were suppressed. GO and KEGG analyses revealed that key genes are involved in the development of this disease through signalling pathways such as the glycolysis pathway, the oxidative phosphorylation pathway, the cholesterol regulatory pathway, and the haem metabolism pathway. Analysis of the constructed miRNA grid revealed that key genes are jointly regulated by multiple transcription factors, among which the most important motif is cisbp_M5578. Disease regulation-related genes were obtained through the GeneCards database, analysis of the correlation with key genes was performed, and the expression levels of the two mRNA and miRNA were significantly correlated. Finally, drug prediction performed through the Connectivity Map database revealed that drugs such as Abt-751, LY-2183240, podophyllotoxin, and vindesine can alleviate or even reverse the disease state. Finally, we collected 10 IVDD and 10 healthy disc tissue samples, and the RT‒qPCR results were consistent with the bioinformatics results. We identified COL6A2, DCXR, GLRX, and PDGFRB as key genes involved in IVDD. In addition, drugs such as Abt-751 are expected to control and reverse the progression of the disease. In the future, these key genes and predicted drugs may provide new directions for further mechanistic studies as well as new therapies for IVDD patients.

Integrated bulk and single-cell RNA sequencing to identify potential biomarkers in intervertebral disc degeneration

BACKGROUND

Nucleus pulposus (NP) deterioration plays a significant role in the development of intervertebral disc degeneration (IVDD) and low back pain (LBP). This paper aims to identify potential genes within degenerated NP tissue and elucidate the pathogenesis of IVDD through bioinformatics analysis.

METHODS

We conducted a transcriptomic analysis of patient's degenerative NP tissue employing advanced bioinformatics techniques and machine learning algorithms. Utilizing hdWGCNA, we successfully acquired WGCNA single-cell sequencing data and pinpointed crucial genes implicated in IVDD. Subsequently, we employed the Monocle3 package to perform pseudotime sequence analysis, enabling the identification of genes associated with the differentiation and developmental processes of NP tissue. Following this, normalized and logarithmically transformed the bulk sequencing data. Subsequently, we conducted preliminary screening using single-factor logistic regression on the genes derived from single-cell sequencing. Next, we applied two machine learning techniques, namely, SVM-RFE and random forest, to discern pivotal pathogenic genes. Finally, we used validation sets to verify trends and qualitativeness and performed in vitro and in vivo validation analyses of normal and degenerative NP tissues.

RESULTS

909 genes associated with IVDD were identified through hdWGCNA, while pseudotime sequence analysis uncovered 1964 genes related to differentiation and developmental processes. The two had 208 genes in common. Subsequently, we conducted an initial screening of single-cell genes by integrating the bulk database with single logistic regression. Next, we utilized machine learning techniques to identify the IVDD genes CDH, DPH5, and SELENOF. PCR analysis confirmed that the expression of CDH and DPH5 in degraded nucleus pulposus cells (NPCs) was decreased by 31% and 28% in vivo, and 36% and 29% in vitro, respectively, while SELENOF showed the opposite trend. Furthermore, IVDD was validated through imaging and histological staining.

CONCLUSION

As pathogenic genes in IVDD, our findings indicate that CTH, DPH5, and SELENOF are important players and might be promising therapeutic targets for IVDD treatment.

Assessment of XCI skewing and demonstration of XCI escape region based on single-cell RNA sequencing: comparison between female Grave's disease and control

BACKGROUND

The reactivation and loss of mosaicism hypothesis due to X chromosome inactivation (XCI) skewing and escape could influence gender differences in autoimmune diseases. XCI selectively inactivates one of the two X chromosomes in females.

METHODS

To estimate XCI skewing and the occurrence of XCI escape, we conducted a normal female (NF) without a history of autoimmune thyroid disease (AITD) and a patient with Grave's disease (GD) based on a thyroid diagnosis. After single-cell RNA sequencing, heterozygous variants were converted and transformed. XCI skewing was calculated using the formula and the skewing degree was defined. NF/GD genes were compared using correction methods. Positions are heterozygous within a single cell as indicated by a unique barcode.

RESULTS

XCI skewing showed 45.8%/48.9% relatively random, 29.4%/27.0% skewing, 24.6%/23.7% severe skewing, and 0.2%/0.4% extreme severe skewing. 24.8%/24.1% in NF/GD exhibited severe skewing or higher. A total of 13 genes were significantly associated with XCI skewing ratios in NF/GD cells. In total, 371/250 nucleotide positions with only one barcode (representing a unique cell) were identified for XCI escape. A total of 143/52 nucleotide positions spanned 20/6 genes, and 12/1 genes were identified as XCI escapes.

