scholarly journals Crosstalk between Venous Thromboembolism and Periodontal Diseases: A Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zheng He ◽  
Qilong Jiang ◽  
Fuping Li ◽  
Mingxiang Chen
Keyword(s):  
Venous Thromboembolism ◽  
Bioinformatics Analysis ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Immune Gene ◽  
Ppi Network ◽  
Immune Genes ◽  
Immune Related Genes ◽  
Control Samples

Background. This current study applied bioinformatics analysis to reveal the crosstalk between venous thromboembolism (VTE) and periodontitis, as well as the potential role of immune-related genes in this context. Methods. Expression data were downloaded from the GEO database. Blood samples from venous thromboembolism (VTE) were used (GSE19151), while for periodontal disease, we used gingival tissue samples (GSE10334, GSE16134, and GSE23586). After batch correction, we used “limma” packages of R language for differential expression analysis ( p value < 0.05, ∣ logFC ∣ ≥ 0.5 ). We used Venn diagrams to extract the differentially expressed genes common to VTE and periodontitis as potential crosstalk genes and applied functional enrichment analysis (GO biological process and KEGG pathway). The protein-protein interaction (PPI) network of crosstalk genes was constructed by Cytoscape software. The immune-related genes were downloaded from the literature. The Wilcoxon test was used to test the scores of immune infiltrating cells. The crosstalk genes were further screened by LASSO Logistic Regression. Results. For periodontitis, 427 case and 136 control samples, and for VTE, 70 case and 63 control samples were included. The obtained PPI network had 1879 nodes and 2257 edges. Moreover, 782 immune genes and 28 cell types were included in the analysis. Over 90% of immune cells had different expressions in VTE and periodontitis. We obtained 12 significant pathways corresponding to crosstalk genes. CD3D, CSF3R, and CXCR4 acted as an immune gene and a crosstalk gene. We obtained a total of 12 shared biomarker crosstalk genes. Among those 12 biomarker crosstalk genes, 4 were immune genes (LGALS1, LSP1, SAMSN1, and WIPF1). Conclusion. Four biomarker crosstalk genes between periodontitis and VTE were also immune genes, i.e., LGALS1, LSP1, SAMSN1, and WIPF1. The findings of the current study need further validation and are a basis for development of biomarkers.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2020 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Prognostic Biomarkers ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

scholarly journals Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

2019 ◽  
Vol 48 (5) ◽  
pp. 030006051988726
Author(s):  
Yuting Zhang ◽  
Bo Shen ◽  
Liya Zhuge ◽  
Yong Xie
Keyword(s):  
Inflammatory Bowel Disease ◽  
Differentially Expressed Genes ◽  
Bowel Disease ◽  
Gene Expression Pattern ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Inflammatory Bowel

Objective We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD). Methods RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein–protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug–gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test. Results DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug–gene network identified these hub genes as potential drug targets. Conclusion Nineteen DEG were identified between the colon and ileum of IBD patients.

scholarly journals Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

2020 ◽  
Vol 25 (1) ◽  
Author(s):  
Xue Jiang ◽  
Zhijie Xu ◽  
Yuanyuan Du ◽  
Hongyu Chen
Keyword(s):  
Gene Ontology ◽  
Inflammatory Response ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Integrin Subunit ◽  
Ppi Network ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN. Results In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN. Conclusions We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

scholarly journals Identification of Potentially Therapeutic Target Genes of Hepatocellular Carcinoma

2020 ◽  
Vol 17 (3) ◽  
pp. 1053
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Gene Expression Profiles ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Differential Expression ◽  
Oncomine Database

