Immune-related Hub Genes Research Articles

Identification of immune-related hub genes in chronic obstructive pulmonary disease

Identification of immune-related hub genes in chronic obstructive pulmonary disease

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.

Construction and validation of an immune-related gene signature predictive of survival and response to immunotherapy for colorectal cancer.

Colorectal cancer is a common malignant tumor with the second incidence rate and the third mortality rate worldwide. In this study, we identified and validated an immune-related gene signature, explored the clinical and molecular characteristics of the signature-defined risk groups, and assessed its ability in predicting prognosis, immune cell infiltration and immunotherapy responses. The Cancer Genome Atlas database was used as the training set while GSE39582 database as the validation set. Immune-related hub genes were selected by the Least Absolute Shrinkage and Selection Operator-penalized Cox regression model, and the signature was then constructed by the selected genes and their relevant coefficients. Prognostic performance of the signature and the signature-base nomogram models were assessed by time-dependent receiver operating characteristic curves and calibration plots in both training and validation cohorts. Clinical and mutation-related data were downloaded and analyzed to explore their associations with signature-defined risk groups. Proportions of infiltrated immune cells was estimated via CIBERSORT algorithm and immunotherapy response was evaluated by immunophenoscore and tumor immune dysfunction and exclusion scores. Seven among 790 immune-related differentially-expressed genes were selected and use to construct the signature. The signature and signature-base nomograms showed promising prognostic performance in both training and validation cohorts. Signature-defined high-risk group was associated with advanced disease, poor pathological prognostic factors and less active immune infiltration microenvironment. Besides, the response to immunotherapy of high-risk group was predicted to be poorer by immunophenoscore and tumor immune dysfunction and exclusion scores. Our signature proved its efficacy in predicting prognosis, tumor immune microenvironment and responses to immunotherapy in colorectal cancer.

Identification of immune-related hub genes in spinal cord injury

ObjectivesImmune regulation is a pivotal factor in the pathogenesis and repair of spinal cord injury (SCI). This study aims to explore potential immune center genes associated with spinal cord injury.MethodsThe public data set GSE151371 was obtained from the GEO database. The R software package "limma" was used to identify differentially expressed genes (DEGs) in SCI. GO, KEGG and GSEA pathway analyses were performed using the DEGs. The key module genes related to spinal cord injury were selected through WGCNA analysis. Overlapping genes were extracted from WGCNA, DEGs, and immune-related genes. LASSO analysis was employed to identify central genes associated with SCI immunity. Pearson correlation analysis assessed the correlation between hub genes and immune cells in SCI. In addition, we further investigated the hub genes' expression, diagnostic potential, function, and targeted drugs.ResultsWe have identified three immunity-related hub genes (ABHD5, EDNRB, EDN3). Immune infiltration analysis showed that the hub gene was significantly associated with resting NK cells, M2 macrophages, and monocytes in the immune microenvironment of SCI. ROC analysis demonstrated that these hub genes have favorable diagnostic performance for SCI. Functional analysis revealed that ABHD5 is primarily associated with lipid metabolism pathways, while EDN3 and EDNRB are mainly involved in endothelin, downstream GPCR signaling, and ERK signaling transduction. In addition, we identified six potential targeted drugs based on our findings.ConclusionsABHD5, EDNRB, and EDN3 are involved in processes such as SCI progression or repair through immunomodulation and deserve further study.

Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors

BackgroundCurrently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.MethodsWe estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering. Differentially expressed immune genes between the patient subgroups were identified, and potential predictive biomarkers for ICIs were explored. Finally, the predictive value of the identified biomarkers was verified in patients with urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC) who received ICIs.ResultsOur study identified two subgroups of patients with distinct immune infiltrating phenotypes and differing clinical outcomes. The patient subgroup with improved outcomes displayed tumors enriched with genes related to immune response regulation and pathway activation. Furthermore, CCL5 and CSF2 were identified as immune-related hub-genes and were found to be prognostic in a pan-cancer setting. Importantly, UC and ESCC patients with high expression of CCL5 and low expression of CSF2 responded better to ICIs.ConclusionWe demonstrated CCL5 and CSF2 as potential novel biomarkers for predicting the response to ICIs in patients with UC and ESCC. The predictive value of these biomarkers in other cancer types warrants further evaluation in future studies.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes.

