The Landscape of Immune Cell Infiltration in the Glomerulus of Diabetic Nephropathy: Evidence Based on Bioinformatics

2021 ◽  
Author(s):  
Wei ZHOU ◽  
Yaoyu LIU ◽  
Qinghong HU ◽  
Jiuyao ZHOU ◽  
Hua LIN
Keyword(s):  
T Cells ◽  
Diabetic Nephropathy ◽  
Mast Cells ◽  
Protein Interactions ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration

Abstract BackgroundIncreasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy. MethodsInfiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein-protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method. Results9 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells CD4 naive, Macrophages M2, Dendritic cells resting, Mast cells resting, Mast cells activated; Down: NK cells resting, Monocytes, Neutrophils). Correlation analysis revealed that immune infiltration act as a complicated and tightly regulated network, among which T cells gamma delta and T cells CD4 naive show the most synergistic effect (r = 0.58, p ＜ 0.001); meanwhile, T cells CD8 and T cells CD4 memory resting show the most competitive effect (r = - 0.67, p ＜ 0.001). Several pathways related to immune were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (ALB, EGF, FOS, CXCR1, CXCR2, CCL2). ConclusionIn the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

scholarly journals Identification of Key Immune-Related Genes, Molecular Pathways and Immune Infiltration as Diagnostic and Therapeutic Candidate Targets for RA: an integrated bioinformatics-based analysis

2021 ◽  
Author(s):  
Sheng Fang ◽  
Xiao Fang ◽  
Xin Xu ◽  
Lin Zhong ◽  
An-quan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Mast Cells ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Systemic Autoimmune Disease ◽  
Molecular Pathways ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Relevance Rheumatoid arthritis (RA) is a systemic autoimmune disease with an aggressive, chronic synovial inflammation as the main pathological change. However, the specific etiology, pathogenesis, and related biomarkers in diagnosis and treatment are still not fully elucidated. This study attempts to provide new perspectives and insights into RA at the genetic, molecular, and cellular levels through the tenet of personalized medicine. Methods Gene expression profiles of four individual knee synovial tissues were downloaded from a comprehensive gene expression database, R language was used to screen for significantly differentially expressed genes (DEGs), Gene Ontology Enrichment Analysis, Kyoto Gene Encyclopedia, and Gene Set Enrichment Analysis were performed to analyze the biological functions and signaling pathways of these DEGs, STRING online database was used to establish protein-protein interaction networks, Cytoscape software to obtain ten hub genes, Goplot to get six inflammatory immune-related hub genes, and CIBERSORT algorithm to impute immune infiltration. Results Molecular pathways that play important roles in RA were obtained: Toll-like receptors, AMPK, MAPK, TNF, FoxO, TGF-beta, PI3K and NF-κB pathways, Ten hub genes: Ccr1, Ccr2, Ccr5, Ccr7, Cxcl5, Cxcl6, Cxcl13, Ccl13, Adcy2, and Pnoc. among which Adcy2 and Pnoc have not been reported in RA studies, suggesting that they may be worthy targets for further study. It was also found that among the synoviocytes in RA, the proportions of plasma cells, CD8 T cells, follicular helper T cells, monocytes, γ delta T cells, and M0 macrophages were higher, while the proportions of CD4 memory resting T cells, regulatory T cells (Tregs), activated NK cells, resting dendritic cells, M1 macrophages, eosinophils, activated mast cells, resting mast cells were lower in proportion, and each cell played an important role in RA. Conclusions This study may help understand the key genes, molecular pathways, the role of inflammatory immune infiltrating cells in RA’s pathogenesis and provide new targets and ideas for the diagnosis and personalized treatment of RA.

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 Down-Regulated CLDN10 Predicts Favorable Prognosis and Correlates With Immune Infiltration in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
XiongHui Rao ◽  
JianLong Jiang ◽  
ZhiHao Liang ◽  
JianBao Zhang ◽  
ZheHong Zhuang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cell ◽  
Immune Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Her2 Status ◽  
Immune Cell Infiltration ◽  
Cell Markers ◽  
Immune Infiltration

Background: CLDN10, an important component of the tight junctions of epithelial cells, plays a crucial role in a variety of tumors. The effect of CLDN10 expression in gastric cancer, however, has yet to be elucidated.Methods: Differential expression of CLDN10 at the mRNA and protein levels was evaluated using Oncomine, ULCAN, HPA and TIMER2.0 databases. Real-time polymerase chain reaction (RT-PCR) was utilized to further verify the expression of CLDN10 in vitro. Correlations between CLDN10 expression and clinical outcomes of gastric cancer were explored by Kaplan-Meier Plotter. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) were performed via LinkedOmics and GeneMANIA. The correlations between CLDN10 expression and immune cell infiltration and somatic copy number alternations (SCNA) in gastric cancer were explored by TIMER2.0 and GEPIA2.0.Results: CLDN10 expression was lower in gastric cancer compared to adjacent normal tissues, and associated with better prognosis. CLDN10 also showed significant differences at different T stages, Lauren classification, treatments and HER2 status. PPI and GSEA analysis showed that CLDN10 might be involved in signal transmission, transmembrane transport and metabolism. In some major immune cells, low expression of CLDN10 was associated with increased levels of immune cell infiltration. In addition, it was found that different SCNA status in CLDN10 might affect the level of immune cell infiltration. Furthermore, the expression of CLDN10 was significantly associated with the expression of several immune cell markers, especially B cell markers, follicular helper T cell (Tfh) markers and T cell exhaustion markers.Conclusion: Down-regulated CLDN10 was associated with better overall survival (OS) in gastric cancer. And CLDN10 may serve as a potential prognostic biomarker and correlate to immune infiltration levels in gastric cancer.

