Identification and verification of ferroptosis-related genes in the pathology of epilepsy: insights from CIBERSORT algorithm analysis

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.

An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes

BACKGROUND

Parkinson's disease (PD), a prevalent neurodegenerative disorder in the aging population, poses significant challenges in unraveling its pathogenesis and progression. A key area of investigation is the disruption of oncological metabolic networks in PD, where diseased cells display distinct metabolic profiles compared to healthy counterparts. Of particular interest are Purine Metabolism Genes (PMGs), which play a pivotal role in nucleic acid synthesis.

METHODS

In this study, bioinformatics analyses were employed to identify and validate PMGs associated with PD. A set of 20 candidate PMGs underwent differential expression analysis. GSEA and GSVA were conducted to explore the biological roles and pathways of these PMGs. Lasso regression and SVM-RFE methods were applied to identify hub genes and assess the diagnostic efficacy of the nine PMGs in distinguishing PD. The correlation between these hub PMGs and clinical characteristics was also explored. Validation of the expression levels of the nine identified PMGs was performed using the GSE6613 and GSE7621 datasets.

RESULTS

The study identified nine PMGs related to PD: NME7, PKM, RRM2, POLR3 C, POLA1, PDE6 C, PDE9 A, PDE11 A, and AMPD1. Biological function analysis highlighted their involvement in processes like neutrophil activation and immune response. The diagnostic potential of these nine PMGs in differentiating PD was found to be substantial.

CONCLUSIONS

This investigation successfully identified nine PMGs associated with PD, providing valuable insights into potential novel biomarkers for this condition. These findings contribute to a deeper understanding of PD's pathogenesis and may aid in monitoring its progression, offering a new perspective in the study of neurodegenerative diseases.

Constructing a Prognostic Model for Subtypes of Colorectal Cancer Based on Machine Learning and Immune Infiltration-Related Genes

This study constructed a prognostic model combining machine learning-based immune infiltration-related genes in each CRC subtype. We used publicly accessible gene expression data and clinical information on colorectal cancer patients. Integrated bioinformatics analysis was used for the identification of immune-wise genes. Machine learning algorithms, like LASSO regression and random forest, were utilised to identify the most important genes that may serve as predictors for patient prognosis. Univariate Cox regression, consensus clustering as well as machine learning algorithms were conducted to construct a prognostic risk scoring model. Analysis of functional enrichment, immune infiltration analyses and copy number variations as well as mutational burdens was performed and validated at the single-cell level. A machine learning-based model is designed with good predictive power-an area under the receiver operating characteristic curve (AUC-ROC) of C-index in cross-validation. The model also achieved good calibration and discrimination ability to stratify patients into high- and low-risk groups with a statistically significant difference in OS (p < 0.05). We have integrated multiple types of gene network features into machine learning systems based on the characteristics of integrating networks with Multi-Expense Learning algorithms, and we propose a robust approach for predicting CRC molecular subtype patient survival. This model could potentially steer personalised treatment strategies and ameliorate outcomes in patients. Although validation in other cohorts and clinical situations is necessary, it may be useful.

Association of coexposure to perfluoroalkyl and polyfluoroalkyl compounds and heavy metals with pregnancy loss and reproductive lifespan: The mediating role of cholesterol

Previous studies have demonstrated the toxic effects of per- and polyfluoroalkyl substances (PFASs) and heavy metals on the reproductive system. However, the interactions and combined effects of these substances remain unexplored. This study utilizes data from the National Health and Nutrition Examination Survey to investigate the associations between coexposure to four types of PFASs, lead (Pb), mercury (Hg) and self-reported pregnancy loss and reproductive lifespan in females. Genes associated with these substances and abortion were identified via the Comparative Toxicogenomics Database. The results revealed that Ln-PFOA (IRR=1.88, 95 % CI=1.42-2.50, Ln--: log transformed), Ln-PFOS (IRR=1.58, 95 % CI=1.12-2.22), Ln-PFHxS (IRR=1.99, 95 % CI=1.57-2.52), and Ln-Hg (IRR=1.92, 95 % CI=1.41-2.43) were positively associated with the risk of pregnancy loss. Ln-PFOA (β=1.27, 95 % CI=0.28-2.27), Ln-PFOS (β=1.01, 95 % CI=0.39-1.63), Ln-PFHxS (β=0.71, 95 % CI=0.12-1.63), Ln-PFNA (β=1.15, 95 % CI=0.23-2.08), Ln-Pb (β=3.87, 95 % CI=2.58-5.15), and Ln-Hg (β=1.01, 95 % CI=0.39-1.64) exposures were positively associated with reproductive lifespan. The mixed and overall effects of coexposure to PFASs and heavy metals were positively correlated with the risk of pregnancy loss and reproductive lifespan. Cholesterol partially mediated the association with the risk of pregnancy loss, whereas delay in menopause fully mediated the association with reproductive lifespan. Significant additive interactions were observed between PFOA and Pb and between PFOS, PFHxS, PFNA and Hg at high levels of coexposure. Thirty-nine overlapping genes associated with abortion were identified for these substances, and further analyses revealed that these genes significantly interact and may contribute to abortion through oxidative stress.

Identification of the ferroptosis-related gene signature and the associated regulation axis in lung cancer and rheumatoid arthritis

Patients with Rheumatoid arthritis (RA) have an elevated risk of lung cancer compared to the healthy population. However, there are few studies on the relationship between RA and lung adenocarcinoma (LUAD), especially the mechanisms at the genetic level. In this study, we investigated the link between RA and LUAD regarding Ferroptosis-Related Genes. The RNA-seq data of RA (GSE77298 and GSE 82107) and LUAD(GSE75037) in the Gene Expression Omnibus (GEO) database were obtained. 259 ferroptosis-related genes were obtained from the website ( http://www.zhounan.org/ferrdb/ ).The differential genes obtained from the RA and LUAD datasets were intersected with ferroptosis-related genes to obtain the ferroptosis-related differentially expressed genes (FRDEGs). Next, the mRNA-miRNA network was constructed, then Gene Set Enrichment Analysis (GSEA) for target genes were performed. The CIBERSORT algorithm was used to analyze the immune infiltration. Finally, the results were validated using external datasets (GSE89408 and GSE48780) and The Cancer Genome Atlas (TCGA) dataset. We obtained FRDEGs common to LUAD and RA: FANCD2, HELLS, RRM2, G6PD, VLDLR. These five genes play important roles in the progression of RA and LUAD. They also hold great diagnostic value for both diseases. Also, we found that LUAD and RA share common signaling pathways and similar immune mechanisms.

Ferroptosis and Pyroptosis in Epilepsy

Epilepsy is sudden, recurrent, and transient central nervous system dysfunction caused by abnormal discharge of neurons in the brain. Ferroptosis and pyroptosis are newly discovered ways of programmed cell death. One of the characteristics of ferroptosis is the oxidative stress generated by lipid peroxides. Similarly, pyroptosis has unique pro-inflammatory properties. As both oxidative stress and neuroinflammation are significant contributors to the pathogenesis of epilepsy, increasing evidence shows that ferroptosis and pyroptosis are closely related to epilepsy. This article reviews the current comprehension of ferroptosis and pyroptosis and elucidates potential mechanisms by which ferroptosis and pyroptosis may contribute to epilepsy. In addition, we also highlight the possible interactions between ferroptosis and pyroptosis because they reportedly coexist in many diseases, and increasing studies have demonstrated the convergence of pathways between the two. This is of great significance for explaining the occurrence and development of epilepsy and provides a new therapeutic perspective for the treatment of epilepsy.