Identification and Validation of Immune-Related Genes and Immune Cell Infiltration in Preeclampsia

2021 ◽  
Author(s):  
Rongxin Chen ◽  
Qing Han ◽  
Huale Zhang ◽  
Jianying Yan
Keyword(s):  
Immune Cell ◽  
Risk Model ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Receptor Interaction ◽  
Immune Cell Infiltration ◽  
Multisystem Disease ◽  
Predicting Model ◽  
Immune Related Genes ◽  
Maternal Fetal Interface

Abstract Background Preeclampsia (PE) is a complex multisystem disease and its etiology remains unclear. The aim of this study was to identify potential immune-related diagnostic genes for PE, analyze the role of immune cell infiltration in PE, and explore the mechanism underlying PE-induced disruption of immune tolerance at the maternal-fetal interface. Methods We used the PE dataset GES25906 from Gene Expression Omnibus and immune-related genes from ImmPort database. The differentially expressed genes (DEGs) were identified using the “limma” package, and the differentially expressed immune-related genes (DEIGs) were extracted from the DEGs and immune-related genes using Venn diagrams. The potential functions of DEIGs were determined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Furthermore, the protein–protein interaction network was obtained from the STRING database, and it was visualized using Cytoscape software. Least absolute shrinkage and selection operator logistic regression was used to verify the diagnostic markers of PE and build a predicting model. The model was validated using datasets GSE66273 and GSE75010. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in PE tissues. Results Six genes (ACTG1, ENG, IFNGR1, ITGB2, NOD1, and SPP1) enriched in Th17 cell differentiation, cytokine-cytokine receptor interaction, innate immune response, and positive regulation of MAPK cascade pathways were identified, and a predicting model was built. Datasets GSE66273 and GSE75010 were used to validate the model, and the area under the curve was 0.8333 and 0.8107, respectively. Immune cell infiltration analysis revealed an increase in plasma cells and gamma delta T cells and a decrease in resting natural killer cells in the high score group according to the predictive model risk values. Conclusions We developed a risk model to predict PE and proved that immune imbalance at the maternal-fetal interface plays a key role in the pathogenesis of PE.

scholarly journals Screening of osteoarthritis diagnostic markers based on immune-related genes and immune infiltration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wen-Hua Yuan ◽  
Qi-Qi Xie ◽  
Ke-Ping Wang ◽  
Wei Shen ◽  
Xiao-Fei Feng ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Immune Cell ◽  
Diagnostic Markers ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Support Vector ◽  
Immune Cell Infiltration ◽  
M1 Macrophage ◽  
Immune Related Genes

AbstractOsteoarthritis (OA) is a chronic degenerative disease of the bone and joints. Immune-related genes and immune cell infiltration are important in OA development. We analyzed immune-related genes and immune infiltrates to identify OA diagnostic markers. The datasets GSE51588, GSE55235, GSE55457, GSE82107, and GSE114007 were downloaded from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and differentially expressed immune-related genes (DEIRGs), and functional correlation analysis was conducted. Second, CIBERSORT was used to evaluate infiltration of immune cells in OA tissue. Finally, the least absolute shrinkage and selection operator logistic regression algorithm and support vector machine-recurrent feature elimination algorithm were used to screen and verify diagnostic markers of OA. A total of 711 DEGs and 270 DEIRGs were identified in this study. Functional enrichment analysis showed that the DEGs and DEIRGs are closely related to cellular calcium ion homeostasis, ion channel complexes, chemokine signaling pathways, and JAK-STAT signaling pathways. Differential analysis of immune cell infiltration showed that M1 macrophage infiltration was increased but that mast cell and neutrophil infiltration were decreased in OA samples. The machine learning algorithm cross-identified 15 biomarkers (BTC, PSMD8, TLR3, IL7, APOD, CIITA, IFIH1, CDC42, FGF9, TNFAIP3, CX3CR1, ERAP2, SEMA3D, MPO, and plasma cells). According to pass validation, all 15 biomarkers had high diagnostic efficacy (AUC > 0.7), and the diagnostic efficiency was higher when the 15 biomarkers were fitted into one variable (AUC = 0.758). We developed 15 biomarkers for OA diagnosis. The findings provide a new understanding of the molecular mechanism of OA from the perspective of immunology.

