scholarly journals Identification of Differentially Expressed Genes Reveals BGN Predicting Overall Survival and Tumor Immune Infiltration of Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Weizhi Chen ◽  
Zhongheng Yang
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Treatment Strategies ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Positive Correlations ◽  
First Time

Gastric cancer (GC) is one of the most widely occurring malignancies worldwide. Although the diagnosis and treatment strategies of GC have been greatly improved in the past few decades, the morbidity and lethality rates of GC are still rising due to lacking early diagnosis strategies and powerful treatments. In this study, a total of 37 differentially expressed genes were identified in GC by analyzing TCGA, GSE118897, GSE19826, and GSE54129. Using the PPI database, we identified 17 hub genes in GC. By analyzing the expression of hub genes and OS, MFAP2, BGN, and TREM1 were related to the prognosis of GC. In addition, our results showed that higher levels of BGN exhibited a significant correlation with shorter OS time in GC. Nomogram analysis showed that the dysregulation of BGN could predict the prognosis of GC. Moreover, we revealed that BGN had a markedly negative correlation with B cells but had positive correlations with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in GC samples. The pan-cancer analysis demonstrated that BGN was differentially expressed and related to tumor-infiltrating immune cells across human cancers. This study for the first time comprehensively revealed that BGN was a potential biomarker for the prediction of GC prognosis and tumor immune infiltration.

scholarly journals Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8390 ◽  
Author(s):  
Weisong Cai ◽  
Haohuan Li ◽  
Yubiao Zhang ◽  
Guangtao Han
Keyword(s):  
T Cells ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Synovial Tissue ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Controls

Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

scholarly journals Identification of Hub Genes related to the Progression of Bladder Cancer by an Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Han Wang ◽  
Jieqing Chen ◽  
Xinhui Liao ◽  
Yang Liu ◽  
Aifa Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Signalling Pathway ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Ppi Networks ◽  
Normal Bladder

Abstract BACKGROUND and OBJECTIVE: A better understanding of the molecular mechanisms underlying bladder cancer is necessary to identify candidate therapeutic targets. METHODS: We screened for genes associated with bladder cancer progression and prognosis. Publicly available expression data were obtained from TCGA and GEO to identify differentially expressed genes (DEGs) between bladder cancer and normal bladder tissues. Weighted co-expression networks were constructed, and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Associations between hub genes and immune infiltration and immune therapy were evaluated. RESULTS: 3461 DEGs in TCGA-BC and 1069 DEGs in the GSE dataset were identified, with 87 overlapping differentially expressed genes between the bladder cancer and normal bladder groups. Hub genes in the tumour group were mainly enriched for cell proliferation-related GO terms and KEGG pathways, while hub genes in the normal group were related to the synthesis and secretion of neurotransmitters. PPI networks for the genes identified in the normal and tumour groups were constructed. Based on a survival analysis, CDH19, RELN, PLP1, and TRIB3 were significantly associated with prognosis (P < 0.05). Four hub genes were significantly enriched in the MAPK signalling pathway, VEGF signalling pathway, WNT signalling pathway, cell cycle, and P53 signalling pathway based on a gene set enrichment analysis; these genes were associated with immune infiltration levels in bladder cancer. CONCLUSIONS: CDH19, RELN, PLP1, and TRIB3 may play important roles in the development of bladder cancer and are potential therapeutic and prognostic targets.

scholarly journals Interactive Verification Analysis of Multiple Sequencing Data for Identifying Potential Biomarker of Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Hongjun Fei ◽  
Songchang Chen ◽  
Chenming Xu
Keyword(s):  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Normal Lung ◽  
Pcr Analysis ◽  
Sequencing Data ◽  
Hub Genes ◽  
Lung Cancers ◽  
Potential Biomarker