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

Identification of key immune-related genes and potential therapeutic drugs in diabetic nephropathy based on machine learning algorithms

BackgroundDiabetic nephropathy (DN) is a major contributor to chronic kidney disease. This study aims to identify immune biomarkers and potential therapeutic drugs in DN.MethodsWe analyzed two DN microarray datasets (GSE96804 and GSE30528) for differentially expressed genes (DEGs) using the Limma package, overlapping them with immune-related genes from ImmPort and InnateDB. LASSO regression, SVM-RFE, and random forest analysis identified four hub genes (EGF, PLTP, RGS2, PTGDS) as proficient predictors of DN. The model achieved an AUC of 0.995 and was validated on GSE142025. Single-cell RNA data (GSE183276) revealed increased hub gene expression in epithelial cells. CIBERSORT analysis showed differences in immune cell proportions between DN patients and controls, with the hub genes correlating positively with neutrophil infiltration. Molecular docking identified potential drugs: cysteamine, eltrombopag, and DMSO. And qPCR and western blot assays were used to confirm the expressions of the four hub genes.ResultsAnalysis found 95 and 88 distinctively expressed immune genes in the two DN datasets, with 14 consistently differentially expressed immune-related genes. After machine learning algorithms, EGF, PLTP, RGS2, PTGDS were identified as the immune-related hub genes associated with DN. In addition, the mRNA and protein levels of them were obviously elevated in HK-2 cells treated with glucose for 24 h, as well as their mRNA expressions in kidney tissues of mice with DN.ConclusionThis study identified 4 hub immune-related genes (EGF, PLTP, RGS2, PTGDS), as well as their expression profiles and the correlation with immune cell infiltration in DN.

Integrated analysis reveals NLRC4 as a potential biomarker in sepsis pathogenesis.

Sepsis remains a significant global health burden and contributor to mortality, yet the precise molecular mechanisms underlying the immune response are not fully elucidated. To gain insight into this issue, we performed a comprehensive analysis using a variety of techniques including bulk RNA sequencing, single-cell RNA sequencing, and enzyme-linked immunosorbent assay (ELISA). We performed enrichment analysis of differentially expressed genes in sepsis and healthy individuals by utilizing Gene Ontology (GO) analysis and indicated significant enrichment of immune-related response. Following Weighted Gene Co-Expression Network Analysis (WGCNA) and protein-protein interaction analysis (PPI) were used to identify key immune-related hub genes and validated by ELISA to show that NLRC4 is highly expressed in sepsis. Additionally, an analysis of scRNA-seq data from newly diagnosed sepsis, sepsis diagnosis at 6 hours, and healthy samples demonstrates a significant increase in both the expression levels and proportions of NLRC4 in sepsis monocytes and neutrophils. In addition, using pySCENIC we identified upstream transcription factors that regulate NLRC4. Our study provides valuable insights into the identification of NLRC4 in peripheral blood as a potential candidate gene for the diagnosis and treatment of sepsis.

A novel immune-related gene signature for diagnosis and potential immunotherapy of microsatellite stable endometrial carcinoma

