scholarly journals Characteristics of Tumor Immune Gene and Immune Cell Infiltration during Keloid Formation

2020 ◽  
Author(s):  
Hao Liu ◽  
Mengjie Shan ◽  
Youbin Wang ◽  
Kexin Song ◽  
Shu Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Immune Cell ◽  
Kegg Pathway ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Immune Gene ◽  
Immune Gene Expression ◽  
M1 Macrophage ◽  
Keloid Formation

Abstract Background: Keloids are benign fibroproliferative skin tumors that can cause disfigurement and disability. Although current research has sought to examine keloids from the perspectives of genetics, inflammation, immunity, and tumorigenesis, their pathological mechanisms remain unclear. Methods: In this study, we used three datasets of tumor immune gene expression profiling from the normal skin tissue of keloid patients (N group), inflammation tissue of keloid patients (I group), and keloid tissue of keloid patients (K group) to describe the occurrence and characteristics of keloid development. Tumor immune-related genes were analyzed, and the differentially expressed genes (DEGs) between the three groups were compared. Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out to determine the main functions of the differentially expressed genes and keloid-related pathways. Results: We identified several genes that may play an important role in keloid development. These genes are CCR1, SELL, CCR7, CD40LG, CD69, CXCL8, ITGAM, ITGAX, CD86, and CXCL9. GO analysis revealed that there were variations in biological processes (BP) between I group and N group, including regulation of lymphocyte activation and T-cell activation. Similar variations were also found between I group and K group, which may play an important role in keloid initiation and formation. Variations in molecular function (MF) were markedly enriched in cytokine receptor binding and receptor ligand activity. Analysis of the KEGG pathway between I group and N group revealed that DEGs were primarily enriched in cytokine−cytokine receptor interaction and viral protein interaction with cytokine and cytokine receptor. We identified a higher proportion of M2 macrophages in N group than in I group, although the difference was not obvious. M1 macrophage production differed significantly between I group and K group. The proportions of CD8+T cells varied significantly between N group and K group. We traced multiple tumor immune-related hub genes from keloid formation and analyzed immune cell subsets in keloid development. Possible molecular mechanisms were described in this study using bioinformatics. Conclusions: These results provide another possibility to elucidate keloid pathogenesis and therapeutic targets in terms of tumor immune gene expression.

scholarly journals Increased Immune Gene Expression and Immune Cell Infiltration in High-Grade Astrocytoma Distinguish Long-Term from Short-Term Survivors

2012 ◽  
Vol 189 (4) ◽  
pp. 1920-1927 ◽  
Author(s):  
Andrew M. Donson ◽  
Diane K. Birks ◽  
Stephanie A. Schittone ◽  
Bette K. Kleinschmidt-DeMasters ◽  
Derrick Y. Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Cell Infiltration ◽  
Immune Gene ◽  
Immune Cell Infiltration ◽  
High Grade ◽  
Short Term ◽  
Immune Gene Expression ◽  
High Grade Astrocytoma

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals FCN3 Is a Prognostic Biomarker Correlated with Immune Response in Liver Cancer

2020 ◽  
Author(s):  
Zhongxiao Lu ◽  
Jian Wu ◽  
Yi-ming Li ◽  
Wen-xiang Chen ◽  
Qiang-feng Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Liver Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
R Software ◽  
Hub Gene

Abstract AimLiver cancer is a common malignant tumor whose molecular pathogenesis remains unclear. This study attempts to identify key genes related to liver cancer by bioinformatics analysis and analyze their biological functions.MethodsThe gene expression data of the microarray were downloaded from the Gene Expression Omnibus(GEO) database. The differentially expressed genes (DEGs) were then identified by the R software package “limma” and were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using DAVID. The protein-protein interaction (PPI) network was constructed via String, and the results were visualized in Cytoscape. Modules and hub genes were identified using the MCODE plugin, while the expression of hub genes and its effects were analyzed by GEPIA2. Additionally, the co-expression of the hub gene was explored in String, while the GO results were visualized using the R software. Finally, the targets of the hub gene were predicted through an online website. ResultsIn total, 43 differentially expressed genes were obtained. The GO analysis was mainly concentrated in the redox process and nuclear mitosis, while the KEGG pathway analysis was mainly enriched in retinol metabolism and the cell cycle. Moreover, four hub genes were identified in the PPI network, however, the Kaplan-Meier risk curve showed that only ECT2 and FCN3 affected the survival of liver cancer. ECT2 was found to be high expressed in liver cancer, carrying out signal transduction and targeting hsa-miR-27a-3p. FCN3 was observed to be lowly expressed in liver cancer and related to the immune response, targeting hsa-miR132-5p.ConclusionThe obtained findings suggest that two genes are significantly related to the prognosis of liver cancer, and the analysis of their biological function provided novel insight into the pathogenesis of liver cancer. Furthermore, FCN3 may serve as a promising biomarker for patients with liver cancer.

