Bioinformatics analysis identifies hub genes and immune infiltration landscapes in human brain temporal lobe epilepsy

doi:10.21203/rs.3.rs-4813314/v1

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Research Article

Bioinformatics analysis identifies hub genes and immune infiltration landscapes in human brain temporal lobe epilepsy

https://doi.org/10.21203/rs.3.rs-4813314/v1

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Background

Temporal lobe epilepsy (TLE) is a prevalent type of focal refractory epilepsy with uncertain underlying molecular mechanisms. This study aimed to elucidate the hub genes and potential mechanisms associated with TLE in the human brain, as well as to characterize the immune infiltration landscape. The datasets GSE190451 and GSE205661 were retrieved from the Gene Expression Omnibus (GEO) database for differential expression analysis and weighted gene co-expression network analysis (WGCNA). Subsequent functional enrichment was performed to explore the biological functions of the differentially expressed genes. Hub genes related to temporal lobe epilepsy were identified by protein-protein interaction network analysis and cytoHubba analysis, with validation conducted using the external dataset GSE128300. Furthermore, CIBERSORT analysis was applied to explore the immune landscape of temporal lobe epilepsy and the immune associations of hub genes. Finally, the expression of hub genes was validated through qRT-PCR and Western Blot experiments.

Results

A total of 163 differential expressed genes were identified, significantly enriched in inflammatory and immune-related pathways, and four hub genes (TLR4, CSF1R, P2RY12, CX3CR1) were further identified. Immune cell infiltration analysis showed that macrophage (microglia) activation was involved in TLE, and the hub genes were significantly positively correlated with macrophages (microglia). Hub genes expression was upregulated in TLE patients.

Conclusions

This work revealed several genes that may be involved in the pathogenesis of TLE by regulating immune cell function, providing new insights to explore the pathogenesis of TLE.

Temporal lobe epilepsy

Bioinformatics analysis

Weighted gene co-expression network analysis

Immune landscape

Macrophage

Microglia

Epilepsy is a prevalent and significant chronic neurological disorder on a global scale, characterized by aberrant synchronized neuronal discharge activity in the brain ^[1], affecting approximately 70 million people worldwide ^[2]. About 70% of patients with epilepsy are adequately controlled by standardized antiepileptic drugs (AEDs), and 30% are still drug-resistant, which imposes a heavy burden on society, families, and individuals ^{[3, 4]}. Current AEDs only relieve symptoms and reduce seizures, but do not suppress epilepsy or alter the progression of the disease ^[3]. This reflects the need for a deeper understanding of the pathogenic events and molecular changes that occur during epileptogenesis to overcome the limitations of current antiepileptic drugs.

Temporal lobe epilepsy (TLE) is one of the most common focal epilepsy, accounting for 60% of epilepsy cases, with varying degrees of cognitive decline and mood disorders ^{[5, 6]}. The pathogenesis of TLE is complex and the underlying pathophysiology is not yet fully understood. Current research has focused on neurotransmission and ion channels ^[7–9]. Imbalances in excitatory and inhibitory neurotransmission and the formation of aberrant epileptic networks are thought to be common mechanisms underlying various seizures. However abnormal network remodeling and hyperexcitability are associated with large-scale changes in gene transcription and protein expression ^[10]. A growing body of research is finding that immune responses and neuroinflammation contribute to the process of epileptogenesis by lowering the threshold for seizures and that seizures exacerbate the inflammatory state of the central nervous system, which in turn increases the frequency and severity of seizures. Neuroinflammation and epilepsy trigger each other, thus perpetuating the susceptibility to seizures in the brain ^[11–13]. Abnormal activation of innate immune cells (microglia and astrocytes) and infiltration of adaptive immune cells (regulatory T-cells, helper T-cells, and B-cells, etc.) in damaged tissues, with their rapid release of inflammatory molecules, including damage-associated molecular patterns, cytokines, chemokines, complement factors, and associated effector pathways, generate a microenvironment that is favorable for seizures through different molecular regulatory mechanisms, thus increasing the susceptibility to seizures ^[13–15]. However, the underlying molecular mechanisms are still not systematically understood, especially regarding the role of immune genes and immune cells in TLE. Analyzing relevant characteristics of TLE patients may help explore TLE pathogenesis and treatment.