Background. Lung adenocarcinoma (LUAD) comprises around 40% of all lung cancers, and in about 70% of patients, it has spread locally or systemically when first detected leading to a worse prognosis. Methods. We filtered out differentially expressed genes (DEGs) based on the RNA sequencing data in the Gene Expression Omnibus database and verified and deeply analyzed screened DEGs using a combined bioinformatics approach. Results. Expressions of 11,143 genes in 694 nontumor lung tissues and LUAD cases from 8 independent laboratories were analyzed; 188 mRNAs were identified as differentially expressed genes (DEGs). A PPI network constructed with 188 DEGs screened out 8 hub DEGs (CDH5, PECAM1, VWF, CLDN5, COL1A1, MMP9, SPP1, and IL6) which highly interconnected with other nodes. The expression levels of 8 hub genes in LUAD and control were assessed in the Oncomine database, and the results were consistent. The survival curves of 8 hub genes showed that their expressions are significantly related to the prognosis of lung cancer and LUAD patients except for IL6. Since the expression of IL6 is nonspecific and highly sensitive, we choose the other 7 hub genes we had verified to do the next analysis. Mutual exclusivity or cooccurrence analysis of 7 hub genes identified a tendency towards cooccurrence between CDH5, PECAM1, and VWF in LUAD. The coexpression profiles of CDH5 in LUAD were identified, and we found that PECAM1 and VWF coexpressed with CDH5. Immunohistochemistry and RT-PCR analysis showed that higher levels of CDH5, PECAM1, and VWF were expressed in normal lung tissues but a low or undetectable level was found in LUAD tissues. Conclusions. Taken together, we speculate that CDH5, PECAM1, and VWF played an important role in LUAD.

scholarly journals SNAP25 Is a Potential Prognostic Biomarker for Prostate Cancer

2021 ◽  
Author(s):  
Longjiang Di ◽  
Maoli Gu ◽  
Yan Wu ◽  
Guoqiang Liu ◽  
Lishuo Zhang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
T Cells ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Gleason Score ◽  
Immune Cells ◽  
Prognostic Biomarker ◽  
Differentially Expressed ◽  
Normal Prostate ◽  
Immune Infiltration

Abstract Background Prostate cancer is one of the most lethal cancers in male individuals. The Synaptosome associated protein 25 (SNAP25) gene is a key mediator of multiple biological functions in tumours. However, its significant impact on the prognosis in prostate cancer remains to be elucidated.Methods We performed a comprehensive analysis of the Cancer Genome Atlas dataset (TCGA) to identify the differentially expressed genes between prostate cancer and normal prostate tissue. We subjected the differentially expressed genes to gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes functional analysis, and constructed a protein-protein interaction network. We then screened for pivotal genes to identify the hub genes of prognostic significance by performing Cox regression analysis. We identified SNAP25 as one such gene and analysed the relationship between its expression in prostate cancer to poor prognosis using Studio R. Results TCGA database demonstrated that SNAP25 was significantly downregulated in prostate cancer, and that its expression was significantly correlated with the Gleason score and pathological TNM stage of patients. The association between SNAP25 expression and tumour-infiltrating immune cells was evaluated using the Tumour Immune Estimation Resource site. Gene set enrichment and gene ontology analyses were used to analyse the function of SNAP25. We found that SNAP25 expression strongly correlated with overall survival in the Gleason score. In addition, SNAP25 was involved in the activation, differentiation, and migration of immune cells, and its expression was positively correlated with immune infiltration, including of B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells, macrophages, and natural killer cells. SNAP25 expression was also positively correlated with chemokines/chemokine receptors, suggesting that SNAP25 might regulate the migration of immune cells. These molecular experiment results validate the low expression of SNAP25 seen in prostate cancer cells.Conclusion Our findings indicate a relationship between SNAP25 expression and prostate cancer, demonstrating that SNAP25 is a potential prognostic biomarker due to its vital role in immune infiltration.

scholarly journals Comparative Analysis on Abnormal Methylome of Differentially Expressed Genes and Disease Pathways in the Immune Cells of RA and SLE

2021 ◽  
Vol 12 ◽  
Author(s):  
Qinghua Fang ◽  
Tingyue Li ◽  
Peiya Chen ◽  
Yuzhe Wu ◽  
Tingting Wang ◽  
...  
Keyword(s):  
T Cells ◽  
B Cells ◽  
Differentially Expressed Genes ◽  
Immune Cells ◽  
Differentially Expressed ◽  
Receiver Operating Curve ◽  
Data Sets ◽  
Hub Genes ◽  
Differentially Methylated Genes ◽  
Disease Pathways