scholarly journals Differential Analysis of Expressed Genes in Diabetic Nephropathy Based on Bioinformatics Technology

2021 ◽  
Author(s):  
Yu Liu ◽  
Jundong Wang ◽  
wencheng Chi ◽  
Jing Xie ◽  
LaiKuan Teh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Biological Effects ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Differential Analysis ◽  
Core Genes

Abstract Objective: Bioinformatics technology was used in this study to analyze the expression data of patients with diabetic nephropathy (DN) and normal subjects from the microarray. The purpose of this study was to screen the differentially expressed genes in DN and to explore the pathogenesis and potential therapeutic targets of DN. Methods: The data of gene expression in the gse142153 gene chip was downloaded from the gene expression database (GEO). The up-regulated and down-regulated expressed genes were analyzed by R language. The core genes of differentially expressed genes were analyzed by string database, Cytoscape software and its plug-in. The differentially expressed genes were analyzed by gene ontology and Kyoto Encyclopedia of genes and genomes. Results: A total of 112 differentially expressed genes were screened, including 50 down-regulated genes and 62 up-regulated genes. There are 10 up-regulated core genes including CXCL8, MMP9, IL1B, IL6, IL10, CXCL2, CCL20, ATF3, CXCL3, F3. Their biological effects are mainly concentrated in the IL-17 signaling pathway, rheumatoid arthritis, viral protein interaction with cytokine and cytokine receptor, Amoebiasis, TNF signaling pathway, Legionellosis, Cytokine-cytokine receptor interaction, Lipid, and atherosclerosis, Malaria, NOD-like receptor signaling pathway, etc. Conclusion: Analysis of differentially expressed genes and core genes enhanced the understanding of the pathogenesis of DN and provided a potential train of thought for the treatment of DN.

scholarly journals Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6425 ◽  
Author(s):  
Yang Fang ◽  
Pingping Wang ◽  
Lin Xia ◽  
Suwen Bai ◽  
Yonggang Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Expressed Genes ◽  
Protein Protein Interaction Network

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.

scholarly journals Immune gene expression in kidney biopsies of lupus nephritis patients at diagnosis and at renal flare

2018 ◽  
Vol 34 (7) ◽  
pp. 1197-1206 ◽  
Author(s):  
Juan M Mejia-Vilet ◽  
Samir V Parikh ◽  
Huijuan Song ◽  
Paolo Fadda ◽  
John P Shapiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lupus Nephritis ◽  
De Novo ◽  
Kidney Tissue ◽  
Differentially Expressed ◽  
Molecular Heterogeneity ◽  
Significant Heterogeneity ◽  
Immune Gene ◽  
Necrosis Factor Alpha ◽  
Immune Gene Expression

