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WGCNA Analysis. (a) Genetic tree diagram; (b) module feature vector clustering heat map; (c, d) analysis of network topologies for various soft threshold powers; (e) and heat map of correlations between module signature genes and differential pyroptosis-related genes.
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Background:
Inflammatory reactions and pyroptosis play an important role in the pathology of intervertebral disc degeneration (IDD). The aim of the present study was to investigate pyroptosis in the nucleus pulposus cells (NPCs) of inflammatory induced IDD by bioinformatic methods and to search for possible diagnostic biomarkers.
Methods:
Gene e...
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Atherosclerosis (AS) is a chronic inflammatory disease, which has a complex interplay between altered immune metabolism and oxidative stress. Therefore, we aimed to determine the oxidative stress and immune-related biomarkers in AS. Differential gene expression analyses are based on the GSE100927 dataset in the Gene Expression Omnibus (GEO), and 38...
Citations
... For the identification of hub genes, least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses were performed to rank individual features on the basis of importance using the "glmnet," "rms," "e1071," "kernlabt," and "caret" R packages [27][28][29]. DEGs screened through these two machine learning methods for feature selection were intersected using the R package "veen" to obtain the hub genes [30]. Thereafter, the expression of the identified hub genes was validated in GSE113873 [22] using the Wilcoxon test, and a p-value of <0.05 was considered statistically significant [31]. ...
Lower-extremity peripheral artery disease (LE-PAD) is a prevalent circulatory disorder with risks of critical limb ischemia and amputation. This study aimed to develop a prediction model for a novel LE-PAD subtype to predict the severity of the disease and guide personalized interventions. Additionally, LE-PAD pathogenesis involves altered immune microenvironment, we examined the immune differences to elucidate LE-PAD pathogenesis. A total of 460 patients with LE-PAD were enrolled and clustered using unsupervised machine learning algorithms (UMLAs). Logistic regression analyses were performed to screen and identify predictive factors for the novel subtype of LE-PAD and a prediction model was built. We performed a comparative analysis regarding neutrophil levels in different subgroups of patients and an immune cell infiltration analysis to explore the associations between neutrophil levels and LE-PAD. Through hematoxylin and eosin (H&E) staining of lower-extremity arteries, neutrophil infiltration in patients with and without LE-PAD was compared. We found that UMLAs can helped in constructing a prediction model for patients with novel LE-PAD subtypes which enabled risk stratification for patients with LE-PAD using routinely available clinical data to assist clinical decision-making and improve personalized management for patients with LE-PAD. Additionally, the results indicated the critical role of neutrophil infiltration in LE-PAD pathogenesis.
... Genetic factors have been identified as playing a role in IDD, and recent research has focused on identifying potential biomarkers for the condition. [5][6][7][8] Weighted gene coexpression network analysis(WGCNA) is a powerful tool for identifying genes driving major signaling pathways. 9 Previous studies by Li et al., 6 Wang et al., 7 and Ma et al. 10 identified potential biomarkers for IDD using WGCNA method, but their findings may have limitations. ...
... [5][6][7][8] Weighted gene coexpression network analysis(WGCNA) is a powerful tool for identifying genes driving major signaling pathways. 9 Previous studies by Li et al., 6 Wang et al., 7 and Ma et al. 10 identified potential biomarkers for IDD using WGCNA method, but their findings may have limitations. Li et al. identified SIRT7, NTRK2, and CHI3L1 from two datasets with different sample origins, which may affect the accuracy and reliability of their results. ...
Background
Intervertebral disc degeneration (IDD) is a significant cause of low back pain and poses a significant public health concern. Genetic factors play a crucial role in IDD, highlighting the need for a better understanding of the underlying mechanisms.
Aim
The aim of this study was to identify potential IDD‐related biomarkers using a comprehensive bioinformatics approach and validate them in vitro.
Materials and Methods
In this study, we employed several analytical approaches to identify the key genes involved in IDD. We utilized weighted gene coexpression network analysis (WGCNA), MCODE, LASSO algorithms, and ROC curves to identify the key genes. Additionally, immune infiltrating analysis and a single‐cell sequencing dataset were utilized to further explore the characteristics of the key genes. Finally, we conducted in vitro experiments on human disc tissues to validate the significance of these key genes in IDD.
Results
we obtained gene expression profiles from the GEO database (GSE23130 and GSE15227) and identified 1015 DEGs associated with IDD. Using WGCNA, we identified the blue module as significantly related to IDD. Among the DEGs, we identified 47 hub genes that overlapped with the genes in the blue module, based on criteria of |logFC| ≥ 2.0 and p .adj <0.05. Further analysis using both MCODE and LASSO algorithms enabled us to identify five key genes, of which CKAP4 and SSR1 were validated by GSE70362, demonstrating significant diagnostic value for IDD. Additionally, immune infiltrating analysis revealed that monocytes were significantly correlated with the two key genes. We also analyzed a single‐cell sequencing dataset, GSE199866, which showed that both CKAP4 and SSR1 were highly expressed in fibrocartilage chondrocytes. Finally, we validated our findings in vitro by performing real time polymerase chain reaction (RT‐PCR) and immunohistochemistry (IHC) on 30 human disc samples. Our results showed that CKAP4 and SSR1 were upregulated in degenerated disc samples. Taken together, our findings suggest that CKAP4 and SSR1 have the potential to serve as disease biomarkers for IDD.