CONCLUSIONS

These results could aid in understanding the immunogenetics of gender differences in various autoimmune disease pathophysiologies.

Deciphering SPP1-related macrophage signaling in the pathogenesis of intervertebral disc degeneration

This study delved into the molecular mechanisms underlying mechanical stress-induced intervertebral disc degeneration (msi-IDD) through single-cell and high-throughput transcriptome sequencing in mouse models and patient samples. Results exhibited an upsurge in macrophage presence in msi-IDD intervertebral disc (IVD) tissues, with secreted phosphoprotein 1 (SPP1) identified as a pivotal driver exacerbating degeneration via the protein kinase RNA-like endoplasmic reticulum kinase/ activating transcription factor 4/ interleukin-10 (PERK/ATF4/IL-10) signaling axis. Inhibition of SPP1 demonstrated promising outcomes in mitigating msi-IDD progression in both in vitro and in vivo models. These findings underscore the therapeutic promise associated with the modulation of the PERK signaling pathway in IDD, shedding light on the pathogenesis of msi-IDD and proposing a promising avenue for intervention strategies.

Experimental confirmation and bioinformatics reveal biomarkers of immune system infiltration and hypertrophy ligamentum flavum

Background

Hypertrophy ligamentum flavum is a prevalent chronic spinal condition that affects middle-aged and older adults. However, the molecular pathways behind this disease are not well comprehended.

Objective

The objective of this work is to implement bioinformatics techniques in order to identify crucial biological markers and immune infiltration that are linked to hypertrophy ligamentum flavum. Further, the study aims to experimentally confirm the molecular mechanisms that underlie the hypertrophy ligamentum flavum.

Methods

The corresponding gene expression profiles (GSE113212) were selected from a comprehensive gene expression database. The gene dataset for hypertrophy ligamentum flavum was acquired from GeneCards. A network of interactions between proteins was created, and an analysis of functional enrichment was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. An study of hub genes was performed to evaluate the infiltration of immune cells in patient samples compared to tissues from the control group. Finally, samples of the ligamentum flavum were taken with the purpose of validating the expression of important genes in a clinical setting.

Results

Overall, 27 hub genes that were differently expressed were found through molecular biology. The hub genes were found to be enriched in immune response, chemokine-mediated signaling pathways, inflammation, ossification, and fibrosis processes, as demonstrated by GO and KEGG studies. The main signaling pathways involved include the TNF signaling pathway, cytokine-cytokine receptor interaction, and TGF-β signaling pathway. An examination of immunocell infiltration showed notable disparities in B cells (naïve and memory) and activated T cells (CD4 memory) between patients with hypertrophic ligamentum flavum and the control group of healthy individuals. The in vitro validation revealed markedly elevated levels of ossification and fibrosis-related components in the hypertrophy ligamentum flavum group, as compared to the normal group.

Conclusion

The TGF-β signaling pathway, TNF signaling pathway, and related hub genes play crucial roles in the progression of ligamentum flavum hypertrophic. Our study may guide future research on fibrosis of the ligamentum flavum.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

Human C15orf39 Inhibits Inflammatory Response via PRMT2 in Human Microglial HMC3 Cell Line

