Microarray analysis reveals an inflammatory transcriptomic signature in peripheral blood for sciatica

Bioinformatics Analysis and Identification of Ferroptosis-Related Hub Genes in Intervertebral Disc Degeneration

Approximately 80% of individuals encounter lower back pain (LBP), a prevalent clinical issue largely attributed to intervertebral disc degeneration (IDD). Ferroptosis is an iron-dependent lipid peroxidation-driven cell death, and there is growing evidence that ferroptosis plays an important role in various human diseases. However, the underlying mechanism of ferroptosis in IDD remains unclear. This study aims to reveal the potential hub genes and related pathways of ferroptosis in the pathogenesis and progression of IDD. In this study, we analyzed three microarray datasets from the GEO database. Additionally, we downloaded ferroptosis-related genes from FerrDb-V2 and extracted apoptosis-related genes from UniProt as a control to show the specificity of ferroptosis. Weighted gene co-expression network analysis (WGCNA) was performed to identify the IDD-related module genes. Then, ferroptosis-related genes and apoptosis-related genes were separately overlapped with the IDD-related module genes, resulting in the identification of 35 ferroptosis-related module genes (FRMG) and 142 apoptosis-related module genes (ARMG). Furthermore, we performed functional enrichment analysis and protein-protein interaction network, and Cytoscape along with CytoHubba was used to identify the hub genes. Finally, logistic regression models were constructed and identified two hub FRMGs (PTEN and EGFR) and one hub ARMG (CTNNB1), which could distinguish IDD patients from controls (P < 0.05). The areas under the ROC curves were 0.792 and 0.730, respectively, suggesting that ferroptosis is more specific than apoptosis in IDD. In conclusion, this study provided fresh perspectives on ferroptosis in the pathogenesis and progression of IDD that can be used to evaluate potential biomarker genes and therapeutic targets.

Identification of Biomarkers, Pathways, Immune Properties of Mitophagy Genes, and Prediction Models for Intervertebral Disc Degeneration

Background

Intervertebral disc degeneration (IDD) is the leading cause of low back pain (LBP). The mechanism of IDD development and progression is not fully understood. Peripheral biomarkers are increasingly vital non-radioactive methods in early detection and diagnosis for IDD. Nevertheless, less attention has been paid to the role of mitophagy genes in the progress of IDD. This study aimed to identify the mitophagy disease-causing genes in the process of IDD and mitophagy diagnostic biomarkers for IDD.

Methods

Mitophagy-related differentially expressed genes (MRDEGs) related to IDD were investigated by analyzing the microarray datasets of IDD cases from GEO, PathCards and Molecular Signatures Databases. We used R software, WGCNA, PPI, mRNA-miRNA, mRNA-TF, GO, KEGG, GSEA, GSVA and Cytoscape to analyze and visualize the data. We further used ssGSEA for immunoinfiltration analysis to obtain different immune cell infiltration. LASSO model was developed to screen for genes that met the diagnostic gene model requirements. Finally, qRT-PCR, Western blotting and HE were used to verify hub genes and their expression from clinical IDD samples.

Results

We identified 14 MRDEGs and 12 hub genes. GO, KEGG, GSEA and GSVA analyses demonstrated that hub genes were critical for the development of IDD. LASSO diagnostic model consisted of six hub genes, among which SQSTM1, ATG7 and OPTN were significantly different between the two IDD disease subtypes. At the same time, SQSTM1 also had a high correlation with immune characteristic subtypes. The results of qRT-PCR and Western blotting also indicated that these genes were significantly differentially expressed in nucleus pulposus cells (NPCs) of the IDD group.

Conclusion

We explored an association between MRDEGs-associated signature in IDD and validated that hub genes like SQSTM1 might serve as biomarkers for diagnostic and therapeutic targets for IDD. Meanwhile, this study can provide new insights into the functional characteristics and mechanism of mitophagy in the development of IDD.

