Characterization of Immune-Related Genes and Immune Infiltration Features in Epilepsy by Multi-Transcriptome Data

Hou, Yunqi; Chen, Zhen; Wang, Liping; Deng, Yingxin; Liu, Genglong; Zhou, Yongfen; Shi, Haiqin; Shi, Xiangqun; Jiang, Qianhua

doi:10.2147/jir.s360743

Cited by 11 publications

(9 citation statements)

References 41 publications

(48 reference statements)

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“…Once the hub genes of the disease are identified, targeted therapies can be considered. Because severe SAH can lead to the occurrence of epilepsy, SAH may influence immune cell infiltration and signaling pathway transduction through IRGs, thereby affecting the development of epilepsy episodes ( 31 ). Indeed, MMP9 and C3AR1 are IRGs.…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis

Gao

Li²,

Li³

et al. 2023

Front. Neurol.

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BackgroundAlthough epilepsy has been linked to subarachnoid hemorrhage (SAH), the underlying mechanism has not been fully elucidated. This study aimed to further explore the potential mechanisms in epilepsy and SAH through genes.MethodsGene expression profiles for subarachnoid hemorrhage (GSE36791) and epilepsy (GSE143272) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify the common differentially expressed genes (DEGs) to epilepsy and SAH, which were further analyzed by functional enrichment analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted correlation network analysis (WGCNA) were used to identify common module genes related to the infiltration of immune cells in epilepsy and SAH. Hub module genes were identified using a protein–protein interaction (PPI) network. Finally, the most relevant genes were obtained by taking the intersection points between the DEGs and hub module genes. We performed validation by retrospectively analyzing the RT-PCR levels of the most relevant genes in patients with pure SAH and patients with SAH complicated with epilepsy. Our experiments verified that the SAH and SAH+epilepsy groups were significantly different from the normal control group. In addition, significant differences were observed between the SAH and SAH+epilepsy groups.ResultsIn total, 159 common DEGs–85 downregulated genes and 74 upregulated genes—were identified. Functional analysis emphasized that the immune response was a common feature to epilepsy and SAH. The results of ssGSEA and WGCNA revealed changes in immunocyte recruitment and the related module genes. Finally, MMP9 and C3aR1 were identified as hub genes, and RT-PCR confirmed that the expression levels of the hub genes were higher in epilepsy and SAH samples than in normal samples.ConclusionsOur study revealed the pathogenesis of SAH complicated with epilepsy and identified hub genes that might provide new ideas for further mechanistic studies.

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Section: Discussionmentioning

confidence: 99%

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis

Gao

Li²,

Li³

et al. 2023

Front. Neurol.

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“…We performed single sample gene set enrichment analysis (ssGSEA), which calculates an enrichment score to reveal the absolute enrichment level of a metagene set in a given dataset in each sample and gene set 28 , to count the abundance of immune cells in subgroups (cluster A and B). Immuno-correlation analysis was also performed based on ssGSEA and results visualized using heatmaps and boxplots.…”

Section: Methodsmentioning

confidence: 99%

5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis

Zhou

Sun

et al. 2023

Sci Rep

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Abstract5-methyladenosine (m5C) modification regulates gene expression and biological functions in oncologic areas. However, the effect of m5C modification in chronic hypersensitivity pneumonitis (CHP) and idiopathic pulmonary fibrosis (IPF) remains unknown. Expression data for 12 significant m5C regulators were obtained from the interstitial lung disease dataset. Five candidate m5C regulators, namely tet methylcytosine dioxygenase 2, NOP2/Sun RNA methyltransferase 5, Y-box binding protein 1, tRNA aspartic acid methyltransferase 1, and NOP2/Sun RNA methyltransferase 3 were screened using random forest and nomogram models to predict risks of pulmonary fibrosis. Next, we applied the consensus clustering method to stratify the samples with different m5C patterns into two groups (cluster A and B). Finally, we calculated immune cell infiltration scores via single-sample gene set enrichment analysis, then compared immune cell infiltration, related functions as well as the expression of programmed cell death 1 (PD-1, PDCD1) and programmed death protein ligand-1 (PD-L1, CD274) between the two clusters. Principal component analysis of m5C-related scores across the 288 samples revealed that cluster A had higher immune-related expression than B. Notably, T helper cell (Th) 2 type cytokines and Th1 signatures were more abundant in clusters A and B, respectively. Our results suggest that m5C is associated with and plays a crucial role in development of pulmonary fibrosis. These m5C patterns could be potential biomarkers for identification of CHP and IPF, and guide future development of immunotherapy or other new drugs strategies for pulmonary fibrosis.