scholarly journals Development of an Immune-Related Gene Signature for Prognosis in Melanoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jia-An Zhang ◽  
Xu-Yue Zhou ◽  
Dan Huang ◽  
Chao Luan ◽  
Heng Gu ◽  
...  
Keyword(s):  
T Cells ◽  
Immune Cell ◽  
Treatment Method ◽  
Gene Signature ◽  
Cell Infiltration ◽  
Venn Diagram ◽  
Immune Cell Infiltration ◽  
Tumor Purity ◽  
New Treatment ◽  
Immune Related Genes

Melanoma remains a potentially deadly malignant tumor. The incidence of melanoma continues to rise. Immunotherapy has become a new treatment method and is widely used in a variety of tumors. Original melanoma data were downloaded from TCGA. ssGSEA was performed to classify them. GSVA software and the "hclust" package were used to analyze the data. The ESTIMATE algorithm screened DEGs. The edgeR package and Venn diagram identified valid immune-related genes. Univariate, LASSO and multivariate analyses were used to explore the hub genes. The "rms" package established the nomogram and calibrated the curve. Immune infiltration data were obtained from the TIMER database. Compared with that of samples in the high immune cell infiltration cluster, we found that the tumor purity of samples in the low immune cell infiltration cluster was higher. The immune score, ESTIMATE score and stromal score in the low immune cell infiltration cluster were lower. In the high immune cell infiltration cluster, the immune components were more abundant, while the tumor purity was lower. The expression levels of TIGIT, PDCD1, LAG3, HAVCR2, CTLA4 and the HLA family were also higher in the high immune cell infiltration cluster. Survival analysis showed that patients in the high immune cell infiltration cluster had shorter OS than patients in the low immune cell infiltration cluster. IGHV1-18, CXCL11, LTF, and HLA-DQB1 were identified as immune cell infiltration-related DEGs. The prognosis of melanoma was significantly negatively correlated with the infiltration of CD4+ T cells, CD8+ T cells, dendritic cells, neutrophils and macrophages. In this study, we identified immune-related melanoma core genes and relevant immune cell subtypes, which may be used in targeted therapy and immunotherapy of melanoma.

scholarly journals Integrated Transcriptional Profiling Analysis and Immune-Related Risk Model Construction for Intracranial Aneurysm Rupture

2021 ◽  
Vol 15 ◽  
Author(s):  
Dezhi Shan ◽  
Xing Guo ◽  
Guozheng Yang ◽  
Zheng He ◽  
Rongrong Zhao ◽  
...  
Keyword(s):  
Immune Response ◽  
Inflammatory Response ◽  
Signaling Pathway ◽  
Immune Cell ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Cell Infiltration ◽  
Enzyme Linked Immunosorbent Assay ◽  
Immune Cell Infiltration ◽  
Related Risk

Intracranial aneurysms (IAs) may cause lethal subarachnoid hemorrhage upon rupture, but the molecular mechanisms are poorly understood. The aims of this study were to analyze the transcriptional profiles to explore the functions and regulatory networks of differentially expressed genes (DEGs) in IA rupture by bioinformatics methods and to identify the underlying mechanisms. In this study, 1,471 DEGs were obtained, of which 619 were upregulated and 852 were downregulated. Gene enrichment analysis showed that the DEGs were mainly enriched in the inflammatory response, immune response, neutrophil chemotaxis, and macrophage differentiation. Related pathways include the regulation of actin cytoskeleton, leukocyte transendothelial migration, nuclear factor κB signaling pathway, Toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, and chemokine signaling pathway. The enrichment analysis of 20 hub genes, subnetworks, and significant enrichment modules of weighted gene coexpression network analysis showed that the inflammatory response and immune response had a causal relationship with the rupture of unruptured IAs (UIAs). Next, the CIBERSORT method was used to analyze immune cell infiltration into ruptured IAs (RIAs) and UIAs. Macrophage infiltration into RIAs increased significantly compared with that into UIAs. The result of principal component analysis revealed that there was a difference between RIAs and UIAs in immune cell infiltration. A 4-gene immune-related risk model for IA rupture (IRMIR), containing CXCR4, CXCL3, CX3CL1, and CXCL16, was established using the glmnet package in R software. The receiver operating characteristic value revealed that the model represented an excellent clinical situation for potential application. Enzyme-linked immunosorbent assay was performed and showed that the concentrations of CXCR4 and CXCL3 in serum from RIA patients were significantly higher than those in serum from UIA patients. Finally, a competing endogenous RNA network was constructed to provide a potential explanation for the mechanism of immune cell infiltration into IAs. Our findings highlighted the importance of immune cell infiltration into RIAs, providing a direction for further research.