We identified abnormally methylated, differentially expressed genes (DEGs) and pathogenic mechanisms in different immune cells of RA and SLE by comprehensive bioinformatics analysis. Six microarray data sets of each immune cell (CD19+ B cells, CD4+ T cells and CD14+ monocytes) were integrated to screen DEGs and differentially methylated genes by using R package “limma.” Gene ontology annotations and KEGG analysis of aberrant methylome of DEGs were done using DAVID online database. Protein-protein interaction (PPI) network was generated to detect the hub genes and their methylation levels were compared using DiseaseMeth 2.0 database. Aberrantly methylated DEGs in CD19+ B cells (173 and 180), CD4+ T cells (184 and 417) and CD14+ monocytes (193 and 392) of RA and SLE patients were identified. We detected 30 hub genes in different immune cells of RA and SLE and confirmed their expression using FACS sorted immune cells by qPCR. Among them, 12 genes (BPTF, PHC2, JUN, KRAS, PTEN, FGFR2, ALB, SERB-1, SKP2, TUBA1A, IMP3, and SMAD4) of RA and 12 genes (OAS1, RSAD2, OASL, IFIT3, OAS2, IFIH1, CENPE, TOP2A, PBK, KIF11, IFIT1, and ISG15) of SLE are proposed as potential biomarker genes based on receiver operating curve analysis. Our study suggests that MAPK signaling pathway could potentially differentiate the mechanisms affecting T- and B- cells in RA, whereas PI3K pathway may be used for exploring common disease pathways between RA and SLE. Compared to individual data analyses, more dependable and precise filtering of results can be achieved by integrating several relevant data sets.

scholarly journals Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

scholarly journals Elucidating the Key Biomarkers and Immune Infiltration in Acne by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Lu Yang ◽  
Yan-hong Shou ◽  
Yong-sheng Yang ◽  
Jin-hua Xu
Keyword(s):  
Mast Cells ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Acne Vulgaris ◽  
Interaction Network ◽  
Microarray Dataset ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Immune Infiltration

Abstract Background Acne vulgaris is a common inflammatory condition of skin. However, the landscape of immune infiltration in acne has not been entirely described. Objectives This study used a bioinformatics approach to investigate the inflammatory acne-related key biomarkers and signaling pathways, and immune infiltration in the acne lesion. Methods Two microarray datasets (GSE108110 and GSE53795) were downloaded from Gene Expression Omnibus. We used “limma” package from R software to identify the differentially expressed genes (DEGs) and perform the functional enrichment analyses. Then we built a protein-protein interaction network (PPI), performed the hub genes’ identification through STRING and Cytoscape. We applied the CIBERSORT algorithm to describe the immune infiltration in acne, and explored the correlation between biomarkers and immune infiltration. In the end, our findings in the study were verified by analyzing microarray dataset GSE6475. Results The differentially expressed genes (DEGs) including 292 upregulated genes and 150 downregulated genes in acne compared with non-lesional skin. The hub genes FPR1, C3AR1, CXCL1, CXCL8, FPR2, C3, CCR7, ITGB2 and pivotal pathways JAK-STAT signaling pathway, Toll-like receptor and NOD-like receptor signaling pathway were the most significantly associated with raising neutrophils, monocytes, activated mast cells, as well as reducing resting mast cells and Tregs. Conclusions Our study provides new insights into the pathogenesis and the targets which might be immunomodulatory potential for acne.

scholarly journals Identification of Immune Infiltration, Differentially Expressed Genes, and Signaling Pathways in Fanconi Anemia Based on Bioinformatics Analysis

2022 ◽  
Author(s):  
Biyu Shen ◽  
Songsong Shi ◽  
Haoyang Chen ◽  
Yi Lu ◽  
Hengmei Cui ◽  
...  
Keyword(s):  
T Cells ◽  
Differentially Expressed Genes ◽  
Fanconi Anemia ◽  
Bioinformatics Analysis ◽  
Neutrophil Activation ◽  
Normal Group ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Immune Infiltration ◽  
Kegg Analysis

Abstract Background and Objective: Fanconi anemia (FA) patients have a reduced ability to form blood cells, accompanied by multiple congenital malformations, mental retardation, solid tumors, and other symptoms. However, the molecular mechanism that causes FA is unclear, and few studies have addressed the regulatory mechanism of immune infiltration in FA. Here, we aimed to identify differentially expressed genes (DEGs), pathways, and immune infiltration involved in FA using integrated bioinformatics analysis and molecular mechanisms. Methods: The GEO gene chip database was searched for FA low density bone marrow tissue, and the content and proportion of 22 types of immune cells in the FA group and the normal group were analyzed using CIBERSORT. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of FA differentially expressed genes (DEGs) using R language and related package programs was also performed.Results: The expression levels of T cells regulatory (Tregs), M2 macrophages, T cells CD8, dendritic cells resting, and T cells CD4 naïve in FA were higher than in the normal group. Furthermore, the expression levels of naïve B cells, monocytes, and resting mast cells in FA were lower than in the normal group. GO analysis of FA differential genes showed that “neutrophil degranulation,” “neutrophil activation,” and “neutrophil activation involved in immune response,” were most frequently enriched among biological processes, with “specific granule,” “tertiary granule,” “tertiary granule lumen” among cellular components, and “carbohydrate binding” among molecular functions. For the KEGG analysis, “Asthma” was most often enriched.Conclusion: This study obtained useful data related to immune infiltration, DEGs, and gene pathways of FA, and provides new evidence for immunotherapy and clinical assessment of FA patients. These results are potentially a useful reference for subsequent related scientific research.

