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 Bioinformatics analysis of the differentially co-expressed genes and immune cell infiltration features associated with pulmonary arterial hypertension

2021 ◽  
Author(s):  
Wenshi Liu ◽  
Dongdong Zheng ◽  
Wenjing Lv ◽  
Ying Hua ◽  
Rong Huang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Integrin Subunit ◽  
Immune Cell Infiltration ◽  
Control Groups ◽  
Immune Infiltration ◽  
And Control

IntroductionThis study aimed to identify novel differentially co-expressed genes and to investigate the features of immune cell infiltration in PAH.Material and methodsThe GSE113439 and GSE117261 datasets were acquired from the Gene Expression Omnibus database. And the differentially expressed genes between PAH and control groups were identified based on the GSE117261 dataset. Weighted Gene Co-Expression Network Analysis (WGCNA) was adopted to analyze the pre-processed data. Functional enrichment analysis was then carried out to explore the biological functions of these genes modules. The differentially co-expressed key genes modules were in-depth verified by GEO2R analysis. The immune infiltration in PAH was investigated by Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT).ResultsWGCNA analysis found 15 differentially co-expressed genes modules, amongst which module blue indicated that it exhibited the strongest positive link to PAH, whereas module green presented the strongest negative association with PAH. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that the genes in module blue were largely enriched in Lysosome, Complement, and coagulation cascades, and others, while the genes in module green were primarily enriched in the Chemokine signaling pathway, Platelet activation, etc. Integrin subunit alpha M (ITGAM) was identified as the differentially co-expressed key gene. Immune infiltration analysis by CIBERSORT showed that the differences between PAH and control groups or between PAH subgroups.ConclusionsITGAM was considered a promising biomarker to discriminate PAH from the control. Obvious differences were observed in immune infiltration between patients with PAH and normal groups.

scholarly journals Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy

2020 ◽  
Vol 10 ◽  
Author(s):  
Jianfang Niu ◽  
Taiqiang Yan ◽  
Wei Guo ◽  
Wei Wang ◽  
Zhiqing Zhao ◽  
...  
Keyword(s):  
Immune Cell ◽  
Therapeutic Targets ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

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 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.

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 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.

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 ARID1A Is a Prognostic Biomarker and Associated with Immune Infiltrates in Hepatocellular Carcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Yuanyuan Feng ◽  
Xinfang Tang ◽  
Changcheng Li ◽  
Ying Su ◽  
Xiaoyu Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Migration And Invasion ◽  
Risk Indicator ◽  
Immune Cell Infiltration

Objective. ARID1A has been discovered as a potential cancer biomarker. But its role in hepatocellular carcinoma (HCC) is subject to considerable dispute. Methods. The relationship between ARID1A and clinical factors was investigated. Clinicopathological variables related to overall survival in HCC subjects were identified using Cox and Kaplan–Meier studies. The connection between immune infiltrating cells and ARID1A expression was investigated using the tumor Genome Atlas (TCGA) dataset for gene set enrichment analysis (GSEA). Finally, a cell experiment was used to confirm it. Results. The gender and cancer topography (T) categorization of HCC were linked to increased ARID1A expression. Participants with advanced levels of ARID1A expression had a worse prognosis than someone with lower levels. ARID1A was shown to be a risk indicator of overall survival on its own. ARID1A expression is inversely proportional to immune cell infiltration. In vitro, decreasing ARID1A expression substantially slowed the cell cycle and decreased HCC cell proliferation, migration, and invasion. Conclusion. The expression of ARID1A could be used to predict the outcome of HCC. It is closely related to tumor immune cell infiltration.

scholarly journals RNA N6-Methyladenosine Regulator-Mediated Methylation Modifications Pattern and Immune Infiltration Features in Glioblastoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yimin Pan ◽  
Kai Xiao ◽  
Yue Li ◽  
Yuzhe Li ◽  
Qing Liu
Keyword(s):  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Lasso Regression ◽  
Gene Set Enrichment ◽  
Clinical Prognosis ◽  
Prognostic Assessment ◽  
The Relationship

Glioblastoma (GBM) is a group of intracranial neoplasms with intra-tumoral heterogeneity. RNA N6-methyladenosine (m6A) methylation modification reportedly plays roles in immune response. The relationship between the m6A modification pattern and immune cell infiltration in GBM remains unknown. Utilizing expression data of GBM patients, we thoroughly explored the potential m6A modification pattern and m6A-related signatures based on 21 regulators. Thereafter, the m6A methylation modification-based prognostic assessment pipeline (MPAP) was constructed to quantitatively assess GBM patients’ clinical prognosis combining the Robustness and LASSO regression. Single-sample gene-set enrichment analysis (ssGSEA) was used to estimate the specific immune cell infiltration level. We identified two diverse clusters with diverse m6A modification characteristics. Based on differentially expressed genes (DEGs) within two clusters, m6A-related signatures were identified to establish the MPAP, which can be used to quantitatively forecast the prognosis of GBM patients. In addition, the relationship between 21 m6A regulators and specific immune cell infiltration was demonstrated in our study and the m6A regulator ELAVL1 was determined to play an important role in the anticancer response to PD-L1 therapy. Our findings indicated the relationship between m6A methylation modification patterns and tumor microenvironment immune cell infiltration, through which we could comprehensively understand resistance to multiple therapies in GBM, as well as accomplish precise risk stratification according to m6A-related signatures.

scholarly journals Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

2022 ◽  
Vol 2022 ◽  
pp. 1-24
Author(s):  
Bin-Bin Da ◽  
Shuai Luo ◽  
Ming Huang ◽  
Fei Song ◽  
Rong Ding ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Inflammatory Response ◽  
Immune Cell ◽  
Cox Regression ◽  
Inflammatory Responses ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Differential Analysis

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

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