This study proposes to identify the hub genes associated with TLE and their correlation with immune cells through bioinformatics analysis methods, thus revealing the potential molecular mechanisms and therapeutic targets of TLE, enhancing our understanding of the role of immune inflammation in the pathogenesis of TLE, and laying a new theoretical foundation for the future treatment of TLE.

Download and processing of transcriptome datasets

The research workflow is illustrated in Fig. 1. We downloaded the microarray expression profiling datasets GSE190451(GPL24676) and GSE205661(GPL19072) associated with temporal lobe epilepsy from the NCBI GEO database (https://www.ncbi.nlm.nih.gov/geo/). The GSE190451 dataset includes 3 normal controls and 3 TLE patients. The required mRNA data were extracted from GSE205661, which included 7 normal temporal lobe and 6 TLE brain tissue specimens. In addition, the GSE128300 (GPL10999) dataset was used for external validation. All data were normalized by log2 to facilitate subsequent analysis.

Analysis of differentially expressed genes(DEGs)

To identify DEGs between TLE and normal brain tissue, we analyzed the GSE190451 and GSE205661 datasets using the limma package in R. DEGs were judged using the following criteria: adjusted p-value < 0.05 and |log2 Fold Change| ≥ 1.5. We were using the "heatmap and ggplot2" packages to generate heatmaps and volcano maps to enhance the visualization of DEGs.

Weighted gene co-expression network analysis (WGCNA)

Weighted gene co-expression network analysis (WGCNA) aims to find co-expressed gene modules (modules) and to explore the associative relationships between gene networks and TLE phenotypes, as well as the hub genes in the networks. We performed cluster analysis on the GSE205661 dataset with a suitable soft threshold. Subsequently, we converted the adjacency matrix into a topological overlap matrix (TOM). Next, we perform hierarchical clustering to identify modules containing at least 100 genes (min ModuleSize = 100). Finally, we computed feature genes, merged similar modules, and calculated module gene-phenotype correlations.

Gene Ontology (GO) Functional Enrichment Analysis

GO (Gene Ontology) is a database produced by the Gene Ontology Consortium that defines and describes the activities of genes and proteins in various species. GO analyses include cellular components (CC), biological processes (BP), and molecular function (MF). GO enrichment analysis was performed using the enrichment GO function in the "Cluster Profile package". We present the top 6 signaling pathways with the smallest p-values.

Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of high-throughput biological databases dealing with genomes, cells, diseases, and signaling pathways. Enriched pathways are generated by comparing detected genes with the set of pathway genes in the database. KEGG enrichment analysis was performed using the enrichment KEGG feature of the Cluster Profile R package. We present the top 10 signaling pathways with the smallest p-values. p < 0.05 and log |FC|>0 were considered statistically significant.

Protein-protein interaction (PPI) network analysis

The target genes were entered into the STRING database, which was used to search for interactions between proteins expressed by DEGs using the STRING database (https://stringdb.org) online platform. The results were then imported into the Cytoscape software to generate a PPI network based on the STRING database. The top 10 genes with the highest connectivity were filtered using the cytoHubba plugin in the Cytoscape program and the results were visualized.

Immune infiltration analysis

To compare the differences in immune cells between TLE and normal brain tissue, normalized data from the GSE205661 dataset were uploaded to CIBERSORT (https://cibersort.stanford.edu/) and then compared with the LM22 (22 immune cell types) gene profile. Histograms were drawn using the "ggplot2" package to visualize the content of the 22 infiltrating immune cells in each sample, and correlation heatmaps were drawn using the "corrplot" package to visualize the correlation of immune cell subtypes. Introducing ssGSEA, the R package "GSVA" was used to quantify the relative infiltration of immune cell types, and the "ggplot2" package was used to draw box plots to demonstrate the differences in immune cells between TLE and normal brain tissue. To identify associations between hub genes and different immune cell populations, we performed Spearman correlation analyses and used the "corrplot" package to create correlation heat maps.