Background: Hepatocellular carcinoma (HCC) is a major threat to public health. However, few effective therapeutic strategies exist. We aimed to identify potentially therapeutic target genes of HCC by analyzing three gene expression profiles. Methods: The gene expression profiles were analyzed with GEO2R, an interactive web tool for gene differential expression analysis, to identify common differentially expressed genes (DEGs). Functional enrichment analyses were then conducted followed by a protein-protein interaction (PPI) network construction with the common DEGs. The PPI network was employed to identify hub genes, and the expression level of the hub genes was validated via data mining the Oncomine database. Survival analysis was carried out to assess the prognosis of hub genes in HCC patients. Results: A total of 51 common up-regulated DEGs and 201 down-regulated DEGs were obtained after gene differential expression analysis of the profiles. Functional enrichment analyses indicated that these common DEGs are linked to a series of cancer events. We finally identified 10 hub genes, six of which (OIP5, ASPM, NUSAP1, UBE2C, CCNA2, and KIF20A) are reported as novel HCC hub genes. Data mining the Oncomine database validated that the hub genes have a significant high level of expression in HCC samples compared normal samples (t-test, p < 0.05). Survival analysis indicated that overexpression of the hub genes is associated with a significant reduction (p < 0.05) in survival time in HCC patients. Conclusions: We identified six novel HCC hub genes that might be therapeutic targets for the development of drugs for some HCC patients.

scholarly journals Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

2021 ◽  
Author(s):  
tiefeng cao ◽  
huimin shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Network Analysis ◽  
Survival Prediction ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background： Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

scholarly journals Immune Classification and Immune Landscape Analysis of Triple-Negative Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Shaojun Hu ◽  
Xiusheng Qu ◽  
Yu Jiao ◽  
Jiahui Hu ◽  
Bo Wang
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Drug Sensitivity ◽  
Triple Negative ◽  
Cox Regression ◽  
Landscape Analysis ◽  
Immune Gene ◽  
Immune Genes ◽  
New Perspective ◽  
Immune Related Genes

Background: To classify triple-negative breast cancer (TNBC) immunotyping using the public database, analyze the differences between subtypes in terms of clinical characteristics and explore the role and clinical significance of immune subtypes in TNBC immunotherapy.Methods: We downloaded TNBC data from the cBioPortal and GEO databases. The immune genes were grouped to obtain immune gene modules and annotate their biological functions. Log-rank tests and Cox regression were used to evaluate the prognosis of immune subtypes (IS). Drug sensitivity analysis was also performed for the differences among immune subtypes in immunotherapy and chemotherapy. In addition, dimension reduction analysis based on graph learning was utilized to reveal the internal structure of the immune system and visualize the distribution of patients.Results: Significant differences in prognosis were observed between subtypes (IS1, IS2, and IS3), with the best in IS3 and the worst in IS1. The sensitivity of IS3 to immunotherapy and chemotherapy was better than the other two subtypes. In addition, Immune landscape analysis found the intra-class heterogeneity of immune subtypes and further classified IS3 subtypes (IS3A and IS3B). Immune-related genes were divided into seven functional modules (The turquoise module has the worst prognosis). Five hub genes (RASSF5, CD8A, ICOS, IRF8, and CD247) were screened out as the final characteristic genes related to poor prognosis by low expression.Conclusions: The immune subtypes of TNBC were significantly different in prognosis, gene mutation, immune infiltration, drug sensitivity, and heterogeneity. We validated the independent role of immune subtypes in tumor progression and immunotherapy for TNBC. This study provides a new perspective for personalized immunotherapy and the prognosis evaluation of TNBC patients in the future.

scholarly journals Integrative Transcriptomic Analysis Reveals the Immune Mechanism for A CyHV-3-Resistant Common Carp Strain

2020 ◽  
Author(s):  
Zhiying Jia ◽  
Nan Wu ◽  
Xiaona Jiang ◽  
Heng Li ◽  
Jiaxin Sun ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Common Carp ◽  
Resistance Mechanism ◽  
Head Kidney ◽  
Immune Gene ◽  
Immune Mechanism ◽  
Protein Coding ◽  
Immune Genes ◽  
Comparison Groups ◽  
Immune Related Genes

