scholarly journals Identification of uterine leiomyosarcoma-associated hub genes and immune cell infiltration pattern using weighted co-expression network analysis and CIBERSORT algorithm

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xiaoqing Shen ◽  
Zhujuan Yang ◽  
Songwei Feng ◽  
Yi Li
Keyword(s):  
Mast Cells ◽  
Network Analysis ◽  
Nk Cells ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Uterine Leiomyosarcoma ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Limma Package ◽  
M1 Type

Abstract Background While large-scale genomic analyses symbolize a precious attempt to decipher the molecular foundation of uterine leiomyosarcoma (ULMS), bioinformatics results associated with the occurrence of ULMS based totally on WGCNA and CIBERSORT have not yet been reported. This study aimed to screen the hub genes and the immune cell infiltration pattern in ULMS by bioinformatics methods. Methods Firstly, the GSE67463 dataset, including 25 ULMS tissues and 29 normal myometrium (NL) tissues, was downloaded from the public database. The differentially expressed genes (DEGs) were screened by the ‘limma’ package and hub modules were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, gene function annotations were performed to investigate the biological role of the genes from the intersection of two groups (hub module and DEGs). The above genes were calculated in the protein–protein interaction (PPI) network to select the hub genes further. The hub genes were validated using external data (GSE764 and GSE68295). In addition, the differential immune cell infiltration between UL and ULMS tissues was investigated using the CIBERSORT algorithm. Finally, we used western blot to preliminarily detect the hub genes in cell lines. Results WGCNA analysis revealed a green-yellow module possessed the highest correlation with ULMS, including 1063 genes. A total of 172 DEGs were selected by thresholds set in the ‘limma’ package. The above two groups of genes were intersected to obtain 72 genes for functional annotation analysis. Interestingly, it indicated that 72 genes were mainly involved in immune processes and the Neddylation pathway. We found a higher infiltration of five types of cells (memory B cells, M0-type macrophages, mast cells activated, M1-type macrophages, and T cells follicular helper) in ULMS tissues than NL tissues, while the infiltration of two types of cells (NK cells activated and mast cells resting) was lower than in NL tissues. In addition, a total of five genes (KDR, CCL21, SELP, DPT, and DCN) were identified as the hub genes. Internal and external validation demonstrated that the five genes were over-expressed in NL tissues compared with USML tissues. Finally, the correlation analysis results indicate that NK cells activated and mast cells activated positively correlated with the hub genes. However, M1-type macrophages had a negative correlation with the hub genes. Moreover, only the DCN may be associated with the Neddylation pathway. Conclusion A series of evidence confirm that the five hub genes and the infiltration of seven types of immune cells are related to USML occurrence. These hub genes may affect the occurrence of USML through immune-related and Neddylation pathways, providing molecular evidence for the treatment of USML in the future.

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 Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Darzi ◽  
Saeid Gorgin ◽  
Keivan Majidzadeh-A ◽  
Rezvan Esmaeili
Keyword(s):  
Network Analysis ◽  
Protein Transport ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Interaction ◽  
Protein Glycosylation ◽  
Cell Infiltration ◽  
Uterine Leiomyosarcoma ◽  
Immune Cell Infiltration ◽  
Protective Genes

AbstractThe present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4–0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44–0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06–2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28–0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell–cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

scholarly journals Comprehensive analysis of the MIR4435-2HG/miR-1-3p/MMP9/miR-29-3p/DUXAP8 ceRNA network axis in hepatocellular carcinoma

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Li Zhang ◽  
Shangshang Hu ◽  
Jiasheng Chen ◽  
Shasha Ma ◽  
Fanghong Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Mast Cells ◽  
Nk Cells ◽  
Immune Cell ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
M2 Macrophages ◽  
Cerna Network

AbstractA growing number of studies have shown that competitive endogenous RNA (ceRNA) regulatory networks might play important roles during the process of hepatocellular carcinoma (HCC). This study assessed the role of the ceRNA network in immune cell infiltration in HCC. Immune-related gene sets were downloaded from Molecular Signatures Database, and differentially expressed genes were screened based on TCGA HCC transcriptome data. The corresponding miRNAs with low expression and good prognostic implications, and the corresponding lncRNAs with high expression and poor prognostic were identified to construct ceRNA networks. The networks were utilized for clinical correlation analysis and risk model construction, and the CIBERSORT algorithm was applied to assess immune cell infiltration. In this study, the mRNA-miRNA-lncRNA model was used to construct a ceRNA network in HCC using immune-related differentially expressed mRNAs. Assessment of the MIR4435-2HG/hsa-miR-1-3p/MMP9/hsa-miR-29-3p/DUXAP8 ceRNA network axis in HCC showed that a high risk/poor prognosis was significantly correlated with tumor stage and invasion depth. MMP9 was positively correlated with resting M0 macrophages and NK cells and negatively correlated with activated mast cells, resting mast cells, monocytes and activated NK cells. DUXAP8 was positively correlated with M2 macrophages and negatively correlated with MIR4435-2HG, which was positively correlated with M2 macrophages and negatively correlated with activated mast cells, CD8 T cells and follicular helper T cells. The correlation of the MIR4435-2HG/hsa-miR-1-3p/MMP9/hsa-miR-29-3p/DUXAP8 ceRNA network axis with immune cell infiltration provides further information on the mechanism of HCC development. The result might improve our understanding the interactions between immune related genes and non-coding RNAs in the occurrence and development of HCC, and the relevant RNAs might be used as diagnostic and prognostic biomarkers and molecular targets in HCC patients.

