Identification of significant immune-related genes for epilepsy via bioinformatics analysis

Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma

Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments. The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs. We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

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.

Identification of Ferroptosis-related potential biomarkers and immunocyte characteristics in Chronic Thromboembolic Pulmonary Hypertension via bioinformatics analysis

BACKGROUND

Chronic Thromboembolic Pulmonary Hypertension (CTEPH) is a form of pulmonary hypertension with a high mortality rate. A new type of iron-mediated cell death is Ferroptosis, which is characterized by the accumulation of lethal iron ions and lipid peroxidation leading to mitochondrial atrophy and increased mitochondrial membrane density. Now, there is a lack of Ferroptosis-related biomarkers (FRBs) associated with pathogenic process of CTEPH.

METHODS

The differentially expressed genes (DEGs) of CTEPH were obtained by GEO2R. Genes related to Ferroptosis were obtained from FerrDb database. The intersection of Ferroptosis and DEGs results in FRBs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed in Database for Annotation, Visualization and Integrated Discovery (DAVID) database. The optimal potential biomarkers for CTEPH were analyzed by least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) machine learning. The four hub genes were verified from the Gene Expression Omnibus (GEO) dataset GSE188938. Immune infiltration was analyzed by CIBERSORT. SPSS software was used to analyze the Spearman rank correlation between FRBs identified and infiltration-related immune cells, and p < 0.05 was considered as statistically significant.

RESULTS

In this study, potential genetic biomarkers associated with Ferroptosis in CTEPH were investigated and explored their role in immune infiltration. In total, we identified 17 differentially expressed Ferroptosis-associated genes by GEOquery package. The key FRBs including ARRDC3, HMOX1, BRD4, and YWHAE were screened using Lasso and SVM-RFE machine learning methods.Through gene set GSE188938 verification, only upregulation of gene ARRDC3 showed statistical difference. In addition, immune infiltration analysis using the CIBERSORT algorithm revealed the infiltration of Eosinophils and Neutrophils in CTEPH samples was less than that in the control group. And correlation analysis revealed that ARRDC3 was positively correlated with T cells follicular helper (r = 0.554, p = 0.017) and negatively correlated with Neutrophils (r = -0.47, p = 0.049).

CONCLUSIONS

In conclusion, ARRDC3 upregulation with different immune cell infiltration were involved in the development of CTEPH. ARRDC3 might a potential Ferroptosis-related biomarker for CTEPH treatment. This study provided a new insight into pathogenesis CTEPH.

Differential immunotoxicity effects of triclosan and triclocarban on larval zebrafish based on RNA-Seq and bioinformatics analysis

Herein, we demonstrated that sublethal-dose exposure to triclosan (TCS) and triclocarban (TCC) triggered larval zebrafish immunotoxicity. Acute exposure to TCS induced significant increases in larval neutrophils and macrophages and a prominent decrease in thymic T cells. In contrast, three kinds of cells (neutrophils, macrophages, and thymic T cells) were significantly reduced under TCC exposure, suggesting that both TCS and TCC suppress thymus development and mature T-cell differentiation. TCC was confirmed to have more severe immunotoxicity than TCS. Using Illumina RNA-Seq, 581 and 738 differentially expressed genes (DEGs) were identified in the TCS and TCC treatments, respectively. GO function and KEGG pathway enrichment analyses revealed that the DEGs were not identical in terms of biological processes, cellular components and molecular functions, but were primarily involved in immune response. KEGG analysis showed that approximately 47% and 11% of DEGs were mainly enriched in the immune system of the TCC and TCS treatments, respectively. Protein-protein interaction (PPI) network analysis confirmed that the hub genes enriched in the immune-related pathways differed between TCS and TCC exposure. The hub genes were fynb, mapk12b, scarb1, pik3r2, prkg3, srfa, arhgef2, cldn15la, and cldn15lb in the TCS treatment, and plg, serping1, masp2, fgg, vtnb, mmp9, serpine1, il1b, sb:cb37 and stat3 in the TCC treatment. Molecular docking simulation demonstrated that both TCS and TCC were stably docked with their target hub genes, and that their target molecules for inducing immunotoxicity were different. The differential target molecules and action pathways induced by TCS and TCC exposure provide us with diagnostic targets and toxicological endpoints.

Identification of Timm13 protein translocase of the mitochondrial inner membrane as a potential mediator of liver fibrosis based on bioinformatics and experimental verification

OBJECTIVE

To explore the association between translocase of the inner mitochondrial membrane 13 (Timm13) and liver fibrosis.