Real-time polymerase chain reaction(qRT-PCR) to verify the expression of hub genes

We collected 7 cases of brain tissue at the Neurological Medicine Centre of the Seventh Affiliated Hospital of Sun Yat-sen University, including 3 cases of surgically resected temporal lobe brain tissue from TLE patients, and 4 cases of non-epileptogenic brain tissue from patients with benign temporal lobe tumors. We used density gradient centrifugation to separate the brain tissues of the seven cases, and then we cut and homogenized the tissues in Trizol (Vazyme Biotech, Nanjing, China), extracted the RNA, and synthesized the cDNA using a reverse transcription reagent (Vazyme Biotech). cDNA was synthesized using the SYBR Green Premix reagent (Vazyme Biotech) and PCR system (Applied Biosystems, Foster City, CA, USA) for PCR analysis. Relative gene expression was calculated by the 2^−ΔΔCt method. The primer sequences we used are shown below. Results are expressed as mean ± SEM of replicate samples from three independent experiments.

Primer	Nucleotide sequence (5′–3′)
GAPDH	Forward: GGCCTCCAAGGAGTAAGACC Reverse: AGGGGAGATTCAGTGTGGTG
TLR4	Forward: ATGCCCCATCTTCAATTGTC Reverse: AGTGAGGATGATGCCAGGAT
CSF1R	Forward: GGGAATCCCAGTGATAGAGCC Reverse: TTGGAAGGTAGCGTTGTTGGT
P2RY12	Forward: CACTGCTCTACACTGTCCTGT Reverse: AGTGGTCCTGTTCCCAGTTTG
CX3CR1	Forward: GTGGTGCTGACAAAGCTTGGAA Reverse: TCACTGGGTGCCATCGTAAGAA

Western blot(WB)to verify hub gene expression

Brain tissues from 7 patients were weighed, lysed in RIPA lysis buffer on ice (Beyotime Institute of Biotechnology, Shanghai, China) for 20 min, and then centrifuged at 13,000 rpm for 20 min to obtain the protein lysates. The BCA kit (Beyotime, China) determined the protein concentration and normalized to the process. After aliquots of proteins were separated on SDS-PAGE, samples were transferred to PVDF membranes, washed with TBST, and closed with TBST containing 5% milk powder. Primary antibodies were diluted 1:1000 overnight at 4⁰C and secondary antibodies were diluted 1:10,000 for 1 hour at room temperature. Images were taken using the Licor Odyssey.

Statistical analysis

For data analysis and visualization, all statistical analyses were performed using GraphPad Prism (8.0) and R4.3.1. Measurement data were expressed as x ± s, and independent samples t-test was used for comparison of the means between the two groups, while rank-sum test was used if the variance was not homogeneous, and the difference was considered statistically significant with a P-value < 0.05.

Screening and functional enrichment analysis of DEGs in the GSE190451 and GSE205661 datasets

We identified a total of 710 DEGs in the GSE190451 dataset. 374 genes were up-regulated and 336 genes were down-regulated (Fig. 2A, Supplementary Table 1).GO enrichment analyses showed that the biological processes of these genes were mainly involved in positive regulation of cell adhesion, steroid hormone response, and myeloid leukocyte activation. Cellular components were enriched in local adhesion, cell-substrate junctions, cell projection membranes. Molecular functions were enriched in MHC protein complex binding, integrin binding (Fig. 2C).KEGG enrichment analysis showed that these genes were mainly involved in the pathways of actin cytoskeleton regulation, cellular adhesion, and phagocytosis(Fig. 2D).

We identified a total of 251 DEGs in the GSE205661 dataset, of which 181 genes were up-regulated and 70 genes were down-regulated (Fig. 3A, Supplementary Table 2). GO enrichment analysis showed that the biological processes of these genes were mainly involved in the positive regulation of cellular adhesion, leukocyte-mediated immunity, and the modulation of chemokine production. Cellular components were enriched in collagen-containing extracellular matrix. Molecular functions were enriched in immune receptor activity (Fig. 3C). KEGG enrichment analysis showed that these genes were mainly involved in neuroactive ligand-receptor interactions, cytokine-cytokine receptor interactions, and PI3K-Akt signaling pathway (Fig. 3D).

WGCNA identifies gene modules associated with TLE

To further explore the differences in transcriptome sequencing between TLE and normal control samples, WGCNA was performed on the GSE205661 dataset. We found that the optimal soft threshold value was 10(R² = 0.85), and the data matched the power law distribution, which was suitable for subsequent analysis (Fig. 4A). Next, we constructed an adjacency matrix and constructed a topological overlap matrix. Finally, 19 modules were identified based on average hierarchical clustering and dynamic tree clipping (Fig. 4B). The light yellow module was highly correlated with TLE with p < 0.05 and a correlation of 0.9(Fig. 4C). In the light yellow module, a positive correlation was observed between MM and GS (Cor = 0.79) (Fig. 4D). Therefore, light yellow module-associated genes were included in subsequent analyses of potential TLE-associated genes (Supplementary Table 3).