An immune-related gene signature (IRGS) was established to better understand the molecular and immunologic characteristics of microsatellite instable (MSI) and microsatellite stable (MSS) endometrial carcinoma (EC), and provide potential immunotherapy directions for MSS patients. Top 20 immune-related hub genes were screened by weight gene coexpression network analysis (WGCNA), and an IRGS was further established through Cox regression analysis. The molecular and immune characteristics were clarified in IRGS high and low risk groups. Expression and MS status validation of the IRGS were conducted through quantitative real-time Polymerase Chain Reaction (rt-qPCR) and immunohistochemistry (IHC) analysis. The IRGS includes 2 oncogenes (AGTR1 and HTR3C) and 2 tumor suppressor genes (CD3E and SERPIND1). Patients in IRGS high-risk group were more with MSS status, higher tumor grade, later FIGO stage, serous histology and elder ages compared with IRGS low-risk group (P < 0.05). Besides, patients in MSS group were more FIGO stages II–IV (42.7% vs. 26%), serous histology (35.7% vs. 5.3%) and with higher IRGS risk score (1.51 ± 3.11 vs. 1.02 ± 0.67) (P < 0.05) than patients in MSI group. Furthermore, patients in IRGS high-risk group had higher tumor purity, more Macrophages M1 and Macrophages M2 infiltrating, higher proportion of Macrophages M2 and Dendritic cells activated, lower proportion of T cells regulatory (Tregs), lower tumor mutation burden (TMB). Correspondingly, subjects in IRGS low-risk group had higher immunphenoscores than IRGS high-risk group. The relative mRNA level of AGTR1 and HTR3C were gradually increase, while CD3E and SERPIND1 were reversed in rt-qPCR. Through IHC experiments, AGTR1(69.2% vs 30%, P = 0.074) and HTR3C (76.9% vs 30%, P = 0.024) had higher positive staining rates in ECs than non-ECs. While SERPIND1 (84.6% vs 20%, P = 0.003) and CD3E (61.5% vs 40%, P = 0.000) had higher positive staining rates in non-ECs. IRGS is a potential diagnostic and prognostic biomarker for EC. IRGS low risk group might benefit from immune checkpoint inhibitors, while IRGS high risk group deserve other potential immunotherapy.

Immune-Related Molecules CD3G and FERMT3: Novel Biomarkers Associated with Sepsis.

Sepsis ranks among the most common health problems worldwide, characterized by organ dysfunction resulting from infection. Excessive inflammatory responses, cytokine storms, and immune-induced microthrombosis are pivotal factors influencing the progression of sepsis. Our objective was to identify novel immune-related hub genes for sepsis through bioinformatic analysis, subsequently validating their specificity and potential as diagnostic and prognostic biomarkers in an animal experiment involving a sepsis mice model. Gene expression profiles of healthy controls and patients with sepsis were obtained from the Gene Expression Omnibus (GEO) and analysis of differentially expressed genes (DEGs) was conducted. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to analyze genes within crucial modules. The functional annotated DEGs which related to the immune signal pathways were used for constructing protein-protein interaction (PPI) analysis. Following this, two hub genes, FERMT3 and CD3G, were identified through correlation analyses associated with sequential organ failure assessment (SOFA) scores. These two hub genes were associated with cell adhesion, migration, thrombosis, and T-cell activation. Furthermore, immune infiltration analysis was conducted to investigate the inflammation microenvironment influenced by the hub genes. The efficacy and specificity of the two hub genes were validated through a mice sepsis model study. Concurrently, we observed a significant negative correlation between the expression of CD3G and IL-1β and GRO/KC. These findings suggest that these two genes probably play important roles in the pathogenesis and progression of sepsis, presenting the potential to serve as more stable biomarkers for sepsis diagnosis and prognosis, deserving further study.

Association between immune-related hub genes CD36, CXCL13, FGFR4, GABBR1, LAMP3, MMP12, and PPM1H and colorectal cancer prognosis

Association between immune-related hub genes CD36, CXCL13, FGFR4, GABBR1, LAMP3, MMP12, and PPM1H and colorectal cancer prognosis

Identification of diagnostic immune-related gene biomarkers for predicting heart failure after acute myocardial infarction.

Post-myocardial infarction heart failure (HF) is a major public health concern. Previous studies have reported the critical role of immune response in HF pathogenesis. However, limited studies have reported predictive immune-associated biomarkers for HF. So we attempted to identify potential immune-related indicators for HF early diagnosis and therapy guidance. This study identified two potential immune-related hub genes (IRHGs), namely CXCR5 and FOS, using bioinformatic approaches. The expression levels of CXCR5 and FOS and their ability to predict long-term HF were analyzed. Functional enrichment analysis revealed that the hub genes were enriched in immune system processes, including the interleukin-17 and nuclear factor-kappa B signaling pathways, which are involved in the pathogenesis of HF. Quantitative real-time polymerase chain reaction revealed that the Fos mRNA levels, but not the Cxcr5 mRNA levels, were downregulated in the mice of the HF group. This study successfully identified two IRHGs that were significantly and differentially expressed in the HF group and could predict long-term HF, providing novel insights for future studies on HF and developing novel therapeutic targets for HF.