Significance of Oxidative Stress in the Diagnosis and Subtype Classification of Intervertebral Disc Degeneration

Intervertebral disc degeneration (IVDD) is a common illness of aging, and its pathophysiological process is mainly manifested by cell aging and apoptosis, an imbalance in the production and catabolism of extracellular matrix, and an inflammatory response. Oxidative stress (OS) is an imbalance that decreases the body's intrinsic antioxidant defense system and/or raises the formation of reactive oxygen species and performs multiple biological functions in the body. However, our current knowledge of the effect of OS on the progression and treatment of IVDD is still extremely limited. In this study, we obtained 35 DEGs by differential expression analysis of 437 OS-related genes (OSRGs) between IVDD patients and healthy individuals from GSE124272 and GSE150408. Then, we identified six hub OSRGs (ATP7A, MELK, NCF1, NOX1, RHOB, and SP1) from 35 DEGs, and the high accuracy of these hub genes was confirmed by constructing ROC curves. In addition, to forecast the risk of IVDD patients, we developed a nomogram. We obtained two OSRG clusters (clusters A and B) by consensus clustering based on the six hub genes. Then, 3147 DEGs were obtained by differential expression analysis in the two clusters, and all samples were further divided into two gene clusters (A and B). We investigated differences in immune cell infiltration levels between different clusters and found that most immune cells had higher infiltration levels in OSRG cluster B or gene cluster B. In conclusion, OS is important in the formation and progression of IVDD, and we believe that our work will help guide future research on OS in IVDD.

Unravelling diagnostic clusters and immune landscapes of cuproptosis patterns in intervertebral disc degeneration through dry and wet experiments

Cuproptosis is a manner of mitochondrial cell death induced by copper. However, cuproptosis modulators' molecular processes in intervertebral disc degeneration (IDD) are still unclear. To better understand the processes of cuproptosis regulators in IDD, a thorough analysis of cuproptosis regulators in the diagnostic biomarkers and subtype determination of IDD was conducted. Then we collected clinical IDD samples and successfully established IDD model in vivo and in vitro, and carried out real-time quantitative polymerase chain reaction (RT-qPCR) validation of significant cuproptosis modulators. Totally we identified 8 crucial cuproptosis regulators in the present research. Using a random forest model, we isolated 8 diagnostic cuproptosis modulators for the prediction of IDD risk. Then, based on our following decision curve analysis, we selected the five diagnostic cuproptosis regulators with importance scores greater than two and built a nomogram model. Using a consensus clustering method, we divided IDD patients into two cuproptosis clusters (clusterA and clusterB) based on the important cuproptosis regulators. Additionally, each sample's cuproptosis value was evaluated using principal component analysis in order to quantify the cuproptosis clusters. Patients in clusterB had higher cuproptosis scores than patients in clusterA. Moreover, we found that clusterB was involved in the immunity of natural killer cell, while clusterA was related to activated CD4 T cell, activated B cell, etc. Notably, cuproptosis modulators detected by RT-qPCR showed generally consistent expression levels with the bioinformatics results. To sum up, cuproptosis modulators play a crucial role in the pathogenic process of IDD, providing biomarkers and immunotherapeutic approaches for IDD.

Donor white blood cell differential is the single largest determinant of whole blood gene expression patterns

It has become widely accepted that sample cellular composition is a significant determinant of the gene expression patterns observed in any transcriptomic experiment performed with bulk tissue. Despite this, many investigations currently performed with whole blood do not experimentally account for possible inter-specimen differences in cellularity, and often assume that any observed gene expression differences are a result of true differences in nuclear transcription. In order to determine how confounding of an assumption this may be, in this study, we recruited a large cohort of human donors (n = 138) and used a combination of next generation sequencing and flow cytometry to quantify and compare the underlying contributions of variance in leukocyte counts versus variance in other biological factors to overall variance in whole blood transcript levels. Our results suggest that the combination of donor neutrophil and lymphocyte counts alone are the primary determinants of whole blood transcript levels for up to 75% of the protein-coding genes expressed in peripheral circulation, whereas the other factors such as age, sex, race, ethnicity, and common disease states have comparatively minimal influence. Broadly, this infers that a majority of gene expression differences observed in experiments performed with whole blood are driven by latent differences in leukocyte counts, and that cell count heterogeneity must be accounted for to meaningfully biologically interpret the results.