... And recently, we did not find a correlation between hemoglobin reduction and the function of the SMIM1 gene in humans. In addition, SMIM1 is a potential diagnostic marker for intervertebral disc degeneration through bioinformatics and machine learning methods [23]. The cell model with Vel− phenotype could be used in biochemical functions, physiological implications, hematological studies, and applications due to the rarity of Vel− individuals. ...
b> Introduction: The Vel– phenotype is a rare blood group, and it is challenging for identifying this phenotype due to limited available reagents. Moreover, there are relatively few studies on genomic editing of erythroid antigens and generation of knockout (KO) cell lines at present. Methods: To identify the high-efficiency small-guiding RNA (sgRNA) sequence, candidate sgRNAs were transfected into HEK 293T cells and analyzed using Sanger sequencing. Following this, the high-efficiency sgRNA was transfected into K562 cells using lentivirus transduction to generate KO Vel blood group gene cells. The expression of the Vel protein was detected using Western blot on single-cell clones. Additionally, flow cytometry was used to detect the erythroid markers CD235a and CD71. Hemoglobin quantification and Giemsa staining were also performed to evaluate the erythroid differentiation of KO clones induced by hemin. Results: The high-efficiency sgRNA was successfully obtained and used for CRISPR-Cas9 editing in K562 cells. After limiting dilution and screening, two KO clones had either deleted 2 or 4 bases and showed no expression of the Vel protein. In the hemin-induced KO clone, there was a significant difference in erythroid marker and hemoglobin quantification compared to untreated cells. The morphological changes were also observed for the hemin-induced KO clone. Conclusion: In this study, a highly efficient sgRNA was screened out and used to generate Vel erythroid antigen KO single-cell clones in K562 cells. The edited cells could then be induced to undergo erythroid differentiation with the use of hemin.
... were defined as DEGs. The R package 'veen' was used to obtain common DEGs [15]. ...
Background:
The abdominal aortic aneurysm (AAA) incidence is closely related to systemic lupus erythematosus (SLE). However, the common mechanisms between AAA and SLE are still unknown. The purpose of this research was to examine the main molecules and pathways involved in the immunization process that lead to the co-occurrence of AAA and SLE through the utilization of quantitative bioinformatics analysis of publicly available RNA sequencing databases. Moreover, routine blood test information was gathered from 460 patients to validate the findings.
Materials and methods:
Datasets of both AAA (GSE57691 and GSE205071) and SLE (GSE50772 and GSE154851) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were analyzed using bioinformatic tools. To determine the functions of the common differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia analyses were conducted. Subsequently, the hub gene was identified through cytoHubba, and its validation was carried out in GSE47472 for AAA and GSE81622 for SLE. Immune cell infiltration analysis was performed to identify the key immune cells correlated with AAA and SLE, and to evaluate the correlation between key immune cells and the hub gene. Subsequently, the routine blood test data of 460 patients were collected, and the result of the immune cell infiltration analysis was further validated by univariate and multivariate logistic regression analysis.
Results:
A total of 25 common DEGs were obtained, and three genes were screened by cytoHubba algorithms. Upon validation of the datasets, CXCL1 emerged as the hub gene with strong predictive capabilities, as evidenced by an area under the curve (AUC) > 0.7 for both AAA and SLE. The infiltration of immune cells was also validated, revealing a significant upregulation of neutrophils in the AAA and SLE datasets, along with a correlation between neutrophil infiltration and CXCL1 upregulation. Clinical data analysis revealed a significant increase in neutrophils in both AAA and SLE patients (p < 0.05). Neutrophils were found to be an independent factor in the diagnosis of AAA and SLE, exhibiting good diagnostic accuracy with AUC >0.7.
Conclusion:
This study elucidates CXCL1 as a hub gene for the co-occurrence of AAA and SLE. Neutrophil infiltration plays a central role in the development of AAA and SLE and may serve to be a potential diagnostic and therapeutic target.
... In degenerative disc disease, high gene expression of SEZ6L2 has been associated with an inflammatory etiology [23]. Mutation in the SEZ6L2 gene and the broader family of SEZ6 proteins are also in the focus of research on autism [24], febrile seizures in children [25,26], bipolar disorder [9] and schizophrenia [27]. ...
Seizure Related 6 Homolog Like 2 (SEZ6L2) protein has been shown to have implications in neuronal and especially motor function development. In oncology, overexpression of SEZ6L2 serves as a negative prognostic marker in several tumor entities. Recently, few cases of anti-SEZ6L2 antibody mediated cerebellar syndromes were reported. In this article, we present a case of a 70-year-old woman with subacute onset of gait disturbance, dysarthria and limb ataxia. Serum anti-SEZ6L2 antibodies were markedly increased, and further diagnostic workup revealed left sided breast cancer. Neurological symptoms and SEZ6L2 titer significantly improved after curative tumor therapy. This is a very rare and educationally important report of anti-SEZ6L2 autoimmune cerebellar syndrome with a paraneoplastic etiology. Additionally, we performed a review of the current literature for SEZ6L2, focusing on comparing the published cases on autoimmune cerebellar syndrome.