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 Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

2021 ◽  
Author(s):  
shenglan li ◽  
Zhuang Kang ◽  
jinyi Chen ◽  
Can Wang ◽  
Zehao Cai ◽  
...  
Keyword(s):  
Immune Cell ◽  
Intracranial Tumor ◽  
Cell Infiltration ◽  
Molecular Profile ◽  
Immune Cell Infiltration ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Hub Gene ◽  
External Data

Abstract Background Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Methods Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. Results The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Conclusions Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

scholarly journals Association Between Tumor Mutation Burden and Prognosis in Lower-Grade Glioma: An Exploratory Study 

2021 ◽  
Author(s):  
Di Cao ◽  
Jun Wang ◽  
Yan Lin ◽  
Guangwei Li
Keyword(s):  
Immune Cell ◽  
Differential Expression Analysis ◽  
Cell Infiltration ◽  
Lower Grade ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Lower Grade Glioma ◽  
Genome Atlas

Abstract Background: The therapeutic efficacy of immune checkpoint inhibitor therapy is highly influenced by tumor mutation burden (TMB). The relationship between TMB and prognosis in lower-grade glioma is still unclear. We aimed to explore the relationships and mechanisms between them in lower-grade glioma.Methods: We leveraged somatic mutation data from The Cancer Genome Atlas (TCGA). Clinical cases were divided into high- and low-TMB groups based on the median of TMB. Infiltrating immune cells were analyzed using CIBERSORT and differential expression analysis between the prognostic groups performed. The key genes were identified as intersecting between immune-related genes. Cox regression and survival analysis were performed on the intersecting genes. A total of 7 hub genes were identified. The effect of somatic copy number alterations (SCNA) of the hub genes on immune cell infiltration was analyzed using TIMER, which was used to determine the risk factors and immune infiltration status in LGG. Subsequently, based on hub genes, a TMB Prognosis Index (TMBPI) model was constructed to predict the risk in LGG patients. Besides, this model was validated using data from TCGA and Chinese Glioma Genome Atlas (CGGA).Results: High-TMB favored worse prognosis (P＜0.001) and macrophage infiltration was an independent risk factor (P＜0.001). In the high-TMB group (P=0.033, P=0.009), the proportion of macrophages M0 and M2 increased and monocytes decreased (P=0.006). Besides, the SCNA of the hub genes affected the level of immune cell infiltration by varying degrees among which IGF2BP3, NPNT, and PLA2G2A had a significant impact. The AUC of the ROC curve at 1-, 3- and 5-years were all above 0.74.Conclusions: This study implies that high-TMB correlated with unfavorable prognosis in lower-grade glioma. And high-TMB may have an impact on prognosis by changing tumor microenvironment, caused by the SCNAs of genes. The TMBPI model accurately predicted prognosis in LGG patients.

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 The Downregulation of PTGS2 Mediated by ncRNAs is Tightly Correlated with Systemic Sclerosis-Interstitial Lung Disease

2022 ◽  
Vol 12 ◽  
Author(s):  
Zhixiao Xu ◽  
Chengshui Chen
Keyword(s):  
Systemic Sclerosis ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Immune Cell ◽  
Gene Expression Omnibus ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Cd20 Expression ◽  
Key Genes

Background: Interstitial lung disease in systemic sclerosis (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the main cause of mortality. In this study, we aimed to explore the key genes in SSc-ILD and analyze the relationship between key genes and immune cell infiltration as well as the key genes relevant to the hallmarks of cancer.Methods: Weighted gene co-expression network analysis (WGCNA) algorithm was implemented to explore hub genes in SSc-ILD samples from the Gene Expression Omnibus (GEO) database. Logistic regression analysis was performed to screen and verify the key gene related to SSc-ILD. CIBERSORT algorithms were utilized to analyze immune cell infiltration. Moreover, the correlation between the key genes and genes relevant to cancer was also evaluated. Furthermore, non-coding RNAs (ncRNAs) linking to PTGS2 were also explored.Results: In this study, we first performed WGCNA analysis for three GEO databases to find the potential hub genes in SSc-ILD. Subsequently, we determined PTGS2 was the key gene in SSC-ILD. Furthermore, in CIBERSORT analyses, PTGS2 were tightly correlated with immune cells such as regulatory T cells (Tregs) and was negatively correlated with CD20 expression. Moreover, PTGS2 was associated with tumor growth. Then, MALAT1, NEAT1, NORAD, XIST identified might be the most potential upstream lncRNAs, and LIMS1 and RANBP2 might be the two most potential upstream circRNAs.Conclusion: Collectively, our findings elucidated that ncRNAs-mediated downregulation of PTGS2, as a key gene in SSc-ILD, was positively related to the occurrence of SSc-ILD and abnormal immunocyte infiltration. It could be a promising factor for SSc-ILD progression to malignancy.

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