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“…The relative abundance of infiltrating immune cells was inferred with CIBERSORTx tool and single sample GSEA (ssGSEA) algorithm 10 . We investigate the differential composition of immune cells between different GPRASP1 subgroups.…”

Section: Methodsmentioning

confidence: 99%

“…The neoantigen and TMB data (based on somatic mutation data) of HNC patients in the TCGA dataset were retrieved from The Cancer Immunome Atlas (TCIA) (https://tcia.at/home). 10 The MSI score was collected from published studies 14 . Then, we detect the above biomarkers differences between different GPRASP1 subgroups.…”

Section: Methodsmentioning

confidence: 99%

“…The relative abundance of infiltrating immune cells was inferred with CIBERSORTx tool and single sample GSEA (ssGSEA) algorithm. 10 We investigate the differential composition of immune cells between different GPRASP1 subgroups. Spearman correlation analyses were further performed to analyse the relationship between GPRASP1 expression with immune cells.…”

Section: Correlation Of Gprasp1 Expression With Immune Cell Infiltrationmentioning

confidence: 99%

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GPRASP1 is a candidate anti‐oncogene and correlates with immune microenvironment and immunotherapeutic efficiency in head and neck cancer

Zhang

Liu

Zhang

et al. 2022

J Oral Pathology Medicine

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Background: G-protein-coupled receptor-associated sorting protein 1 (GPRASP1) plays an important role in tumorigenesis. However, GPRASP1 specific role has not been clarified in head and neck cancer (HNC).Methods: HNC RNA sequencing (RNA-seq) datasets, DNA methylation data, somatic mutation data, copy number variation (CNV) data, and corresponding clinicopathologic information were acquired from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A comprehensive evaluation was performed to explore the relationship of GPRASP1 expression with clinicopathologic characteristics, CNV, and DNA methylation. Additionally, we employed HNC tissue microarray (TMA) to further confirm the relation between GPRASP1 expression and clinical features. Then, we systematically associated the GPRASP1 with immunological properties from numerous perspectives, such as immune cell infiltration, immune-related pathways, immune checkpoint inhibitors (ICIs), immunomodulators, immunogenicity, and immunotherapy.Results: Analyzing TCGA, GEO, and TMA datasets, GPRASP1 is significantly downregulated in HNC compared to normal tissues. The expression of GPRASP1 is significantly negatively correlated with clinical features (perineural invasion, histologic grade, T stage, and TNM stage), and is an independent predictor of favorable prognosis, regardless of other clinicopathological features (HR: 0.42, 95% CI 0.20-0.91, p = 0.028). The etiological investigation found that the abnormal expression of GPRASP1 was related to DNA methylation, not CMV. Subsequently, the high expression of GPRASP1 was significantly correlated with immune cell infiltration (CD8 + T cell, tumor infiltrating lymphocyte), immune-related pathways (cytolytic activity, check-point, human leukocyte antigen), ICIs (CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT), immunomodulators (CCR4/5, CXCL9, CXCR3/4/5), and immunogenicity (immune score, neoantigen, tumor mutation burden). Finally, immunophenoscore and

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Characterization of Immune-Related Genes and Immune Infiltration Features in Epilepsy by Multi-Transcriptome Data

Cited by 11 publications

References 41 publications

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis

Identification of hub genes significantly linked to subarachnoid hemorrhage and epilepsy via bioinformatics analysis

5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis

GPRASP1 is a candidate anti‐oncogene and correlates with immune microenvironment and immunotherapeutic efficiency in head and neck cancer

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