ABSTRACTAnti-disease breeding is becoming the most promising solution to cyprinid herpesvirus-3 (CyHV-3) infection, the major threat to common carp aquaculture. Mortality studies suggested that a breeding strain of common carp is resistant to this disease. This study illustrates the immune mechanisms involved in anti-CyHV-3 ability. An integrative analysis of the protein-coding genes and long non-coding RNAs (lncRNAs) using transcriptomic data was also performed. Tissues from the head kidney of common carp were extracted at day 0 (the healthy control) and day 7 after CyHV-3 infection (the survivors), and used to analyze the transcriptome through both Illumina and Pac-bio sequencing. Following analysis of the Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms involved, the immune-related genes were merged. These genes were filtered using the current common carp immune gene library, and information on the immune process was detailed. Immune gene categories and their corresponding genes in different comparison groups were revealed. Also, the immunological Gene Ontology terms for lncRNA modulation were retained. The weighted gene co-expression network analysis was used for the regulation of immune genes lncRNA. The results demonstrated that the breeding carp strain develops marked resistance to CyHV-3 through a specific innate immune mechanism. The featured biological processes were autophagy, phagocytosis, cytotoxicity, and virus blockage by lectins and mucin 3 (MUC3). Moreover, the immune suppressive signals, such as suppression of interleukin 21 receptor (IL21R) on STAT3, PI3K mediated the inhibition of inflammation by dopamine upon infection, as well as the inhibition of NLR family CARD domain containing 3 (NLRC3) on STING during a steady state. Possible susceptible factors for CyHV-3, such as integrin beta 1 (ITGB1), toll-like receptor 18 (TLR18), and C-C motif chemokine ligand 4 (CCL4), were also revealed from the common strain. The results of this study suggested that the regulation of galectin 3 (LGALS3) and T cell leukemia homeobox 3 (TLX3) by lncRNA may play a role in the resistance mechanism. Therefore, immune factors that are immunogenetically insensitive or susceptible to CyHV-3 infection have been revealed.

scholarly journals Immune gene diversity in archaic and present-day humans

2018 ◽  
Author(s):  
David Reher ◽  
Felix M. Key ◽  
Aida M. Andrés ◽  
Janet Kelso
Keyword(s):  
Gene Diversity ◽  
Innate Immune ◽  
Immune Gene ◽  
Similar Reduction ◽  
Protein Coding ◽  
Immune Genes ◽  
Genome Wide ◽  
Show Evidence ◽  
Functional Consequences ◽  
Immune Related Genes

Genome-wide analyses of two Neandertals and a Denisovan have shown that these archaic humans had lower genetic heterozygosity than present-day people. A similar reduction in genetic diversity of protein-coding genes (gene diversity) was found in exome sequences of three Neandertals. Reduced gene diversity, and particularly in genes involved in immunity, may have important functional consequences. In fact, it has been suggested that reduced diversity in immune genes may have contributed to Neandertal extinction. We therefore explored gene diversity in different human groups and at different time points on the Neandertal lineage with a particular focus on the diversity of genes involved in innate immunity and genes of the Major Histocompatibility Complex (MHC).We find that the two Neandertals and the Denisovan have similar gene diversity, both significantly lower than any present-day human. This is true across gene categories, with no gene set showing an excess decrease in diversity compared to the genome-wide average. Innate immune-related genes show a similar reduction in diversity to other genes, both in present-day and archaic humans. There is also no observable decrease in gene diversity over time in Neandertals, suggesting that there may have been no ongoing reduction in gene diversity in later Neandertals, although this needs confirmation with a larger sample size. In both archaic and present-day humans, genes with the highest levels of diversity are enriched for MHC-related functions. In fact, in archaic humans the MHC genes show evidence of having retained more diversity than genes involved only in the innate immune system.

scholarly journals Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

2020 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Endometrial Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Uterine Corpus ◽  
Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis especially when at an advanced stage. In the present study, we explored the potential of an immune-related gene signature to predict overall survival in UCEC patients.Methods: We analyzed expression data of 616 UCEC patients from The Cancer Genome Atlas database and the International Cancer Genome Consortium as well as immune genes from the ImmPort database and identified the signature. We constructed a transcription factor regulatory network based on Cistrome databases and performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using Cox regression analysis then constructed a prognostic model. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.Results: Results indicated that the immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic model revealed a ten-gene prognosis signature , comprising PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC . This can be used as an independent tool to predict the prognosis of UCEC owing to the observed risk-score. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, and the expression of ten genes is associated with immune cell infiltrates.Conclusions: In summary, we present a 10-gene signature with the potential to predict the prognosis of UCEC. This is expected to guide future development of individualized treatment approaches.

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