Construction of PPI network and identification of hub genes

After taking the intersection of the GSE190451 dataset DEGs and GSE205661 dataset DEGs and the genes of the TLE-related module, 163 genes related to TLE were obtained (Fig. 5A). GO functional enrichment analysis showed that the biological processes of these genes were mainly involved in the positive regulation of leukocytes, lymphocytes, and monocytes. Cellular components were enriched in the transport vesicles and MHC protein complex, and molecular functions were enriched in the MHC protein complex binding, and G protein-coupled purinergic nucleotide receptor activity (Fig. 5C). KEGG enrichment analysis showed that these genes were mainly involved in the phospholipase D signaling pathway, cell adhesion molecules, processing and presentation of antibodies and other pathways (Fig. 5D). To find the hub genes among these genes, PPI network analysis was performed (Fig. 5B) and the top 10 genes were calculated using the cytoHubba plug-in MCC method (Fig. 5E). We found TLR4, CSF1R, P2RY12, and CX3CR1 as the top 4 ranked hub genes.

Immune landscape and association of hub genes with immune cells

To clarify the relative proportions of the 22 immune cell types, the CIBERSORT algorithm was performed on the GSE205661 dataset. In addition, to gain insight into the immune microenvironment of TLE, specific immune cell types in the TLE patient cohort were analyzed using ssGSEA (Fig. 6A). In addition, a visual heatmap was generated to depict the ssGSEA assessment of immune cell infiltration (Fig. 6B). Levels of M2-type macrophages were significantly elevated in the TLE group (Fig. 6C). On the contrary, the abundance of monocytes was higher in the normal control group. All four hub genes (TLR4, CSF1R, P2RY12, CX3CR1) were significantly and positively correlated with M2-type macrophages. In addition, CSF1R was positively correlated with CD8 + T cells and negatively correlated with monocytes and helper T cells, and CX3CR1 was negatively correlated with monocytes (Fig. 6D). The above results suggest that immune cells and immune responses are involved in the development of TLE.

Validation of hub genes in datasets

In the GSE190451 and GSE205661 datasets, we found that TLR4, CSF1R, P2RY12, and CX3CR1 were upregulated in TLE patients (Fig. 7A-B). In the external GSE128300 dataset, TLR4, CSF1R, P2RY12, and CX3CR1 were also expressed up-regulated in TLE patients (Fig. 7C), suggesting that these four hub genes may play important roles in TLE.

qRT-PCR and WB verification of hub genes expression

We performed qRT-PCR and WB analyses on three temporal lobe epileptogenic tissues and four control brain tissues to validate the expression levels of the hub genes (TLR4, CSF1R, P2RY12, CX3CR1). qRT-PCR analysis showed that the mRNA expression levels of TLR4, CSF1R, P2RY12, and CX3CR1 in the brain tissues of TLE patients were significantly higher than that of the control group (Fig. 8A). WB analysis showed that the protein expression levels of TLR4, CSF1R, P2RY12, and CX3CR1 in the brain tissues of TLE patients were significantly higher than that of the control group (Fig. 8B). These suggested that the high expression of hub genes might be involved in the disease process of TLE.

We integrated multiple transcriptome datasets to identify 163 differential genes, and based on PPI and cytoHubba, identified four hub genes (TLR4, CSF1R, P2RY12, and CX3CR1). CIBERSORT immune infiltration analysis showed a significant increase in the amount of immune cells (macrophages) in TLE. All four hub genes were significantly positively correlated with macrophages. In addition, CSF1R was positively correlated with CD8 + T cells and negatively correlated with helper T cells. The qRT-PCR and WB results showed that the expression levels of TLR4, CSF1R, P2RY12, and CX3CR1 in brain tissues of TLE patients were higher than that of controls, which was consistent with the results of bioinformatics analysis of GEO database. This suggests us that these four hub genes may play an important role in the TLE disease process.