Ferrostatin-1 Inhibits Toll-Like Receptor 4/NF-κB Signaling to Alleviate Intervertebral Disc Degeneration in Rats

Ferrostatin-1 (Fer-1) is an inhibitor of ferroptosis and has been documented to be implicated in the development of intervertebral disc degeneration (IDD). This study intends to explore the role of Fer-1 in IDD via the toll-like receptor 4 (TLR4)/NF-κB signaling pathway. Through the Gene Expression Omnibus database, IDD-related gene expression microarray GSE124272 and high-throughput sequencing data set GSE175710 were obtained. Then, differentially expressed genes in IDD were identified, followed by implementation of protein-protein interaction network analysis and receiver operating characteristic curve analysis. Main pathways in IDD were obtained through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional analyses, and target genes of Fer-1 were obtained through PubChem and PharmMapper websites. Finally, GPX4, FTH, and TLR4 expression was determined in a constructed IDD rat model. Three key co-expression modules involved in IDD were obtained through Weighted Gene Co-Expression Network Analysis. Thirteen differentially expressed genes were found to be associated with IDD, and eight key genes (TLR4, BCL2A1, CXCL1, IL1R1, NAMPT, SOCS3, XCL1, and IRAK3) were found to affect IDD. These eight key genes had the diagnostic potential for IDD. The NF-κB signaling pathway played a predominant role in IDD development. From network pharmacologic analysis, Fer-1 might suppress ferroptosis and ameliorate IDD via the TLR4/NF-κB signaling pathway, and the in vivo animal experiment further verified it. Fer-1 down-regulates TLR4 to inactivate NF-κB signaling pathway, suppressing ferroptosis and finally alleviating IDD in rats.

Identification of pyroptosis‑related genes in neuropathic pain based on bioinformatics analysis

Pyroptosis is defined as inflammation-induced programmed cell death. However, gene expression levels related to pyroptosis and their role in neuropathic pain (NP) remain unclear. The present study aimed to develop and validate an NP-predictive signature based on the genes associated with pyroptosis. Gene expression level profiles were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the pyroptotic genes most highly associated with NP. NP-related pyroptosis gene signature was constructed using multivariate logistic regression. A rat model of neuropathic pain was established through chronic constriction injury to analyse the inflammatory infiltration and myelin damage around the sciatic nerve, and examine the expression levels of macrophage markers S100 calcium-binding protein β (S100β) and ionized calcium-binding adapter molecule 1 (Iba-1). Finally, flow cytometry analysis was used to examine the lipopolysaccharide (LPS)-induced cell death ratio of RSC96 cells (Schwann cells), while the expression levels of LPS-induced pyroptosis-related genes in RSC96 cells were measured via reverse transcription-quantitative PCR. The results demonstrated that pyroptosis-related genes (gasdermin D, NLR family pyrin domain containing 3, neuronal apoptosis inhibitory protein and NLR family CARD domain containing 4) were identified to increase the risk of NP. NP-related pyroptosis signatures were constructed based on these four genes. Moreover, the high-risk group had a higher level of macrophage infiltration compared with the low-risk group, as determined by the CIBERSORT algorithm. H&E staining results showed that the myelin structure of the sciatic nerve tissue of chronic constriction injury (CCI) rats was destroyed and inflammatory cells infiltrated around neurons. The results of immunohistochemistry showed that compared with in the sham group, the expression levels of Iba-1 and sS100β in the sciatic nerve of the CCI group were increased. Furthermore, the expression levels of cell death and pyroptosis-related genes in Schwann cells induced by LPS were increased compared with in the control group. In conclusion, an NP-related pyroptosis gene signature was constructed based on four pyroptosis-related genes and it was found that the expression of pyroptosis-related genes was upregulated in the early steps of the neuroinflammatory process in RSC96 cells.

