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 Identification of Crucial Methylation Genes Associated With Immune Cell Infiltration in Twin-to-twin Transfusion Syndrome Using Co-methylation Analysis

2021 ◽  
Author(s):  
Chuang Li ◽  
Yuan Wang ◽  
Caixia Liu ◽  
Shaowei Yin
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Interaction Network ◽  
Cell Infiltration ◽  
Future Research ◽  
Regulation Mechanism ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Neurodevelopmental Abnormalities

Abstract Background: DNA methylation (DNAm), is an important transcriptional regulation mechanism, relevant to various diseases. Twin-to-twin transfusion syndrome (TTTS) is a complication in twin pregnancies resulting from disproportionate blood circulation. Survivors of TTTS show a high risk of neurodevelopmental abnormalities, particularly in the hippocampus, which is important in learning and memory. Here, we investigate gene expression and DNAm in hippocampus tissues of TTTS specimens. Methods: DNAm and gene expression levels were compared among the three groups: 10 recipients, 10 donors, and 10 matched control, using methylation microarray. We further explored the immune infiltration of six immune cell sub-populations using EpiDISH analysis. The methylated sites related to immune cell infiltration were identified using the WGCNA package. We explored the core methylation genes in the protein-protein interaction network using the MCODE plugin in Cytoscape software. Results: There were 188 differential methylation sites among three groups. Based on WGCNA, we found that the turquoise module containing 174 CpG sites is significantly related to the immune infiltration level. And four hub genes correlated with immune infiltration level, namely, PTPRJ, FYN, LYN, and AKT1, and were identified using gene sub-network analysis. Conclusions: We identify the four hub methylation genes related to immune infiltration in the TTTS. The molecular function of hub genes is still explored in the future research.

scholarly journals Identification of new biomarkers and pathways associated with treatment and prognosis in melanoma patients

2020 ◽  
Author(s):  
Biao Huang ◽  
Wei Han ◽  
Zu-Feng Sheng ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Prognostic Value ◽  
Immune Cell ◽  
Gene Interaction ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Strong Positive Correlation ◽  
Hub Genes

Abstract Background Skin cutaneous melanoma (SKCM) is known as the most malignancy and treatment-resistant in human tumor, causing about 72% of deaths in skin carcinoma. However, the potential mechanism and new effective targets remain to be further elucidated. Available datasets such as Gene Expression Omnibus (GEO) can be utilized to search for novel therapeutic targets and prognostic biomarkers. Methods Three data sets were downloaded from GEO database . The differentially expressed genes (DEGs) were identified via Venn software. Protein‐protein interaction network of DEGs was developed and the module hub genes analysis was constructed by Cytoscape. Subsequently, multiple online tools and Kaplan-Meier survival curves were analyzed to detect underlying signaling pathways, gene expression, drug-gene interaction and prognostic value of hub genes. In addition, we explored the correlation between hub genes and immune cell infiltration. At last, the related miRNA, lncRNA networks were constructed by R software. Results A total of 308 DEGs and 12 hub genes were identified. Function and pathway enrichment results demonstrated a correlation between DEGs and the tumor microenvironment, immune response and melanoma tumorigenesis. Subsequently, we focused on assessing potential value of 12 hub genes. Seven hub genes ( CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 ) were identified with significant overall survival for prognosis. What’s more, five of these seven hub genes were found to be related to clinical stages (P values<0.05). In addition, the most important pathways of hub genes include interleukin-10 signaling, peptide ligand-binding receptors, which play important roles in tumor microenvironment for immune activation or immunosuppressive by regulating the infiltration of immune cells. Our results revealed a strong positive correlation between gene expression (CCL4, CCL5, CXCL9, CXCL10 and CXCL13) and immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells). Interestingly, 8 of 12 hub genes (CXCL10, CCL4, CCL5, IL6, CXCL2, PTGER3, GAL, NPY1R) were also found in the predicted drug-gene interaction. The related miRNA, lncRNA for diagnosis and prognosis were found in networks. Conclusion In conclusion, CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 were of high prognostic value and may be potential targets for the diagnosis and therapy of patients with melanoma.

scholarly journals Analyzing the Impact of ATF3 in Tumorigenesis and Immune Cell Infiltration of Ovarian Tumor: A Bioinformatics Study

2021 ◽  
Author(s):  
Xiujuan wu
Keyword(s):  
Ovarian Tumor ◽  
Immune Cell ◽  
Interaction Network ◽  
Critical Factor ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Transcription Activator ◽  
Critical Pathways ◽  
The Impact