The disease mechanisms of TLE are not yet fully understood, especially regarding the role of the immunogenome and immune infiltration in TLE. However, there is growing evidence that several components of the immune system, including alterations in the function or number of immune cells, the release of pro-inflammatory cytokines and chemokines, and the inhibition of the expression of receptors or their ligands, are crucial factors in the development and progression of epilepsy^[16–18]. Suppression of neuroinflammation has been shown to attenuate neuronal damage and achieve neuroprotection in epilepsy ^{[13, 16, 19, 20]}.

Toll-like receptor 4 (TLR4) is a member of the Toll-like receptor family and plays an important role in initiating innate and adaptive immune pathways in vivo.TLR4 is predominantly expressed by immune cells, such as macrophages, and microglia are the most classical macrophage population in the CNS, which is consistent with the results of our immune infiltration analysis^[13]. Major immune functions of TLR4 include the regulation of cytokine secretion and phagocytic activity of microglia ^[21]. Several previous studies have found that TLR4 signaling in the brain drives autoimmune responses and triggers neuroinflammation, which is involved in the pathogenesis of degenerative diseases such as Alzheimer's disease (AD) ^[22], Parkinson's disease (PD) ^[23], and multiple sclerosis (MS) ^[24]. Although the role of TLR4 in these neurodegenerative diseases has been extensively studied, its potential significance and mechanism of action in epileptogenesis have not been fully investigated. KAMAŞAK et al. found that serum levels of TLR4 were significantly elevated in patients with drug-refractory epilepsy and correlated with the severity of epilepsy, and therefore considered as a biomarker for refractory epilepsy^[25].TLR4 expression was found to be upregulated in neurons and astrocytes in animal models of acute and chronic spontaneous epilepsy, and the TLR4-HMGB1 axis may be involved in epileptogenesis and recurrence.TLR4 antagonists reduced seizure frequency and duration while delaying seizures, suggesting a targeting potential for the contribution of TLR4 to epileptogenesis^[26–29]. Thus TLR4 may be a new therapeutic target for temporal lobe epilepsy.

Colony-stimulating factor 1 receptor (CSF-1R) belongs to the type III protein tyrosine kinase receptor family, which is mainly expressed in the macrophage lineage and regulates macrophage homeostasis, osteoclast formation, and microglia development and maintenance^[30].CSF-1 and interleukin (IL)-34 play key roles in macrophages by regulating different signal transduction. Over-activation of the CSF-1/CSF-1R axis leads to the aberrant expression of pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), which promotes the development of inflammatory diseases^[31]. There is growing evidence that CSF-1R may be directly or indirectly involved in neurological disorders. Nynke et al. report 2 cases of white matter encephalopathy with childhood-onset caused by a pure mutation in CSF1R, which can lead to congenital microglial cell deficiency and seizures^[32]. Other studies find that CSF-1R inhibition slows amyotrophic lateral sclerosis progression^[33]. CSF-1R Inhibitor PLX339 Eliminates Microglia and Reduces Plaque Formation in an Alzheimer's Disease Model ^{[34, 35]}. Recent studies find that inhibition of CSF1R reduces seizures in a mouse model of pilocarpine epilepsy^{[36, 37]}. Therefore, inhibition of CSF-1R holds great promise in anti-inflammatory therapy, but the exact mechanism needs to be further investigated.

P2RY12, a member of the P2 purinergic receptor family, is a G protein-coupled receptor containing seven transmembrane structural domains, which is mainly expressed in platelets and microglia^[38]. Although P2RY12 appears to be a good marker for microglia in the brain, whether expressed microglia are protective or pro-inflammatory is controversial. Deficient P2RY12 expression in microglia has been reported in most neuropathologies associated with neuroinflammation^[39]. Amadio et al. highlighted the progressive loss of P2RY12 immunoreactivity in animals and humans as an early manifestation of the markers neuroinflammation and microglia activation, with changes in P2RY12 expression levels dependent on microglia phenotype ^[38]. However, blockade of microglia P2RY12 with the antagonists tegretol or clopidogrel was found to reduce ischemic injury and neurotoxicity in microglia by reducing their migration to the site of injury in an ischemic animal model ^{[40, 41]}. A recent in vitro study on microglia showed that microglial P2RY12 is involved in nuclear factor kappa-B (NF-κB) activation and NLRP3 inflammasome activation, resulting in pro-inflammatory consequences ^[42]. Walker et al. find that upregulation of P2RY12 may be a feature of advanced Alzheimer's disease ^[43]. Previous studies have found significant upregulation of P2RY12 in experimental epilepsy models, with only a slight, non-significant increase in brain tissue from TLE patients ^[44]. However, the present study found that P2RY12 is significantly elevated in TLE patients and is involved in the TLE seizure process, which may be a potential target for temporal lobe epilepsy treatment. However, further studies are needed to clarify the exact role played by P2RY12 in the disease process of TLE.