scholarly journals Identification of Potential Therapeutic Targets and Molecular Regulatory Mechanisms for Osteoporosis by Bioinformatics Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Yunlong Yang ◽  
Dechun Geng ◽  
Yonghua Wu
Keyword(s):  
Immune Cell ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Differential Analysis ◽  
Ppi Network ◽  
The Difference ◽  
And Control

Background. Osteoporosis is characterized by low bone mass, deterioration of bone tissue structure, and susceptibility to fracture. New and more suitable therapeutic targets need to be discovered. Methods. We collected osteoporosis-related datasets (GSE56815, GSE99624, and GSE63446). The methylation markers were obtained by differential analysis. Degree, DMNC, MCC, and MNC plug-ins were used to screen the important methylation markers in PPI network, then enrichment analysis was performed. ROC curve was used to evaluate the diagnostic effect of osteoporosis. In addition, we evaluated the difference in immune cell infiltration between osteoporotic patients and control by ssGSEA. Finally, differential miRNAs in osteoporosis were used to predict the regulators of key methylation markers. Results. A total of 2351 differentially expressed genes and 5246 differentially methylated positions were obtained between osteoporotic patients and controls. We identified 19 methylation markers by PPI network. They were mainly involved in biological functions and signaling pathways such as apoptosis and immune inflammation. HIST1H3G, MAP3K5, NOP2, OXA1L, and ZFPM2 with higher AUC values were considered key methylation markers. There were significant differences in immune cell infiltration between osteoporotic patients and controls, especially dendritic cells and natural killer cells. The correlation between MAP3K5 and immune cells was high, and its differential expression was also validated by other two datasets. In addition, NOP2 was predicted to be regulated by differentially expressed hsa-miR-3130-5p. Conclusion. Our efforts aim to provide new methylation markers as therapeutic targets for osteoporosis to better treat osteoporosis in the future.

scholarly journals Relevance of STK11 Mutations Regarding Immune Cell Infiltration, Drug Sensitivity, and Cellular Processes in Lung Adenocarcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Zhenqing Li ◽  
Bo Ding ◽  
Jianxun Xu ◽  
Kai Mao ◽  
Pengfei Zhang ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Drug Sensitivity ◽  
Immune Cell ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Wild Type ◽  
Serine Threonine Kinase ◽  
Threonine Kinase ◽  
Cellular Processes

Serine/threonine kinase 11 (STK11) is one member of the serine/threonine kinase family, which is involved in regulating cell polarity, apoptosis, and DNA damage repair. In lung adenocarcinoma (LUAD), it can play as one tumor suppressor and always be mutated. In this study, we aimed to assess the relevance of STK11 mutations in LUAD, in which we also studied the correlation among immune cell infiltration, drug sensitivity, and cellular processes. By performing the bioinformatics analysis of the Cancer Genome Atlas (TCGA) about LUAD patients, we found that the mutation efficiency of STK11 mutations is about 19%. Additionally, the differentially expressed gene analysis showed that there were 746 differentially expressed genes (DEGs) between LUAD patients with and without STK11 mutations. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis showed that the DEGs were enriched in various tumorigenesis signaling pathways and metabolic processes. Among these DEGs, the top ranking 21 genes were found that they were more frequently mutated in the STK11 mutation group than in the wild-type group (p-value<0.01). Finally, the LUAD patients with STK11 mutations suffered the worse immune cell infiltration levels than the LUAD patients with wild-type. The STK11 gene copy number was correlated with immune cell infiltration. Aiming to develop the therapeutic drugs, we performed Genomics of Drug Sensitivity in Cancer (GDSC) data to identify the potential therapeutic candidate and the results showed that Nutlin-3a(-) may be a sensitive drug for LUAD cases harboring STK11 mutations. The specific genes and pathways shown to be associated with LUAD cases involving STK11 mutations may serve as targets for individualized LUAD treatment.