Ferulic acid alleviates sciatica by inhibiting peripheral sensitization through the RhoA/p38MAPK signalling pathway

BACKGROUND

Nonsteroidal anti-inflammatory drugs are used to relieve sciatica, but their effects are not satisfactory.

PURPOSE

This study aimed to explore the therapeutic effects of ferulic acid on sciatica.

METHODS

Thirty-two SD rats were randomly divided into 4 groups, i.e., sham operation group, chronic constriction injury (CCI) group, mecobalamin group, and ferulic acid group. We conducted behavioural tests, ELISA, PCR, Western blots, and immunofluorescence analysis. Specific inhibitors were used in cell experiments to explore the related mechanisms.

RESULTS

Thermal hyperalgesia was induced after CCI operation, and ferulic acid relieved thermal hyperalgesia. In addition, ferulic acid decreased the IL1β, IL6, TNF-α, and CRP mRNA levels; the IBA-1, iNOS, IL1β, RhoA, RhoA-GTP, COX2, Rock1, TRPV1, TRPA1, and p-p38MAPK levels in dorsal root ganglion (DRG) neurons; and the LPS, CRP, substance P (SP), and prostaglandin E2 (PGE2) levels in serum, and these levels were higher in the CCI group. In the cell experiments, LPS induced M1 polarization of GMI-R1 cells via the RhoA/Rock pathway. Ferulic acid attenuated LPS-induced M1 polarization by decreasing the levels of M1 polarization markers, including IL1β, IL6, TNF-α, iNOS, and CD32, and increased M2 polarization by increasing the levels of M2 polarization markers, including CD206 and Arg-1. LPS treatment clearly increased the iNOS, IL1β, RhoA, Rock1, Rock2 and p-p38 MAPK levels and reduced Arg-1 expression, and ferulic acid reversed these changes.

CONCLUSION

Ferulic acid can inhibit peripheral sensitization by reducing the levels of inflammatory factors, TRPA1 and TRPV1 through the RhoA/p38 MAPK pathway to alleviate sciatica.

Integrative Bioinformatics Analysis Revealed Mitochondrial Dysfunction-Related Genes Underlying Intervertebral Disc Degeneration

Objective

Mitochondrial dysfunction plays an important role in intervertebral disc degeneration (IDD). We aim to explore the pathways and key genes that cause mitochondrial dysfunction during IDD and to further reveal the pathogenesis of IDD based on bioinformatic analyses.

Methods

Datasets GSE70362 and GSE124272 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) of mitochondrial dysfunction between IDD patients and healthy controls were screened by package limma package. Critical genes were identified by adopting gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. We collected both degenerated and normal disc tissues obtained surgically, and we performed western blot and qPCR to verify the key DEGs identified in intervertebral disc tissues.

Results

In total, 40 cases of IDD and 24 healthy controls were included. We identified 152 DEGs, including 67 upregulated genes and 85 downregulated genes. Four genes related to mitochondrial dysfunction (SOX9, FLVCR1, NR5A1 and UCHL1) were screened out. Of them, SOX9, FLVCR1, and UCHL1 were down-regulated in peripheral blood and intervertebral disc tissues of IDD patients, while NR5A1 was up-regulated. The analysis of immune infiltration showed the concentrations of mast cells activated were significantly the highest in IDD patients. Compared with the control group, the level of T cells CD4 memory resting was the lowest in the patients. In addition, 24 cases of IDD tissues and 12 cases of normal disc tissues were obtained to verify the results of bioinformatics analysis. Both western blot and qPCR results were consistent with the results of bioinformatics analysis.

Conclusion

We identified four genes (SOX9, FLVCR1, NR5A1 and UCHL1) associated with mitochondrial dysfunction that play an important role in the progress of disc degeneration. The identification of these differential genes may provide new insights for the diagnosis and treatment of IDD.

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.

Key LncRNAs Associated With Oxidative Stress Were Identified by GEO Database Data and Whole Blood Analysis of Intervertebral Disc Degeneration Patients

Background: Intervertebral disc degeneration (IDD) is a major cause of low back pain, but the onset and progression of IDD are unknown. Long non-coding RNA (lncRNA) has been validated to play a critical role in IDD, while an increasing number of studies have linked oxidative stress (OS) to the initiation and progression of IDD. We aim to investigate key lncRNAs in IDD through a comprehensive network of competing endogenous RNA (ceRNA) and to identify possible underlying mechanisms.