Abstract ATF3 is an essential transcription activator in regulating cancer-related genetic expression. To identify the role of ATF3 in ovarian, we investigated the correlation between ATF3 expression and the clinicopathological properties using multiple database. The cBioPortal and GEPIA database displayed the clinical information of ovarian patients harboring or without harboring ATF3 mutation. Furthermore, we assessed the relationship between survival and ATF3 expression level using Kaplan-Meier plotter, which reveals that the ovarian patients with higher expression of ATF3 suffered the worse overall survival and progression-free survival. The differentially expressed genes were analyzed using Gene Ontology, protein-protein interaction network and gene set enrichment analysis to identify the hub gene and critical pathways, significantly affecting the tumorigenesis of ovarian tumor. Finally, we assessed the correlation between ATF3 and immune cell infiltration using Tumor Immunoassay Resource (TIMER) database. The results demonstrated that higher expression is positive correlation with macrophage infiltration, expression for M1 and M2 type macrophages. Our study suggests that ATF3 can regulate the cell cycle and heme-related oxidative phosphorylation process, and it may be a critical factor to regulate the macrophage cell to be infiltrated into ovarian cancer. ATF3 can be as a biomarker for diagnosis and therapy of ovarian.

scholarly journals Identification and validation of hub genes of synovial tissue for patients with osteoarthritis and rheumatoid arthritis

2021 ◽  
Author(s):  
Yanzhi Ge ◽  
Zuxiang Chen ◽  
Yanbin Fu ◽  
Li Zhou ◽  
Haipeng Xu ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Hub Genes

Abstract Osteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with partially common phenotypes and genotypes. This study aimed to determine the mechanistic similarities and differences between osteoarthritis and rheumatoid arthritis by analyzing the differentially expressed genes and signaling pathways. Microarray data of osteoarthritis and rheumatoid arthritis were obtained from the Gene Expression Omnibus. By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified in synovial membrane samples from patients and healthy donations. Then, the Gene ontology significant functions annotation, Kyoto Encyclopedia of Genes and Genomes pathways and protein-protein interaction network analysis were conducted. Moreover, CIBERSORT was used to further distinguish OA and RA in immune infiltration. Finally, animal experimentation was conducted and the establishment of model, which was verified using PCR in the mouse. As an overlapping process, we identified 1116 DEGs between OA and RA. It was indicated that specific gene signatures differed significantly between OA and RA connected with the distinct pathways. Of identified DEGs, 9 immune cell types among 22 were identified to distinguish from each other. The qRT-PCR result showed that the eight-tenths expression levels of the hub genes were significantly increased in OA samples (P < 0.05). This large-scale gene expression study provided new insights for disease-associated genes and molecular mechanisms as well as their associated function in osteoarthritis and rheumatoid arthritis, which simultaneously offer a new direction for biomarker development and the distinguishment of gene-level mechanisms between osteoarthritis and rheumatoid arthritis.

scholarly journals Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

2021 ◽  
Vol 8 ◽  
Author(s):  
Yujia Yang ◽  
Yue Cai ◽  
Yuan Zhang ◽  
Xu Yi ◽  
Zhiqiang Xu
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Validation Dataset ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Significant Difference ◽  
Key Genes

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein–protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

scholarly journals Immune cell infiltration characteristics and related core genes of bioinformatic analysis in multiple sclerosis

2021 ◽  
Author(s):  
jingyu zhao ◽  
Jianyong Zheng ◽  
Qun Wang ◽  
Qian Li ◽  
Nan zhang
Keyword(s):  
Multiple Sclerosis ◽  
Immune Cells ◽  
Immune Cell ◽  
Clinical Information ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Systematic Understanding ◽  
Core Genes