scholarly journals Immune Infiltration-Related Signature Predicts Risk Stratification and Immunotherapy Efficacy in Grade II and III Gliomas

2021 ◽  
Vol 9 ◽  
Author(s):  
Cong Luo ◽  
Zhixiong Liu ◽  
Wenrui Ye ◽  
Fangkun Liu
Keyword(s):  
T Cells ◽  
Risk Stratification ◽  
Cd4 T Cells ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Grade Ii ◽  
Genome Atlas

Background: Tumor microenvironment, especially infiltrating immune cell, is crucial for solid tumors including glioma. However, the hub genes as well as their effects on patient prognosis and immunotherapy efficacy remain obscure.Methods: We employed a total of 952 lower grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and 24 samples in our hospital for subsequent analyses. Abundances of immune infiltrates were evaluated using CIBERSORT and ImmuCellAI. Their correlations with prognosis were assessed by log-rank test. Immune infiltration-related hub genes were obtained from overlapped differential expressed genes (DEGs) in various subsets of survival-related immune cell types. The risk signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The functional analyses were estimated by GVSA and Gene Set Enrichment Analysis (GSEA) algorithms. And protein–protein interaction enrichment analysis was carried out with the Metascape database integrating STRING, BioGrid, OmniPath, and InWeb_IM.Results: Among the 21 infiltrates, the abundances of five immune infiltrates were correlated with overall survival (OS) in LGG patients. Higher abundances of naïve CD4+ T cells (p = 0.002), activated mast cells (p = 0.015), and monocytes (p = 0.014) were correlated with better prognosis, while higher abundances of resting memory CD4+ T cells (p = 0.015) and M1 macrophages (p = 0.020) correlated with poorer OS. We finally obtained 44 hub genes and constructed an immune infiltration-related signature (IIRS). The IIRS correlates with clinicopathological characteristics and exhibited potential power in predicting the immunotherapy efficacy. The IRRS correlates with cancer related pathways, especially “epithelial-mesenchymal transition (EMT),” and cytotoxic T lymphocytes.Conclusion: Our study constructed and validated a novel signature for risk stratification and prediction of immunotherapy response in grade II and III gliomas, which was closely associated with glioma immune microenvironment and could serve as a promising prognostic biomarker for glioma patients.

scholarly journals Development of an Immune-Related LncRNA Prognostic Signature for Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yudong Cao ◽  
Hecheng Zhu ◽  
Jun Tan ◽  
Wen Yin ◽  
Quanwei Zhou ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Clinical Outcome ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

IntroductionGlioma is the most common primary cancer of the central nervous system with dismal prognosis. Long noncoding RNAs (lncRNAs) have been discovered to play key roles in tumorigenesis in various cancers, including glioma. Because of the relevance between immune infiltrating and clinical outcome of glioma, identifying immune-related lncRNAs is urgent for better personalized management.Materials and methodsSingle-sample gene set enrichment analysis (ssGSEA) was applied to estimate immune infiltration, and glioma samples were divided into high immune cell infiltration group and low immune cell infiltration group. After screening differentially expressed lncRNAs in two immune groups, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct an immune-related prognostic signature. Additionally, we explored the correlation between immune infiltration and the prognostic signature.ResultsA total of 653 samples were appropriate for further analyses, and 10 lncRNAs were identified as immune-related lncRNAs in glioma. After univariate Cox regression and LASSO Cox regression analysis, six lncRNAs were identified to construct a prognostic signature for glioma, which could be taken as independent prognostic factors in both univariate and multivariate Cox regression analyses. Moreover, risk score was significantly correlated with all the 29 immune-related checkpoint expression (p &lt; 0.05) in ssGSEA except neutrophils (p = 0.43).ConclusionThe study constructed an immune-related prognostic signature for glioma, which contributed to improve clinical outcome prediction and guide immunotherapy.

scholarly journals Significance of hub genes and immune cell infiltration identified by bioinformatics analysis in pelvic organ prolapse

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9773
Author(s):  
Ying Zhao ◽  
Zhijun Xia ◽  
Te Lin ◽  
Yitong Yin
Keyword(s):  
Pelvic Organ Prolapse ◽  
Mast Cells ◽  
Immune Cell ◽  
Pearson Correlation ◽  
Cell Types ◽  
Pelvic Organ ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Differential Analysis ◽  
Hub Genes