scholarly journals Identification and Validation of ATF3 Serving as a Potential Biomarker and Correlating With Pharmacotherapy Response and Immune Infiltration Characteristics in Rheumatoid Arthritis

2021 ◽  
Vol 8 ◽  
Author(s):  
Huan Hu ◽  
Facai Zhang ◽  
Li Li ◽  
Jun Liu ◽  
Qin Ao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Differentially Expressed Genes ◽  
Immune Cell ◽  
Differentially Expressed ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Potential Biomarker ◽  
Gene Modules ◽  
Atf3 Expression

Background: Although disease-modifying antirheumatic drugs (DMARDs) have significantly improved the prognosis of patients with rheumatoid arthritis (RA), approximately 40% of RA patients have limited response. Therefore, it was essential to explore new biomarkers to improve the therapeutic effects on RA. This study aimed to develop a new biomarker and validate it by an in vitro study.Methods: The RNA-seq and the clinicopathologic data of RA patients were downloaded from Gene Expression Omnibus (GEO) databases. Differentially expressed genes were screened in the GPL96 and GPL570 databases. Then, weighted gene co-expression network analysis (WGCNA) was used to explore the most correlated gene modules to normal and RA synovium in the GPL96 and GPL570 databases. After that, the differentially expressed genes were intersected with the correlated gene modules to find the potential biomarkers. The CIBERSORT tool was applied to investigate the relationship between activated transcription factor 3 (ATF3) expression and the immune cell infiltration, and Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in the high and low ATF3 groups. Furthermore, the relationships between ATF3 expression and clinical parameters were also explored in the GEO database. Finally, the role of ATF3 was verified by in vitro cell experiments.Results: We intersected the differentially expressed genes and the most correlated gene modules in the GPL570 and GPL96 databases and identified that ATF3 is a significant potential biomarker and correlates with some clinical–pharmacological variables. Immune infiltration analysis showed that activated mast cells had a significant infiltration in the high ATF3 group in the two databases. GSEA showed that metabolism-associated pathways belonged to the high ATF3 groups and that inflammation and immunoregulation pathways were enriched in the low ATF3 group. Finally, we validated that ATF3 could promote the proliferation, migration, and invasion of RA fibroblast-like synoviocyte (FLS) and MH7A. Flow cytometry showed that ATF3 expression could decrease the proportion of apoptotic cells and increase the proportion of S and G2/M phase cells.Conclusion: We successfully identified and validated that ATF3 could serve as a novel biomarker in RA, which correlated with pharmacotherapy response and immune cell infiltration.

Bioinformatics Analysis Identifies CPZ as a Tumor Immunology Biomarker For Gastric Cancer

2020 ◽  
Vol 15 ◽  
Author(s):  
Yuan Gu ◽  
Ying Gao ◽  
Xiaodan Tang ◽  
Huizhong Xia ◽  
Kunhe Shi
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Signaling Pathway ◽  
Tumor Immunology ◽  
Bioinformatics Analysis ◽  
Human Cancer ◽  
Disease Free Survival ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Pathway Regulation

Background: Gastric cancer (GC) is one of the most common malignancies worldwide. However, the biomarkers for the prognosis and diagnosis of Gastric cancer were still need. Objective: The present study aimed to evaluate whether CPZ could be a potential biomarker for GC. Method: Kaplan-Meier plotter (http://kmplot.com/analysis/) was used to determine the correlation between CPZ expression and overall survival (OS) and disease-free survival (DFS) time in GC [9]. We analyzed CPZ expression in different types of cancer and the correlation of CPZ expression with the abundance of immune infiltrates, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, via gene modules using TIMER Database. Results: The present study identified that CPZ was overexpressed in multiple types of human cancer, including Gastric cancer. We found that overexpression of CPZ correlates to the poor prognosis of patients with STAD. Furthermore, our analyses show that immune infiltration levels and diverse immune marker sets are correlated with levels of CPZ expression in STAD. Bioinformatics analysis revealed that CPZ was involved in regulating multiple pathways, including PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, Rap1 signaling pathway, TGF-beta signaling pathway, regulation of cell adhesion, extracellular matrix organization, collagen fibril organization, collagen catabolic process. Conclusion: This study for the first time provides useful information to understand the potential roles of CPZ in tumor immunology and validate it to be a potential biomarker for GC.

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