Profiles of immune‐related genes and immune cell infiltration in the tumor microenvironment of diffuse lower‐grade gliomas

2020 ◽  
Vol 235 (10) ◽  
pp. 7321-7331 ◽  
Author(s):  
Xiangyang Deng ◽  
Dongdong Lin ◽  
Xiaojia Zhang ◽  
Xuchao Shen ◽  
Zelin Yang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Lower Grade ◽  
Immune Cell Infiltration ◽  
Immune Related Genes

scholarly journals Identification of Methylated Differentially Expressed Hub Genes and Immune Infiltration in Diabetic Retinopathy

2021 ◽  
Author(s):  
Qi Zhou ◽  
Xin Xiong ◽  
Min Tang ◽  
Yingqing Lei ◽  
Hongbin Lv
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Immune Cell ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Critical Pathways

Abstract BackgroundDiabetic retinopathy (DR), a severe complication of diabetes mellitus (DM), is a global social and economic burden. However, the pathological mechanisms mediating DR are not well-understood. This study aimed to identify differentially methylated and differentially expressed hub genes (DMGs and DEGs, respectively) and associated signaling pathways, and to evaluate immune cell infiltration involved in DR. MethodsTwo publicly available datasets were downloaded from the Gene Expression Omnibus database. Transcriptome and epigenome microarray data and multi-component weighted gene coexpression network analysis (WGCNA) were utilized to determine hub genes within DR. One dataset was utilized to screen DEGs and to further explore their potential biological functions using functional annotation analysis. A protein-protein interaction network was constructed. Gene set enrichment and variation analyses (GSVA and GSEA, respectively) were utilized to identify the potential mechanisms mediating the function of hub genes in DR. Infiltrating immune cells were evaluated in one dataset using CIBERSORT. The Connectivity Map (CMap) database was used to predict potential therapeutic agents. ResultsIn total, 673 DEGs (151 upregulated and 522 downregulated genes) were detected. Gene expression was significantly enriched in the extracellular matrix and sensory organ development, extracellular matrix organization, and glial cell differentiation pathways. Through WGCNA, one module was found to be significantly related with DR (r=0.34, P =0.002), and 979 hub genes were identified. By comparing DMGs, DEGs, and genes in WGCNA, we identified eight hub genes in DR ( AKAP13, BOC, ACSS1, ARNT2, TGFB2, LHFPL2, GFPT2, TNFRSF1A ), which were significantly enriched in critical pathways involving coagulation, angiogenesis, TGF-β, and TNF-α-NF-κB signaling via GSVA and GSEA. Immune cell infiltration analysis revealed that activated natural killer cells, M0 macrophages, resting mast cells, and CD8 + T cells may be involved in DR. ARNT2, TGFB2, LHFPL2 , and AKAP13 expression were correlated with immune cell processes, and ZG-10, JNK-9L, chromomycin-a3, and calyculin were identified as potential drugs against DR. Finally, TNFRSF1A , GFPT2 , and LHFPL2 expression levels were consistent with the bioinformatic analysis. ConclusionsOur results are informative with respect to correlations between differentially methylated and expressed hub genes and immune cell infiltration in DR, providing new insight towards DR drug development and treatment.

scholarly journals Relation between IDH1 status, histologic grade, immune-cell infiltration and expression of immune-related genes in patients with gliomas

2018 ◽  
Vol 29 ◽  
pp. x5
Author(s):  
S. Cabezas-Camarero ◽  
R. Pérez-Alfayate ◽  
I. Casado Fariñas ◽  
M. Sáiz-Pardo Sanz ◽  
I. Subhi-Issa ◽  
...  
Keyword(s):  
Immune Cell ◽  
Histologic Grade ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Related Genes
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