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.

Identification of immune-related hub genes contributing to the pathogenesis, diagnosis, and remission of ulcerative colitis by integrated bioinformatic analyses.

The inflammatory disease ulcerative colitis (UC) is multifaceted, immune-mediated, chronic, and relapsing, which is considered to be mainly driven by dysregulated mucosal immune response. The remission of the inflammatory response is a marker of mucosal healing, relating to the low risk of hospitalizations, colorectal cancer, and colectomy. In spite of this, it is still unclear what the key immunological mechanism is which contributes to UC. Here, we explored the immune mechanism and related key genes underlying the state of inflammation in UC. Co-expression networks were constructed based on the expression profiles of immune-related genes in GSE179285. Using Weighted Gene Co-expression Network Analysis and Protein-protein interactions analysis, common hub genes were identified in the module of interest. Then, screening of real hub genes, significantly differentially expressing in inflamed UC, was carried out by Differential Expression Genes Analysis of GSE75214, GSE53306, and GSE6731datasets and immunohistochemistry of clinical samples. The diagnosis Capacity of the hub gene was identified by "glm" function in R. The potential key immune-related mechanisms were investigated using functional enrichment analysis and gene set enrichment analysis (GSEA). Bioinformatics tools were used to predict potential upstream transcription factors (TF), including the UCSC genome browser, correlation analyses, and JASPAR browser. The analysis revealed the blue module, consisting of 227 immune-related genes, showed the highest correlation with inflamed UC. And then, forty-three common candidates were distinguished. S100A9 was identified within the key module as a real hub gene with good diagnostic performance. The immune genes in the blue module were markedly enriched in the Cytokine-Cytokine receptor interaction. S100A9 most likely gets involved NOD-like receptor (NLR) signaling pathway. SPI1 showed the strongest likelihood to be the regulator. S100A9 was identified as the real immune-related hub gene for inflamed UC. Both diagnosis and remission may be aided by its high expression in the inflamed UC.

Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation.

Membranous nephropathy (MN) is an autoimmune glomerular disease that is predominantly mediated by immune complex deposition and complement activation. The aim of this study was to identify key biomarkers of MN and investigate their association with immune-related mechanisms, inflammatory cytokines, chemokines and chemokine receptors (CCRs). MN cohort microarray expression data were downloaded from the GEO database. Differentially expressed genes (DEGs) in MN were identified, and hub genes were determined using a protein-protein interaction (PPI) network. The relationships between immune-related hub genes, immune cells, CCRs, and inflammatory cytokines were examined using immune infiltration analysis, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA). Finally, the immune-related hub genes in MN were validated using ELISA. In total, 501 DEGs were identified. Enrichment analysis revealed the involvement of immune- and cytokine-related pathways in MN progression. Using WGCNA and immune infiltration analysis, 2 immune-related hub genes (CYBB and CSF1R) were identified. These genes exhibited significant correlations with a wide range of immune cells and were found to participate in B cell/T cell receptor and chemokine signaling pathways. In addition, the expressions of 2 immune-related hub genes were positively correlated with the expression of CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2. Our study identified CSF1 and CYBB as immune-related hub genes that potentially influence the expression of CCRs and pro-inflammatory cytokines (CCR1, CX3CR1, IL1B, CCL4, TNF, and CCR2). CSF1 and CYBB may be potential biomarkers for MN progression, providing a perspective for diagnostic and immunotherapeutic targets of MN.

Identification of immune-related hub genes and construction of an immune-related gene prognostic index for low-grade glioma.