Abstract Background Introduction Multiple sclerosis(MS) is a common complication of uncontrolled or excessive neuroinflammation and autoimmunity disease. Advances in high-throughput technologies and available bioinformatics tools make it possible to evaluate different expressions in the whole genome instead of focusing on a limited number of genes. MethodsMaterials and methods Two public available databases GSE81279 and GSE21942 of multiple sclerosis samples were downloaded analyzed by CIBERSORT. Gene Ontology (GO) and KEGG pathway analysis based on GSEA was performed by cluster profile software to reveal the regulatory relations among genes and provided a systematic understanding of the functional differentially expressed genes at the transcriptional level.GSE81279 was used to validate the association between core genes and clinical information. ResultsFor immune cells, T-cell gamma delta and monocyte showed a trend toward reduction. The connection between the most prominent GO terms showed HBB, GATA2, NAA35, TCL1A, SECISBP2L, CLC, AGPAT5, CCR3, LTF, MALAT1, MS4A3 were significantly differentially expressed in MS. Gene set enrichment result was presented CDKN1A, DDB2, MME HMGN1, XPC, RELA for subsequent analysis.GSE81279 showed five types of immune cells revealed important links with MS. GSEA and layered KEGG analyses revealed that enrichment of immune response-related in primary immunodeficiency, it also consistent with previous studies. We got 10 genes, including HLA-DR, IL7R, HBB, TNFRSF1A, CYP27B1, NR1H3, IL2RA, TNFR1, BAFF, and CYP2R1 had close connections to clinical features. ConclusionsOur study identifies immune cell infiltration with microarray data of the plasma in MS by using CIBERSORT analysis, we also provide novel information for further study of genes of multiple sclerosis.

scholarly journals Identification of VCAM1 as a Novel Biomarker in the Immune Microenvironment of Gastric Cancer by Comprehensive Bioinformatics Analysis

2022 ◽  
Author(s):  
Xin Jiang ◽  
Di Chen ◽  
Mengmeng Wang ◽  
Yushuang Xu ◽  
Mengjun Qiu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Immune Cell ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
High Morbidity ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Analysis

Abstract Background and Purpose Gastric cancer (GC) is a common malignant tumor of the digestive tract worldwide and has high morbidity and mortality. The tumor immune microenvironment (TIME), especially the immune cell infiltration, plays an important role in the progression and prognosis of GC. In this study, we investigated the TIME-related genes and explored their role in the GC immune microenvironment. Method We used ssGSEA to assess the immune cell infiltration in 375 patients with GC downloaded from TCGA. Then GC samples were divided into high-, medium-, and low-immune cell infiltration groups by hierarchical clustering. Differentially expressed genes analysis were further proceed between groups to determine TIME-related differentially expressed genes (DEGs). By protein interaction network and Cox analysis, the angiogenesis gene was intersected. The results showed that vascular cell adhesion molecular 1 (VCAM1) was the most critical gene. We further analyze the importance of VCAM1 in the progression of GC and its role in the GC microenvironment. Results We identified 463 TIME-associated DEGs and found that VCAM1 was involved in development and prognosis of GC. Further analysis revealed that VCAM1 was involved in the regulation of immune, vascular, and metastasis-related signaling pathways. Immuno-correlation analysis showed that VCAM1 expression was associated with various immune infiltrating cells, including macrophages and T cells. In addition, combined with online database prediction analysis, we speculated that VCAM1 expression in GC could be enhanced by AC104211.1 sponge Has-mir-183-5p. Conclusion VCAM1 may be involved in the regulation of immune state and angiogenesis in the TIME in GC. This protein could be a promising therapeutic target and prognostic biomarker for GC.

scholarly journals Integrated bioinformatics analysis of differentially expressed genes and immune cell infiltration characteristics in Esophageal Squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zitong Feng ◽  
Jingge Qu ◽  
Xiao Liu ◽  
Jinghui Liang ◽  
Yongmeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes

AbstractEsophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The role of molecular alterations and the immune microenvironment in ESCC development has not been fully elucidated. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. Nine gene expression datasets from the Gene Expression Omnibus (GEO) database were analysed to identify robust differentially expressed genes (DEGs) using the robust rank aggregation (RRA) algorithm. Functional enrichment analyses showed that the 152 robust DEGs are involved in multiple processes in the tumor microenvironment (TME). Immune cell infiltration analysis based on the 9 normalized GEO microarray datasets was conducted with the CIBERSORT algorithm. The changes in macrophages between ESCC and normal tissues were particularly obvious. In ESCC tissues, M0 and M1 macrophages were increased dramatically, while M2 macrophages were decreased. A robust DEG-based protein–protein interaction (PPI) network was used for hub gene selection with the CytoHubba plugin in Cytoscape. Nine hub genes (CDA, CXCL1, IGFBP3, MMP3, MMP11, PLAU, SERPINE1, SPP1 and VCAN) had high diagnostic efficiency for ESCC according to receiver operating characteristic (ROC) curve analysis. The expression of all hub genes except MMP3 and PLAU was significantly related to macrophage infiltration. Univariate and multivariate regression analyses showed that a 7-gene signature constructed from the robust DEGs was useful for predicting ESCC prognosis. Our results might facilitate the exploration of potential targeted TME therapies and prognostic evaluation in ESCC.

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