Identification of Immune-Related Hub Genes in Parkinson’s Disease

Integrated Bioinformatics Analysis for Revealing CBL is a Potential Diagnosing Biomarker and Related Immune Infiltration in Parkinson's Disease

Purpose

There is growing evidence that the immune system plays an important role in the progression of Parkinson's disease, the second most common neurodegenerative disorder. This study aims to address the comprehensive understanding of the immunopathogenesis of Parkinson's disease and explore new inflammatory biomarkers.

Patients and Methods

In this study, Immune-related differential expressed genes (DEIRGs) were obtained from GEO database and Immport database. The hub gene was screened in DEIRGs using LASSO regression and random forest algorithm, and the mRNA expression of the identified hub gene was validated using clinical blood samples.

Results

We obtained a total of 157 DEIRGs that played an important role in the immune response. The results of immune cell infiltration analysis showed that the degree of memory B cells infiltration was higher in PD patients, while the degree of Monocytes, resting mast cells and M0 macrophages infiltration was lower (p<0.05). A total of 8 hub genes were screened by machine learning methods, and RT-PCR results showed that the expression level of CBL gene in PD was significantly increased (p<0.05).

Conclusion

Our findings suggest that CBL is a new potential diagnostic biomarker for PD and that abnormal immune cell infiltration may influence PD development.

Integrated bioinformatics analysis for exploring potential biomarkers related to Parkinson's disease progression

BACKGROUND

Parkinson's disease (PD) is a progressive neurodegenerative disease with increasing prevalence. Effective diagnostic markers and therapeutic methods are still lacking. Exploring key molecular markers and mechanisms for PD can help with early diagnosis and treatment improvement.

METHODS

Three datasets GSE174052, GSE77668, and GSE168496 were obtained from the GEO database to search differentially expressed circRNA (DECs), miRNAs (DEMis), and mRNAs (DEMs). GO and KEGG enrichment analyses, and protein-protein interaction (PPI) network construction were implemented to explore possible actions of DEMs. Hub genes were selected to establish circRNA-related competing endogenous RNA (ceRNA) networks.

RESULTS

There were 1005 downregulated DECs, 21 upregulated and 21 downregulated DEMis, and 266 upregulated and 234 downregulated DEMs identified. The DEMs were significantly enriched in various PD-associated functions and pathways such as extracellular matrix organization, dopamine synthesis, PI3K-Akt, and calcium signaling pathways. Twenty-one hub genes were screened out, and a PD-related ceRNA regulatory network was constructed containing 31 circRNAs, one miRNA (miR-371a-3p), and one hub gene (KCNJ6).

CONCLUSION

We identified PD-related molecular markers and ceRNA regulatory networks, providing new directions for PD diagnosis and treatment.

The Influence of Myeloid-Derived Suppressor Cell Expansion in Neuroinflammation and Neurodegenerative Diseases

The neuro-immune axis has a crucial function both during physiological and pathological conditions. Among the immune cells, myeloid-derived suppressor cells (MDSCs) exert a pivotal role in regulating the immune response in many pathological conditions, influencing neuroinflammation and neurodegenerative disease progression. In chronic neuroinflammation, MDSCs could lead to exacerbation of the inflammatory state and eventually participate in the impairment of cognitive functions. To have a complete overview of the role of MDSCs in neurodegenerative diseases, research on PubMed for articles using a combination of terms made with Boolean operators was performed. According to the search strategy, 80 papers were retrieved. Among these, 44 papers met the eligibility criteria. The two subtypes of MDSCs, monocytic and polymorphonuclear MDSCs, behave differently in these diseases. The initial MDSC proliferation is fundamental for attenuating inflammation in Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS), but not in amyotrophic lateral sclerosis (ALS), where MDSC expansion leads to exacerbation of the disease. Moreover, the accumulation of MDSC subtypes in distinct organs changes during the disease. The proliferation of MDSC subtypes occurs at different disease stages and can influence the progression of each neurodegenerative disorder differently.

Screening of key immunerelated gene in Parkinsons disease based on WGCNA and machine learning

OBJECTIVES

Abnormal immune system activation and inflammation are crucial in causing Parkinson's disease. However, we still don't fully understand how certain immune-related genes contribute to the disease's development and progression. This study aims to screen key immune-related gene in Parkinson's disease based on weighted gene co-expression network analysis (WGCNA) and machine learning.