Objective Pelvic organ prolapse (POP) refers to the decline of pelvic organ position and dysfunction caused by weak pelvic floor support. The aim of the present study was to screen the hub genes and immune cell infiltration related to POP disease. Methods Microarray data of 34 POP tissues in the GSE12852 gene expression dataset were used as research objects. Weighted gene co-expression network analysis (WGCNA) was performed to elucidate the hub module and hub genes related to POP occurrence. Gene function annotation was performed using the DAVID tool. Differential analysis based on the GSE12852 dataset was carried out to explore the expression of the selected hub genes in POP and non-POP tissues, and RT-qPCR was used to validate the results. The differential immune cell infiltration between POP and non-POP tissues was investigated using the CIBERSORT algorithm. Results WGCNA revealed the module that possessed the highest correlation with POP occurrence. Functional annotation indicated that the genes in this module were mainly involved in immunity. ZNF331, THBS1, IFRD1, FLJ20533, CXCR4, GEM, SOD2, and SAT were identified as the hub genes. Differential analysis and RT-qPCR demonstrated that the selected hub genes were overexpressed in POP tissues as compared with non-POP tissues. The CIBERSORT algorithm was employed to evaluate the infiltration of 22 immune cell types in POP tissues and non-POP tissues. We found greater infiltration of activated mast cells and neutrophils in POP tissues than non-POP tissues, while the infiltration of resting mast cells was lower in POP tissues. Moreover, we investigated the relationship between the type of immune cell infiltration and hub genes by Pearson correlation analysis. The results indicate that activated mast cells and neutrophils had a positive correlation with the hub genes, while resting mast cells had a negative correlation with the hub genes. Conclusions Our research identified eight hub genes and the infiltration of three types of immune cells related to POP occurrence. These hub genes may participate in the pathogenesis of POP through the immune system, giving them a certain diagnostic and therapeutic value.

scholarly journals Comprehensive Analysis of circRNA-associated Competing Endogenous RNA Networks and Immune Infiltration in Gastric Cancer

2021 ◽  
Author(s):  
beibei xu ◽  
Endian Zheng ◽  
Yi Huang ◽  
Liang Zheng ◽  
Qiaoli Lan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Immune Cell ◽  
Clinical Stage ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Ppi Networks

Abstract BackgroundCircular RNA (circRNA) has been shown to be an important regulator in gastric cancer (GC). However, functions and regulatory mechanisms of circRNA-related competitive endogenous RNA (ceRNA) in GC have not been established.MethodsCircRNA data and clinical data were downloaded from the GEO and TCGA databases. The ceRNA and Protein-Protein Interaction (PPI) networks were constructed through bioinformatics analysis. Function enrichment analysis was performed. Additionally, correlations between expression levels of the top 10 hub genes and immune cell infiltration levels, histopathological grade and clinical stage were determined to establish their clinical values. The differentially expressed circRNA (DEcircRNA) was validated by quantitative real-time PCR (qRT-PCR).ResultsScreening of the GEO and TCGA databases revealed a total of 1627 DEcircRNAs, 6516 DEmRNAs, and 1451 DEmiRNAs. The ceRNA interaction network contained 2 circRNAs, 3 miRNAs and 55 mRNAs. Meanwhile, Gene Ontology (GO) analysis revealed a total of 323 biological processes (BP) terms, 53 cellular components (CC) terms, 51 molecular functions (MF) terms, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed 4 signaling pathways. Gene Set Enrichment Analysis (GSEA) analysis revealed that EPHA4, NCAM1 and NRXN1 were positively correlated with the axon guidan and adhesion molecules pathways. Most of top 10 hub genes were positively correlated with B cells, CD8+ T cells, CD4+ T cells, Neutrophils and Dendritic Cell infiltration. Correlation analysis between hub genes and clinical phenotypes revealed that elevated expressions of EPHA4 and KCNA1 indicated poor tissue differentiation and were associated with clinically advanced stages of GC. The qRT-PCR results revealed that the expression of has_circ_0002504 was significantly down-regulated in 3 GC cell lines which was consistent with the results of our bioinformatics analysis.ConclusionsHas_circ_0001998 and has_circ_0002504 are potential diagnostic biomarkers for GC, and the high expressions of both EPHA4 and KCNA1 may predict poor prognosis.

scholarly journals The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

2021 ◽  
Vol 9 ◽  
Author(s):  
Sitong Zhou ◽  
Yidan Sun ◽  
Tianqi Chen ◽  
Jingru Wang ◽  
Jia He ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Signature ◽  
Clinicopathological Characteristics ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cluster A

The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored.Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p &lt; 0.001) and metastatic SKCM (p &lt; 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens.Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

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 GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration

Diagnostics ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 171 ◽  
Author(s):  
Ya-Jun Deng ◽  
En-Hui Ren ◽  
Wen-Hua Yuan ◽  
Guang-Zhi Zhang ◽  
Zuo-Long Wu ◽  
...  
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Diagnostic Markers ◽  
Cell Infiltration ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Resting Cells ◽  
Immune Cell Infiltration

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

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