Microglia-mediated inflammatory response is one key cause of many central nervous system diseases, like Alzheimer's disease. We hypothesized that a novel C15orf39 (MAPK1 substrate) plays a critical role in the microglial inflammatory response. To confirm this hypothesis, we used lipopolysaccharide (LPS)-and interferon-gamma (IFN-γ)-induced human microglia HMC3 cells as a representative indicator of the microglial in vitro inflammatory response. We found that C15orf39 was down-regulated when interleukin-6 (IL-6) and tumor necrosis factor-α (TNFα) expression increased in LPS/IFN-γ-stimulated HMC3 cells. Once C15orf39 was overexpressed, IL-6 and TNFα expression were reduced in LPS/IFN-γ-stimulated HMC3 cells. In contrast, C15orf39 knockdown promoted IL-6 and TNFα expression in LPS/IFN-γ-stimulated HMC3 cells. These results suggest that C15orf39 is a suppressive factor in the microglial inflammatory response. Mechanistically, C15orf39 interacts with the cytoplasmic protein arginine methyltransferase 2 (PRMT2). Thus, we termed C15orf39 a PRMT2 interaction protein (PRMT2 IP). Furthermore, the interaction of C15orf39 and PRMT2 suppressed the activation of NF-κB signaling via the PRMT2-IκBα signaling axis, which then led to a reduction in transcription of the inflammatory factors IL6 and TNF-α. Under inflammatory conditions, NF-κBp65 was found to be activated and to suppress C15orf39 promoter activation, after which it canceled the suppressive effect of the C15orf39-PRMT2-IκBα signaling axis on IL-6 and TNFα transcriptional expression. In conclusion, our findings demonstrate that in a steady condition, the interaction of C15orf39 and PRMT2 stabilizes IκBα to inhibit IL-6 and TNFα expression by suppressing NF-κB signaling, which reversely suppresses C15orf39 transcription to enhance IL-6 and TNFα expression in the microglial inflammatory condition. Our study provides a clue as to the role of C15orf39 in microglia-mediated inflammation, suggesting the potential therapeutic efficacy of C15orf39 in some central nervous system diseases.

Bioinformatics-based discovery of intervertebral disc degeneration biomarkers and immune-inflammatory infiltrates

Background

Intervertebral disc degeneration (IVDD) is a common chronic disease in orthopedics, and its molecular mechanisms are still not well explained.

Aim

This study's objective was to bioinformatics-based discovery of IVDD biomarkers and immune-inflammatory infiltrates.

Materials and Methods

The IVDD illness gene collection was gathered from GeneCards, DisGeNet, and gene expression profiles were chosen from the extensive Gene Expression Omnibus database (GSE124272, GSE150408, and GSE153761). The STRING database was used to create a network of protein-protein interactions, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases were used for functional enrichment analysis. Using hub genes, the immune cell infiltration between IVDD patient samples and control tissues was examined. Finally, quantitative polymerase chain reaction and Western blot experiments were used to verify the expression of hub genes.

Results

A total of 27 differentially expressed hub genes were identified by bioinformatics. According to GO and KEGG analyses, hub genes were prominent in immunological responses, chemokine-mediated signaling pathways, and inflammatory responses, with the key signaling pathways engaged in cellular senescence, apoptosis, Th1 and Th2 cell differentiation, and Th17 cell differentiation. Immune cell infiltration research revealed that T cells, lymphocytes, B cells, and NK cells were decreased in IVDD patients while monocytes, neutrophils, and CD8 T cells were increased. The expression levels of the senescence hub genes SP1, VEGFA, IL-6, and the apoptosis key gene CASP3 were considerably greater in the IVDD model group than in the control group, according to in vitro validation.

Conclusion

In conclusion, the cellular senescence signaling pathway, the apoptosis signaling pathway, and associated hub genes play significant roles in the development and progression of IVDD, this finding may help direct future research on the senescence signaling route in IVDD.

Conjoint research of WGCNA, single-cell transcriptome and structural biology reveals the potential targets of IDD development and treatment and JAK3 involvement

OBJECTIVES

This study conducted integrated analysis of bulk RNA sequencing, single-cell RNA sequencing and Weighted Gene Co-expression Network Analysis (WGCNA), to comprehensively decode the most essential genes of intervertebral disc degeneration (IDD); then mainly focused on the JAK3 macromolecule to identify natural compounds to provide more candidate drug options in alleviating IDD.

METHODS

In the first part, we performed single-cell transcriptome analysis and WGCNA workflow to delineate the most pivotal genes of IDD. Then series of structural biology approaches and high-throughput virtual screening techniques were performed to discover potential compounds targeting JAK-STAT signaling pathway, such as Libdock, ADMET, precise molecular docking algorithm and in-vivo drug stability assessment.

RESULTS

Totally 4 hub genes were determined in the development of IDD, namely VEGFA, MMP3, TNFSF11, and TIMP3, respectively. Then, 3 novel natural materials, ZINC000014952116, ZINC000003938642 and ZINC000072131515, were determined as potential compounds, with less toxicities and moderate ADME characteristics. In-vivo drug stability assessment suggested that these drugs could interact with JAK3, and their ligand-JAK3 complexes maintained the homeostasis in-vivo, which acted as regulatory role to JAK3 protein. Among them, ZINC000072131515, also known as Menaquinone, demonstrated significant protective roles to alleviate the progression of IDD in vitro, which proved the nutritional therapy in alleviating IDD.