METHODS

Gene expression profiles of GSE167033 were collected from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between liver disease and normal samples were analyzed using GEO2R. Gene Ontology and Enrichment function were performed, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and the hub genes of the PPI network were calculated by MCODE plug-in in Cytoscape. We validated the transcriptional and post-transcriptional expression levels of the top correlated genes using fibrotic animal and cell models. A cell transfection experiment was conducted to silence Timm13 and detect the expression of fibrosis genes and apoptosis genes.

RESULTS

21,722 genes were analyzed and 178 DEGs were identified by GEO2R analysis. The top 200 DEGs were selected and analyzed in STRING for PPI network analysis. Timm13 was one of the hub genes via the PPI network. We found that the mRNA levels of Timm13 in fibrotic liver tissue decreased (P < 0.05), and the mRNA and protein levels of Timm13 also decreased when hepatocytes were stimulated with transforming growth factor-β1. Silencing Timm13 significantly reduced the expression of profibrogenic genes and apoptosis related genes.

CONCLUSIONS

The results showed that Timm13 is closely related to liver fibrosis and silencing Timm13 significantly reduced the expression of profibrogenic genes and apoptosis related genes, which will provide novel ideas and targets for the clinical diagnosis and treatment of liver fibrosis.

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

Background

Although 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.

Methods

Gene 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.

Results

In 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.

Conclusions

Our study revealed the pathogenesis of SAH complicated with epilepsy and identified hub genes that might provide new ideas for further mechanistic studies.

Identification of RNA N6-methyladenosine regulation in epilepsy: Significance of the cell death mode, glycometabolism, and drug reactivity

Epilepsy, a functional disease caused by abnormal discharge of neurons, has attracted the attention of neurologists due to its complex characteristics. N6-methyladenosine (m6A) is a reversible mRNA modification that plays essential role in various biological processes. Nevertheless, no previous study has systematically evaluated the role of m6A regulators in epilepsy. Here, using gene expression screening in the Gene Expression Omnibus GSE143272, we identified seven significant m6A regulator genes in epileptic and non-epileptic patients. The random forest (RF) model was applied to the screening, and seven m6A regulators (HNRNPC, WATP, RBM15, YTHDC1, YTHDC2, CBLL1, and RBMX) were selected as the candidate genes for predicting the risk of epilepsy. A nomogram model was then established based on the seven-candidate m6A regulators. Decision curve analysis preliminarily showed that patients with epilepsy could benefit from the nomogram model. The consensus clustering method was performed to divide patients with epilepsy into two m6A patterns (clusterA and clusterB) based on the selected significant m6A regulators. Principal component analysis algorithms were constructed to calculate the m6A score for each sample to quantify the m6A patterns. Patients in clusterB had higher m6A scores than those in clusterA. Furthermore, the patients in each cluster had unique immune cell components and different cell death patterns. Meanwhile, based on the M6A classification, a correlation between epilepsy and glucose metabolism was laterally verified. In conclusion, the m6A regulation pattern plays a vital role in the pathogenesis of epilepsy. The research on m6A regulatory factors will play a key role in guiding the immune-related treatment, drug selection, and identification of metabolism conditions and mechanisms of epilepsy in the future.

A novel 4 immune-related genes as diagnostic markers and correlated with immune infiltrates in major depressive disorder

Background

Immune response is prevalently related with major depressive disorder (MDD) pathophysiology. However, the study on the relationship between immune-related genes (IRGs) and immune infiltrates of MDD remains scarce.

Methods

We extracted expression data of 148 MDD patients from 2 cohorts, and systematically characterized differentially expressed IRGs by using limma package in R software. Then, the LASSO and multivariate logistic regression analysis was used to identify the most powerful IRGs. Next, we analyzed the relationship between IRGs and immune infiltrates of MDD. Finally, GSE76826 was used to to verificate of IRGs as a diagnostic markers in MDD.

Results

203 different IRGs s in MDD has been identified (P < 0.05). GSEA revealed that the different IRGs was more likely to be enriched in immune-specific pathways. Then, a 9 IRGs was successfully established to predict MDD based on LASSO. Next, 4 IRGs was obtained by multivariate logistic regression analysis, and AUC for CD1C, SPP1, CD3D, CAMKK2, and IRGs model was 0.733, 0.767, 0.816, 0.800, and 0.861, suggesting that they have a good diagnostic performance. Furthermore, the proportion of T cells CD8, T cells γδ, macrophages M0, and NK cells resting in MDD group was lower than that in the healthy controls, suggesting that the immune system in MDD group is impaired. Simultaneously, CD3D was validated a reliable marker in MDD, and was positively correlated with T cells CD8. GSEA revealed high expression CD3D was more likely to be enriched in immune-specific pathways, and low expression CD3D was more likely to be enriched in glucose metabolism metabolism-specific pathways.

Conclusions

We applied bioinformatics approaches to suggest that a 4 IRGs could serve as diagnostic markers to provide a novel direction to explore the pathogenesis of MDD.