Low-grade glioma (LGG) poses significant management challenges and has a dismal prognosis. While immunotherapy has shown significant promise in cancer treatment, its progress in glioma has confronted with challenges. In our study, we aimed to develop an immune-related gene prognostic index (IRGPI) which could be used to evaluate the response and efficacy of LGG patients with immunotherapy. We included a total of 529 LGG samples from TCGA database and 1152 normal brain tissue samples from the GTEx database. Immune-related differentially expressed genes (DEGs) were screened. Then, we used weighted gene co-expression network analysis (WGCNA) to identify immune-related hub genes in LGG patients and performed Cox regression analysis to construct an IRGPI. The median IRGPI was used as the cut-off value to categorize LGG patients into IRGPI-high and low subgroups, and the molecular and immune mechanism in IRGPI-defined subgroups were analysed. Finally, we explored the relationship between IRGPI-defined subgroups and immunotherapy related indicators in patients after immunotherapy. Three genes (RHOA, NFKBIA and CCL3) were selected to construct the IRGPI. In a survival analysis using TCGA cohort as a training set, patients in the IRGPI-low subgroup had a better OS than those in IRGPI-high subgroup, consistent with the results in CGGA cohort. The comprehensive results showed that IRGPI-low subgroup had a more abundant activated immune cell population and lower TIDE score, higher MSI, higher TMB score, lower T cell dysfunction score, more likely benefit from ICIs therapy. IRGPI is a promising biomarker in the field of LGG ICIs therapy to distinguish the prognosis, the molecular and immunological characteristics of patients.

Identification of immune-related biomarkers in peripheral blood of schizophrenia using bioinformatic methods and machine learning algorithms.

Schizophrenia is a group of severe neurodevelopmental disorders. Identification of peripheral diagnostic biomarkers is an effective approach to improving diagnosis of schizophrenia. In this study, four datasets of schizophrenia patients' blood or serum samples were downloaded from the GEO database and merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WCGNA). The WGCNA analysis showed that the cyan module, among 9 modules, was significantly related to schizophrenia, which subsequently yielded 317 schizophrenia-related key genes by comparing with the DEGs. The enrichment analyses on these key genes indicated a strong correlation with immune-related processes. The CIBERSORT algorithm was adopted to analyze immune cell infiltration, which revealed differences in eosinophils, M0 macrophages, resting mast cells, and gamma delta T cells. Furthermore, by comparing with the immune genes obtained from online databases, 95 immune-related key genes for schizophrenia were screened out. Moreover, machine learning algorithms including Random Forest, LASSO, and SVM-RFE were used to further screen immune-related hub genes of schizophrenia. Finally, CLIC3 was found as an immune-related hub gene of schizophrenia by the three machine learning algorithms. A schizophrenia rat model was established to validate CLIC3 expression and found that CLIC3 levels were reduced in the model rat plasma and brains in a brain-regional dependent manner, but can be reversed by an antipsychotic drug risperidone. In conclusion, using various bioinformatic and biological methods, this study found an immune-related hub gene of schizophrenia - CLIC3 that might be a potential diagnostic biomarker and therapeutic target for schizophrenia.

Identification of immune-associated signatures and potential therapeutic targets for pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) comprises a heterogeneous group of diseases with diverse aetiologies. It is characterized by increased pulmonary arterial pressure and right ventricular (RV) failure without specific drugs for treatment. Emerging evidence suggests that inflammation and autoimmune disorders are common features across all PAH phenotypes. This provides a novel idea to explore the characteristics of immunological disorders in PAH and identify immune-related genes or biomarkers for specific anti-remodelling regimens. In this study, we integrated three gene expression profiles and performed Gene Ontology (GO) and KEGG pathway analysis. CIBERSORT was utilized to estimate the abundance of tissue-infiltrating immune cells in PAH. The PPI network and machine learning were constructed to identify immune-related hub genes and then evaluate the relationship between hub genes and differential immune cells using ImmucellAI. Additionally, we implemented molecular docking to screen potential small-molecule compounds based on the obtained genes. Our findings demonstrated the density and distribution of infiltrating CD4 T cells in PAH and identified four immune-related genes (ROCK2, ATHL1, HSP90AA1 and ACTR2) as potential targets. We also listed 20 promising molecules, including TDI01953, pemetrexed acid and radotinib, for PAH treatment. These results provide a promising avenue for further research into immunological disorders in PAH and potential novel therapeutic targets.

Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches.

Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein-protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker.

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.