Methods: We downloaded IDD-related gene expression data from the Gene Expression Omnibus (GEO) database and obtained differentially expressed-lncRNAs (DE-lncRNA), -microRNAs (DE-miRNA), and -messenger RNAs (DE-mRNA) by bioinformatics analysis. The OS-related lncRNA-miRNA-mRNA ceRNA interaction axis was constructed and key lncRNAs were identified based on ceRNA theory. We performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on mRNAs regulated by lncRNAs in the ceRNA network. Single sample gene set enrichment analysis (ssGSEA) was used to reveal the immune landscape. Expression of key lncRNAs in IDD was assessed by quantitative reverse transcription-polymerase chain reaction (qRT-PCR).

Results: In this study, 111 DE-mRNAs, 20 DE-lncRNAs, and 502 DE-miRNAs were identified between IDD patients and controls, and 16 OS-related DE-lncRNAs were also identified. The resulting lncRNA-miRNA-mRNA network consisted of eight OS-related DE-lncRNA nodes, 24 DE-miRNA nodes, 70 DE-mRNA nodes, and 183 edges. Functional enrichment analysis suggested that the ceRNA network may be involved in regulating biological processes related to cytokine secretion, lipid, and angiogenesis. We also identified four key lncRNAs, namely lncRNA GNAS-AS1, lncRNA MIR100HG, lncRNA LINC01359, and lncRNA LUCAT1, which were also found to be significantly associated with immune cells.

Conclusion: These results provide novel insights into the potential applications of OS-related lncRNAs in patients with IDD.

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

Background: Intervertebral disc degeneration (IDD), characterized by diverse pathological changes, causes low back pain (LBP). However, prophylactic and delaying treatments for IDD are limited. The aim of our study was to investigate the gene network and biomarkers of IDD and suggest potential therapeutic targets.

Methods: Differentially expressed genes (DEGs) associated with IDD were identified by analyzing the mRNA, miRNA, and lncRNA expression profiles of IDD cases from the Gene Expression Omnibus (GEO). The protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis as well as miRNA–lncRNA–mRNA networks were conducted. Moreover, we obtained 71 hub genes and performed a comprehensive analysis including GO, KEGG, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), Disease Ontology (DO), methylation analysis, receiver operating characteristic (ROC) curve analysis, immune infiltration analysis, and potential drug identification. We finally used qRT-PCR to verify 13 significant DEGs in normal and degenerative nucleus pulposus cells (NPCs).

Results: We identified 305 DEGs closely related to IDD. The GO and KEGG analyses indicated that changes in IDD are significantly associated with enrichment of the inflammatory and immune response. GSEA analysis suggested that cell activation involved in the inflammatory immune response amide biosynthetic process was the key for the development of IDD. The GSVA suggested that DNA repair, oxidative phosphorylation, peroxisome, IL-6-JAK-STAT3 signaling, and apoptosis were crucial in the development of IDD. Among the 71 hub genes, the methylation levels of 11 genes were increased in IDD. A total of twenty genes showed a high functional similarity and diagnostic value in IDD. The result of the immune cell infiltration analysis indicated that seven genes were closely related to active natural killer cells. The most relevant targeted hub genes for potential drug or molecular compounds were MET and PIK3CD. Also, qRT-PCR results showed that ARHGAP27, C15orf39, DEPDC1, DHRSX, MGAM, SLC11A1, SMC4, and LINC00887 were significantly downregulated in degenerative NPCs; H19, LINC00685, mir-185-5p, and mir-4306 were upregulated in degenerative NPCs; and the expression level of mir-663a did not change significantly in normal and degenerative NPCs.

Conclusion: Our findings may provide new insights into the functional characteristics and mechanism of IDD and aid the development of IDD therapeutics.