CONCLUSIONS

This study reported the essential genes in the development of IDD, and also the roles of Menaquinone to ameliorate IDD through inhibiting JAK3 protein. This study also provided more options and resources on JAK3 targeted screening, which may further expand the drug resources in the pharmaceutical market.

Mesenchymal Stem Cells May Alleviate the Intervertebral Disc Degeneration by Reducing the Oxidative Stress in Nucleus Pulposus Cells

Background

Stem cell therapy is a promising therapeutic modality for intervertebral disc degeneration (IDD). Oxidative stress is a vital contributor to the IDD; however, the definite role of oxidative stress in stem cell therapy for IDD remains obscure. The aim of this study was to determine the vital role of oxidative stress-related differentially expressed genes (OSRDEGs) in degenerative NPCs cocultured with mesenchymal stem cells (MSCs).

Methods

A series of bioinformatic methods were used to calculate the oxidative stress score and autophagy score, identify the OSRDEGs, conduct the function enrichment analysis and protein-protein interaction (PPI) analysis, build the relevant competing endogenous RNA (ceRNA) regulatory networks, and explore the potential association between oxidative stress and autophagy in degenerative NPCs cocultured with MSCs.

Results

There was a significantly different oxidative stress score between NPC/MSC samples and NPC samples (p < 0.05). Forty-one OSRDEGs were selected for the function enrichment and PPI analyses. Ten hub OSRDEGs were obtained according to the PPI score, including JUN, CAT, PTGS2, TLR4, FOS, APOE, EDN1, TXNRD1, LRRK2, and KLF2. The ceRNA regulatory network, which contained 17 DElncRNAs, 240 miRNAs, and 10 hub OSRDEGs, was constructed. Moreover, a significant relationship between the oxidative stress score and autophagy score was observed (p < 0.05), and 125 significantly related gene pairs were obtained (|r| > 0.90, p < 0.05).

Conclusion

Stem cell therapy might repair the degenerative IVD via reducing the oxidative stress through the ceRNA regulatory work and restoration of autophagy in degenerative NPCs. This research could provide new insights into the mechanism research of stem cell therapy for IDD and potential therapeutic targets in the IDD treatment.

Significance of Immune-Related Genes in the Diagnosis and Classification of Intervertebral Disc Degeneration

Background

With the extensive development of intervertebral disc degeneration (IDD) research, IDD has been found to be a complex disease associated with immune-related gene (IRGs) changes. Nonetheless, the roles of IRGs in IDD are unclear.

Methods

In our study, 11 IRGs were chosen using differential analysis between nondisc degeneration and degenerative patients from the GEO database. Then, we utilized a random forest (RF) model to screen six candidate IRGs to predict the risk of IDD. A nomogram was developed on the basis of six candidate IRGs, and DCA showed that patients could benefit from the nomogram. Based on the selected significant IRGs, a consensus clustering approach was used to differentiate disc degeneration patients into two immune patterns (immune cluster A and B). The PCA algorithm was constructed to compute immune scores for every sample, to quantify immune patterns. The immune scores of immune cluster B patients were higher than those of immune cluster A.

Results

Through differential expression analysis between healthy and IDD samples, 11 significant IRGs (CTSS, S100Z, STAT3, KLRK1, FPR1, C5AR2, RLN1, IFGR2, IL2RB, IL17RA, and IL6R) were recognized through significant IRGs. The “Reverse Cumulative Distribution of Residual” and “Boxplots of Residual” indicate that the RF model has minimal residuals. The majority of samples in the model have relatively small residuals, demonstrating that the model is better. Besides, the nomogram model was constructed based on importance and the IRGs with importance scores greater than 2 (FPR1, RLN1, S100Z, IFNGR2, KLRK1, and CTSS). The nomogram model revealed that decision-making based on an established model might be beneficial for IDD patients, and the predictive power of the nomogram model was significant. In addition, we identified two different immune cluster patterns (immune cluster A and immune cluster B) based on the 11 IRGs. We found that immune cluster A had significantly higher levels of MDSC, neutrophil, plasmacytoid dendritic cell, and type 17 T helper cell expression than immune cluster B. And we calculated the score for each sample to quantify the gene patterns. The patients in immune cluster B or gene cluster B had higher immune scores than those in immune cluster A or gene cluster A.

Conclusion

In conclusion, IRGs play an extremely significant role in the occurrence of IDD. Our study of immune patterns may guide the strategies of prevention and treatment for IDD in the future.