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. 2022 May 5:15:2855-2876.
doi: 10.2147/JIR.S360743. eCollection 2022.

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

Yunqi Hou #  1 , Zhen Chen #  2 , Liping Wang  3 , Yingxin Deng  1 , Genglong Liu  4 , Yongfen Zhou  1 , Haiqin Shi  1 , Xiangqun Shi  1 , Qianhua Jiang  2
Affiliations

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

Yunqi Hou et al. J Inflamm Res. .

Abstract

Background: Epilepsy encompasses a group of heterogeneous brain diseases that afflict about 1% of the world's population. Accumulating evidence shows that the immune system plays a key role in epileptogenesis. Nevertheless, the immune-related mechanisms remain not been precisely understood.

Methods: Three epilepsy datasets (GSE16969, GSE32534 and GSE143272) were screened to obtain differentially expressed immune-related genes (DEIRGs). Random forest (RF) and protein-protein interaction (PPI) network were constructed to identify core genes. Another dataset (GSE31718) and 60 clinical samples via quantitative real-time polymerase chain reaction (qRT-PCR) were utilized to validate core genes. Immune cell infiltration score was performed with CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched during normal and epilepsy. The correlation between hub genes, immune cells, and enriched molecular pathways was evaluated by Pearson correlation analysis.

Results: Based on RF and PPI, 4 DEIRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were identified as hub genes. Results of qRT-PCR validated that higher expression levels of CSF1R, IL6R, TLR2, and TNFRSF1A in epilepsy samples compared to control sample. Immune infiltration analysis by CIBERSORTx displayed immune signatures that are significantly richer in epilepsy, T cell subsets in particular. Notably, ssGSEA found that Th1 signatures were more abundant in normal tissues; yet Th2 signatures were more abundant in epilepsy tissues. Cytokine cytokine receptor interaction (CCR) was significantly enriched in epilepsy based on multi-transcriptome data. Additionally, hub genes were significantly correlated with score of Th1/Th2 signatures and enrichment score of CCR in multi-transcriptome data.

Conclusion: Four IRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were closely correlated pathogenesis of epilepsy, which may be by impacting CCR and the balance of Th1/Th2 signatures involved in the occurrence of epilepsy. Our data offer compelling insights into the pathogenesis and promising therapeutic targets for epilepsy.

Keywords: CCR; Th1/Th2 signatures; epilepsy; immune infiltration; immune-related genes.

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Conflict of interest statement

The authors declare that they have no competing interests in this work.

Figures

Figure 1
Figure 1
The comparison of the microenvironment score in normal samples and epilepsy samples. (A) Immune score in GESM sets. (B) Stromal score in GESM sets. (C) Immune score in GSE16969 sets. (D) Stromal score in GSE16969 sets. *p < 0.05, and **p ≤ 0.01.
Figure 2
Figure 2
(A) UpSet plot presents the intersection of three datasets. (B) The location of differentially expressed immune-related genes (DEIRGs) on 23 chromosomes.
Figure 3
Figure 3
Functional enrichment analysis of differentially expressed immune related genes (DEIRGs) in GSEM + GSE143272 datasets. (A) Gene ontology (GO) analysis on biological process (BP), cellular component (CC), and molecular function (MF). (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
Figure 4
Figure 4
Random forest (RF) algorithm to screen hub genes in GSEM + GSE143272 datasets. (A) Distribution diagram of regression tree and error. (B) The top 30 most important variables ranked by IncNodePurity.
Figure 5
Figure 5
(A) The top 30 DEIRGs ranked by the number of nodes based on the PPI network. (B) UpSet plot present the intersection of six methods (Betweenness (top 15), Degree (top 15), MCC (top 15), PPICount (top 20), DiffGs (top 20), and RF (top 20)).
Figure 6
Figure 6
(A) The receiver operating characteristic (ROC) curves of the diagnostic efficacy from combined hub genes in GSE31718 dataset. (B) Correlation between combined hub genes and epileptic frequency. (C–F) Verification of hub genes via qRT-PCR on blood PBMCs - Comparison of gene expression levels of 4 hub genes between controls and epilepsy samples. *p < 0.05, and **p ≤ 0.01.
Figure 7
Figure 7
Comparison of infiltrating immune cells between normal samples and epilepsy samples. (A) GSE143272 sets based on CIBERSORTx. (B) GSE143272 sets based on ssGSEA. (C) GESM sets based on ssGSEA.
Figure 8
Figure 8
Correlation between hub genes and infiltrating immune cells. (A) GSE143272 sets. (B) GESM sets. Yellow represents common between GESM and GSE143272. *p < 0.05, **p ≤ 0.01, and ***p ≤ 0.001.
Figure 9
Figure 9
Correlation between hub genes and Th1 cells in GSE143272 sets and GESM sets. (A) Association between CSF1R and Th1 cells in GSE143272 sets. (B) Association between CSF1R and Th1 cells in GESM sets. (C) Association between IL6R and Th1 cells in GSE143272 sets. (D) Association between IL6R and Th1 cells in GESM sets. (E) Association between TLR2 and Th1 cells in GSE143272 sets. (F) Association between TLR2 and Th1 cells in GESM sets. (G) Association between TNFRSF1A and Th1 cells in GSE143272 sets. (H) Association between TNFRSF1A and Th1 cells in GESM sets.
Figure 10
Figure 10
Correlation between hub genes and Th1/Th2 cells in GSE143272 sets and GESM sets. (A) Association between CSF1R and Th1/Th2 cells in GSE143272 sets. (B) Association between CSF1R and Th1/Th2 cells in GESM sets. (C) Association between IL6R and Th1/Th2 cells in GSE143272 sets. (D) Association between IL6R and Th1/Th2 cells in GESM sets. (E) Association between TLR2 and Th1/Th2 cells in GSE143272 sets. (F) Association between TLR2 and Th1/Th2 cells in GESM sets. (G) Association between TNFRSF1A and Th1/Th2 cells in GSE143272 sets. (H) Association between TNFRSF1A and Th1/Th2 cells in GESM sets.
Figure 11
Figure 11
Heatmap displays differences in pathway activities between normal samples and epilepsy samples in GESM datasets. Red indicates epilepsy samples, while blue indicates normal samples.
Figure 12
Figure 12
Comparison of the immune-related pathway between normal samples and epilepsy samples. (A) GESM sets. (B) GSE143272 sets.
Figure 13
Figure 13
Correlation between hub genes and pivotal molecular pathways. (A) GESM sets based on GSVA. (B) GESM sets based on ssGSEA. (C) GSE143272 sets based on ssGSEA. *p < 0.05, **p ≤ 0.01, and ***p ≤ 0.001.
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