Computational Analysis of the Immune Infiltration Pattern and Candidate Diagnostic Biomarkers in Lumbar Disc Herniation

Objectives

Lumbar disc herniation (LDH) is a musculoskeletal disease that contributes to low back pain, sciatica, and movement disorder. Existing studies have suggested that the immune environment factors are the primary contributions to LDH. However, its etiology remains unknown. We sought to identify the potential diagnostic biomarkers and analyze the immune infiltration pattern in LDH.

Methods

The whole-blood gene expression level profiles of GSE124272 and GSE150408 were downloaded from the Gene Expression Omnibus (GEO) database, including that of 25 patients with LDH and 25 healthy volunteers. After merging the two microarray datasets, Differentially Expressed Genes (DEGs) were screened, and a functional correlation analysis was performed. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithm and support vector machine recursive feature elimination (SVM-RFE) were applied to identify diagnostic biomarkers by a cross-validation method. Then, the GSE42611 dataset was used as a validation dataset to detect the expression level of these diagnostic biomarkers in the nucleus pulposus and evaluate their accuracy. The hub genes in the network were identified by the CIBERSORT tool and the Weighted Gene Coexpression Network Analysis (WGCNA). A Spearman correlation analysis between diagnostic markers and infiltrating immune cells was conducted to further illustrate the molecular immune mechanism of LDH.

Results

The azurophil granule and the systemic lupus erythematosus pathway were significantly different between the healthy group and the LDH group after gene enrichment analysis. The XLOC_l2_012836, lnc-FGD3-1, and scavenger receptor class A member 5 were correlated with the immune cell infiltration in various degrees. In addition, five hub genes that correlated with LDH were identified, including AQP9, SIRPB2, SLC16A3, LILRB3, and HSPA6.

Conclusion

The XLOC_l2_012836, lnc-FGD3-1, and SCARA5 might be adopted for the early diagnosis of LDH. The five identified hub genes might have similar pathological mechanisms that contribute to the degeneration of the lumbar disc. The identified hub genes and immune infiltrating pattern extend the knowledge on the potential functioning mechanisms, which offer guidance for the development of therapeutic targets of LDH.

Eucommia ulmoides Oliver-Tribulus terrestris L. Drug Pair Regulates Ferroptosis by Mediating the Neurovascular-Related Ligand-Receptor Interaction Pathway- A Potential Drug Pair for Treatment Hypertension and Prevention Ischemic Stroke

Background

In this study, we used the network pharmacology approach to explore the potential disease targets of the Eucommia ulmoides Oliver (EUO)-Tribulus terrestris L. (TT) drug pair in the treatment of hypertension-associated neurovascular lesions and IS via the ferroptosis pathway.

Methods

We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform to search for the key active compounds and targets of the drug pair. Based on the GeneCards database, the relevant targets for the drug pair were obtained. Then, we performed the molecular docking of the screened core active ingredients and proteins using the DAVID database and the R AutoDock Vina software. Based on the GSE22255 dataset, these screened target proteins were used to build random forest (RF) and support vector machine (SVM) models. Finally, a new IS nomogram prediction model was constructed and evaluated.

Results

There were 36 active compounds in the EUO-TT drug pair. CHRM1, NR3C1, ADRB2, and OPRD1 proteins of the neuroactive ligand-receptor interaction pathway interacted with the proteins related to the ferroptosis pathway. Molecular docking experiments identified 12 active ingredients of the drug pair that may tightly bind to those target proteins. We constructed a visual IS nomogram prediction model using four genes (CHRM1, NR3C1, ADRB2, and OPRD1). The calibration curve, DCA, and clinical impact curves all indicated that the nomogram model is clinically applicable and diagnostically capable. CHRM1, NR3C1, ADRB2, and OPRD1, the target genes of the four effective components of the EUO-TT drug pair, were considered as risk markers for IS.

Conclusions

The active ingredients of EUO-TT drug pair may act on proteins associated with the neuroactive ligand-receptor interaction pathway to regulate ferroptosis in vascular neurons cells, ultimately affecting the onset and progression of hypertension.