METHODS

This study downloaded the gene chip data from the Gene Expression Omnibus (GEO) database, and used WGCNA to screen out important gene modules related to Parkinson's disease. Genes from important modules were exported and a Venn diagram of important Parkinson's disease-related genes and immune-related genes was drawn to screen out immune related genes of Parkinson's disease. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the the functions of immune-related genes and signaling pathways involved. Immune cell infiltration analysis was performed using the CIBERSORT package of R language. Using bioinformatics method and 3 machine learning methods [least absolute shrinkage and selection operator (LASSO) regression, random forest (RF), and support vector machine (SVM)], the immune-related genes of Parkinson's disease were further screened. A Venn diagram of differentially expressed genes screened using the 4 methods was drawn with the intersection gene being hub nodes (hub) gene. The downstream proteins of the Parkinson's disease hub gene was identified through the STRING database and a protein-protein interaction network diagram was drawn.

RESULTS

A total of 218 immune genes related to Parkinson's disease were identified, including 45 upregulated genes and 50 downregulated genes. Enrichment analysis showed that the 218 genes were mainly enriched in immune system response to foreign substances and viral infection pathways. The results of immune infiltration analysis showed that the infiltration percentages of CD4+ T cells, NK cells, CD8+ T cells, and B cells were higher in the samples of Parkinson's disease patients, while resting NK cells and resting CD4+ T cells were significantly infiltrated in the samples of Parkinson's disease patients. ANK1 was screened out as the hub gene. The analysis of the protein-protein interaction network showed that the ANK1 translated and expressed 11 proteins which mainly participated in functions such as signal transduction, iron homeostasis regulation, and immune system activation.

CONCLUSIONS

This study identifies the Parkinson's disease immune-related key gene ANK1 via WGCNA and machine learning methods, suggesting its potential as a candidate therapeutic target for Parkinson's disease.

The role of CD56bright NK cells in neurodegenerative disorders

Emerging evidence suggests a potential role for natural killer (NK) cells in neurodegenerative diseases, such as multiple sclerosis, Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. However, the precise function of NK cells in these diseases remains ambiguous. The existence of two NK cell subsets, CD56bright and CD56dim NK cells, complicates the understanding of the contribution of NK cells in neurodegeneration as their functions within the context of neurodegenerative diseases may differ significantly. CD56bright NK cells are potent cytokine secretors and are considered more immunoregulatory and less terminally differentiated than their mostly cytotoxic CD56dim counterparts. Hence, this review focusses on NK cells, specifically on CD56bright NK cells, and their role in neurodegenerative diseases. Moreover, it explores the mechanisms underlying their ability to enter the central nervous system. By consolidating current knowledge, we aim to provide a comprehensive overview on the role of CD56bright NK cells in neurodegenerative diseases. Elucidating their impact on neurodegeneration may have implications for future therapeutic interventions, potentially ameliorating disease pathogenesis.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Neutrophils: a subgroup of neglected immune cells in ALS

Amyotrophic lateral sclerosis (ALS) is a chronic, progressive neurodegenerative disease characterized by the loss of motor neurons. Dysregulated peripheral immunity has been identified as a hallmark of ALS. Neutrophils, as the front-line responders of innate immunity, contribute to host defense through pathogen clearance. However, they can concurrently play a detrimental role in chronic inflammation. With the unveiling of novel functions of neutrophils in neurodegenerative diseases, it becomes essential to review our current understanding of neutrophils and to recognize the gap in our knowledge about their role in ALS. Thus, a detailed comprehension of the biological processes underlying neutrophil-induced pathogenesis in ALS may assist in identifying potential cell-based therapeutic strategies to delay disease progression.

Gender differences in microRNA expression in levodopa-naive PD patients

Gender is an important factor influencing epidemiological and clinical features of Parkinson's disease (PD). We aimed to evaluate gender differences in the expression of a panel of miRNAs (miR-34a-5p, miR-146a, miR-155, miR-29a, miR-106a) possibly involved in the pathophysiology or progression of disease. Serum samples were obtained from 104 PD patients (58 men and 46 women) never treated with levodopa. We measured levels of miRNAs using quantitative PCR. Correlations between miRNA expression and clinical data were assessed using the Spearman's correlation test. We used STRING to evaluate co-expression relationship among target genes. MiR-34a-5p was significantly upregulated in PD male patients compared to PD female patients (fc: 1.62; p < 0.0001). No correlation was found with age, BMI, and disease severity, assessed by UPDRS III scale, in male and female patients. MiR-146a-5p was significantly upregulated in female as compared to male patients (fc: 3.44; p < 0.0001) and a significant correlation was also observed between disease duration and mir-146a-5p. No differences were found in the expression of miR-29a, miR-106a-5p and miR-155 between genders. Predicted target genes for miR-34a-5p and miR-146-5p and protein interactions in biological processes were reported. Our study supports the hypothesis that there are gender-specific differences in serum miRNAs expression in PD patients. Follow-up of this cohort is needed to understand if these differences may affect disease progression and response to treatment.