Abstract Background Up to 50% of lupus nephritis (LN) patients experience renal flares after their initial episode of LN. These flares contribute to poor renal outcomes. We postulated that intrarenal immune gene expression is different in flares compared with de novo LN, and conducted these studies to test this hypothesis. Methods Glomerular and tubulointerstitial immune gene expression was evaluated in 14 patients who had a kidney biopsy to diagnose LN and another biopsy at their first LN flare. Ten healthy living kidney donors were included as controls. RNA was extracted from laser microdissected formalin-fixed paraffin-embedded kidney biopsies. Gene expression was analyzed using the Nanostring nCounter® platform and validated by quantitative real-time polymerase chain reaction. Differentially expressed genes were analyzed by the Ingenuity Pathway Analysis and Panther Gene Ontology tools. Results Over 110 genes were differentially expressed between LN and healthy control kidney biopsies. Although there was considerable molecular heterogeneity between LN biopsies at diagnosis and flare, for about half the LN patients gene expression from the first LN biopsy clustered with the repeated LN biopsy. However, in all patients, a set of eight interferon alpha-controlled genes had a significantly higher expression in the diagnostic biopsy compared with the flare biopsy. In contrast, nine tumor necrosis factor alpha-controlled genes had higher expression in flare biopsies. Conclusions There is significant heterogeneity in immune-gene expression of kidney tissue from LN patients. There are limited but important differences in gene expression between LN flares, which may influence treatment decisions.

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 Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ben Wan ◽  
Renxian Wang ◽  
Jingjun Nie ◽  
Yuyang Sun ◽  
Bowen Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Cell ◽  
Structure Learning ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Poor Response ◽  
Immune Gene ◽  
Immune Gene Expression ◽  
Tumor Immune Microenvironment ◽  
Graph Structure Learning

Background. Osteosarcoma (OS) patients have a poor response to immunotherapy due to the sheer complexity of the immune system and the nuances of the tumor-immune microenvironment. Methodology. To gain insights into the immune heterogeneity of OS, we identified robust clusters of patients based on the immune gene expression profiles of OS patients in the TARGET database and assessed their reproducibility in an independent cohort collected from the GEO database. The association of comprehensive molecular characterization with reproducible immune subtypes was accessed with ANOVA. Furthermore, we visualized the distribution of individual patients in a tree structure by the graph structure learning-based dimensionality reduction algorithm. Results. We found that 87 OS samples can be divided into 5 immune subtypes, and each of them was associated with distinct clinical outcomes. The immune subtypes also demonstrated widely different patterns in tumor genetic aberrations, tumor-infiltrating, immune cell composition, and cytokine profiles. The immune landscape of OS uncovered the significant intracluster heterogeneity within each subtype and depicted a continuous immune spectrum across patients. Conclusion. The established five immune subtypes in our study suggested immune heterogeneity in OS patients and may provide optimal individual immunotherapy for patients exhibiting OS.

Transcriptional identification of differentially expressed genes during the prepupal–pupal transition in the oriental armyworm, Mythimna separata (Walker) (Lepidoptera: Noctuidae)

2021 ◽  
pp. 1-14
Author(s):  
Peirong Li ◽  
Xinru Li ◽  
Wei Wang ◽  
Xiaoling Tan ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Metabolic Pathways ◽  
Developmental Stages ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Mythimna Separata ◽  
Kegg Pathway Analysis ◽  
Oriental Armyworm ◽  
Lepidoptera Noctuidae

Abstract The oriental armyworm, Mythimna separata (Walker) is a serious pest of agriculture that does particular damage to Gramineae crops in Asia, Europe, and Oceania. Metamorphosis is a key developmental stage in insects, although the genes underlying the metamorphic transition in M. separata remain largely unknown. Here, we sequenced the transcriptomes of five stages; mature larvae (ML), wandering (W), and pupation (1, 5, and 10 days after pupation, designated P1, P5, and P10) to identify transition-associated genes. Four libraries were generated, with 22,884, 23,534, 26,643, and 33,238 differentially expressed genes (DEGs) for the ML-vs-W, W-vs-P1, P1-vs-P5, and P5-vs-P10, respectively. Gene ontology enrichment analysis of DEGs showed that genes regulating the biosynthesis of the membrane and integral components of the membrane, which includes the cuticular protein (CP), 20-hydroxyecdysone (20E), and juvenile hormone (JH) biosynthesis, were enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that DEGs were enriched in the metabolic pathways. Of these DEGs, thirty CP, seventeen 20E, and seven JH genes were differentially expressed across the developmental stages. For transcriptome validation, ten CP, 20E, and JH-related genes were selected and verified by real-time PCR quantitative. Collectively, our results provided a basis for further studies of the molecular mechanism of metamorphosis in M. separata.

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