Toll-Like Receptor 4 in Pain: Bridging Molecules-to-Cells-to-Systems

Pain impacts the lives of billions of people around the world - both directly and indirectly. It is complex and transcends beyond an unpleasant sensory experience to encompass emotional experiences. To date, there are no successful treatments for sufferers of chronic pain. Although opioids do not provide any benefit to chronic pain sufferers, they are still prescribed, often resulting in more complications such as hyperalgesia and dependence. In order to develop effective and safe medications to manage, and perhaps even treat pain, it is important to evaluate novel contributors to pain pathologies. As such, in this chapter we review the role of Toll-like receptor 4, a receptor of the innate immune system, that continues to gain substantial attention in the field of pain research. Positioned in the nexus of the neuro and immune systems, TLR4 may provide one of the missing pieces in understanding the complexities of pain. Here we consider how TLR4 enables a mechanistical understanding of pain as a multidimensional biopsychosocial state from molecules to cells to systems and back again.

Identification of Immune-Related Biomarkers for Sciatica in Peripheral Blood

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood. Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the "limma" package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method. Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method. Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.

Therapeutic effect and safety of Tuina on sciatica: A protocol for systematic review and meta-analysis∗

BACKGROUND

Sciatica is one of the common pain symptoms in the human body, also known as radiating leg pain. Sciatica is increasingly occurring due to poor posture and lack of physical exercise all over the world. At present, many studies have indicated that Tuina can improve the clinical symptoms and functional status of sciatica. However, there is currently no relevant systematic review to evaluate and report this clinical scientific issue. Consequently, this study will conduct a meta-analysis on the effectiveness and safety of Tuina therapy for sciatica.

METHODS AND ANALYSIS

Randomized controlled trials (RCTs) related to Tuina treatment of sciatica will be retrieved from the Chinese and English databases and Clinical Trial Register. These databases include China National Knowledge Infrastructure, Wan Fang Database, Chinese Biomedical Literature Database, VIP Database for Chinese Technical Periodicals, PubMed, Embase, Web of Science, Cochrane Library, and Medline, etc. We will consider articles published in English or Chinese between database initiation and October 2021. Our team will use Review Manager Software 5.3 software provided by the Cochrane Collaborative Network to conduct this systematic review and meta-analysis.

RESULTS

This study provides a comprehensive evaluation of the effectiveness and safety of Tuina therapy for sciatica.

CONCLUSION

The conclusion of our study will provide scientific evidence and reference to determine whether Tuina is an effective and safe intervention for patients with sciatica.

REGISTRATION NUMBER

INPLASY2021100034.

Systematic analysis of Long non-coding RNAs reveals diagnostic biomarkers and potential therapeutic drugs for intervertebral disc degeneration

Long non-coding RNAs (lncRNAs) are related to a variety of human diseases. However, little is known about the role of lncRNA in intervertebral disc degeneration (IDD). LncRNA expression profile of human IDD were downloaded from Gene Expression Omnibus (GEO) database. Potential biomarkers and therapeutic drugs for IDD were analyzed by weighted gene co-expression network analysis (WGCNA), R software package Limma, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 1455 differentially expressed genes and 423 differentially expressed lncRNAs. Twenty-six co-expression modules were obtained, among them, the tan, brown, and turquoise modules were most closely related to IDD. The turquoise module contained a large number of differential expressed lncRNAs and genes, these genes were mainly enriched in the MAPK signaling pathway, TGF-beta signaling pathway. Furthermore, we obtained 11,857 LmiRM-Degenerated, these lncRNAs and genes showed higher differential expression multiples and higher expression correlation. After constructing a disease-gene interaction network, 25 disease-specific genes and 9 disease-specific lncRNAs were identified. Combined with the drug-target gene interaction network, three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, which may have curative effects on IDD, were determined. Finally, a genetic diagnosis model and lncRNA diagnosis model with 100% diagnostic performance in both the training data set and the validation data set were established based on these genes and lncRNA. This study provided new diagnostic features for IDD and could help design personalized treatment of IDD.