CX3CR1 is a receptor containing seven transmembrane structural domains coupled to the Gi and Gz isoforms of G proteins and is expressed predominantly on microglial cells^[45].CX3CL1 is a member of the chemokine gene family, also known as fractalkine, produced in neurons. Studies have shown that CX3CL1 binds to its unique receptor CX3CR1 to regulate microglia activation and is essential for maintaining brain function^[46]. It was found that CX3CL1-/- and CX3CR1-/- mice exhibit increased neurotoxicity in many models of neuroinflammation and neurodegenerative disease, associated with increased microglial pro-inflammatory activity and excessive release of pro-inflammatory factors, including inducible nitric oxide synthase (NOS), IL-1β, TNF-α and IL-6^[47–49]. Several studies have suggested that CX3CL1/CX3CR1 signaling is involved in the pathogenesis of epilepsy. Xu et al. reported increased expression of CX3CL1 in epilepsy patients and rat models^[50]. Eyo et al. found in an animal model of epilepsy that CX3CR1 deficiency reduced microglia interaction with neuronal axons and dendrites and that blocking CX3CL1 signaling increased seizures^[51]. These suggest that the CX3CL1/CX3CR1 axis is a key regulator of potential neuroprotective microglia-neuron interactions. However, Wu et al. found in an animal model of pilocarpine epilepsy that microglia may exacerbate epilepsy-induced brain damage by upregulating TLR4/NF-κB/CX3CR1 after seizures^[52]. Therefore, more research is needed to determine the specific role of CX3CR1 in temporal lobe epilepsy.

However, our study still has some limitations. Although we validated elevated hub gene expression in TLE patients, we failed to explore the mechanisms in more depth. Further, follow-up is needed to clarify the role of the hub genes in animal experiments and more TLE patients.

In conclusion, by comparing the transcriptome sequencing data of normal and TLE brain tissues, differentially expressed genes were screened to further analyze and mine the PPI network. We identified four hub genes with elevated expression in TLE patients, associated with microglia activation. These genes reveal the important role of microglia in TLE-mediated immune regulation and inflammatory responses and provide a theoretical basis for further research.

Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Seventh Affiliated Hospital of Sun Yat-sen University (KY-2024-177-01).

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Consent to publish: The authors affirm that human research participants provided informed consent for publication of the images in Fig 8.

Data availability statement: All relevant data are within the paper.

Conflict of Interest: The authors declare no competing interests.

Funding: This work was funded by the National Natural Science Foundation of China (file no. LMZ 82071447,82371456), the Shenzhen Municipal Science and Technology key projects of the Basic Research Program (file no. LMZ JCYJ20220818102007015).

Author Contributions: LMZ and YQZ contributed to the study conception and design. Material preparation, data collection, and analysis were performed by YQZ, LL, QHZ, YCL, YYL, and YBF. The first draft of the manuscript was written by YQZ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgments: We would like to thank the Experimental Center, Seventh Affiliated Hospital of Sun Yat-sen University.

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Bioinformatics analysis identifies hub genes and immune infiltration landscapes in human brain temporal lobe epilepsy

Status:

Version 1

Abstract

Background

Results

Conclusions

Figures

Background

Materials and methods

Download and processing of transcriptome datasets

Analysis of differentially expressed genes(DEGs)

Weighted gene co-expression network analysis (WGCNA)

Gene Ontology (GO) Functional Enrichment Analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis

Protein-protein interaction (PPI) network analysis

Immune infiltration analysis

Real-time polymerase chain reaction(qRT-PCR) to verify the expression of hub genes

Western blot(WB)to verify hub gene expression

Statistical analysis

Results

Screening and functional enrichment analysis of DEGs in the GSE190451 and GSE205661 datasets

WGCNA identifies gene modules associated with TLE

Construction of PPI network and identification of hub genes

Immune landscape and association of hub genes with immune cells

Validation of hub genes in datasets

qRT-PCR and WB verification of hub genes expression

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1