scholarly journals Identification of Prognostic Immune Genes in Bladder Urothelial Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qisheng Su ◽  
Yan Sun ◽  
Zunni Zhang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Urothelial Carcinoma ◽  
Proportional Hazards ◽  
Differentially Expressed ◽  
Immune Gene ◽  
Hub Genes ◽  
Immune Genes ◽  
Proportional Hazards Regression ◽  
Bladder Urothelial Carcinoma ◽  
Gene Regulatory

Background. The aim of this study is to identify possible prognostic-related immune genes in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these genes. Methods. The Cancer Genome Atlas (TCGA) expression profile data and corresponding clinical traits were obtained. Differential gene analysis was performed using R software. Reactome was used to analyze the pathway of immune gene participation. The differentially expressed transcription factors and differentially expressed immune-related genes were extracted from the obtained list of differentially expressed genes, and the transcription factor-immune gene network was constructed. To analyze the relationship between immune genes and clinical traits of bladder urothelial carcinoma, a multifactor Cox proportional hazards regression model based on the expression of immune genes was established and validated. Results. Fifty-eight immune genes were identified to be associated with the prognosis of bladder urothelial carcinoma. These genes were enriched in Cytokine Signaling in Immune System, Signaling by Receptor Tyrosine Kinases, Interferon alpha/beta signaling, and other immune related pathways. Transcription factor-immune gene regulatory network was established, and EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were screened as hub genes in the network. The model calculated by the expression of 16 immune genes showed a good survival prediction ability (p<0.05 and AUC = 0.778). Conclusion. A transcription factor-immune gene regulatory network related to the prognosis of bladder urothelial carcinoma was established. EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were identified as hub genes in the network. The proportional hazards regression model constructed by 16 immune genes shows a good predictive ability for the prognosis of bladder urothelial carcinoma.

scholarly journals Development of an Immune-Related Risk Signature in Patients with Bladder Urothelial Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yaofei Jiang ◽  
Yibing Wang ◽  
Cong Li ◽  
Zhenhong Zou ◽  
Bo Liang
Keyword(s):  
Urothelial Carcinoma ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Clinical Value ◽  
Hazards Model ◽  
Kaplan Meier ◽  
Bladder Urothelial Carcinoma ◽  
Immune Related Genes

With recent advances in immunooncology and tumor microenvironment, the treatment landscape of bladder urothelial carcinoma has been changing dramatically. We aim to construct an immune gene-related signature which can predict BLCA patients’ overall survival. Transcriptomic data of BLCA patients was downloaded from The Cancer Genome Atlas database, and immune-related genes were downloaded from the Immunology Database and Analysis Portal database. Prognostic immune-related genes were identified. We then constructed and validated an immune gene-related signature. Tumor-related transcription factors were downloaded from the Cistrome database, and a network between them and prognostic immune-related genes was generated. Cox’s proportional hazards model and the Kaplan–Meier survival analysis were performed to assess our signature’s prognostic ability. Relationship between the signature and patients’ clinicopathologic features was then explored to validate its clinical value. We further downloaded concentration of six types of immune cells from the Tumor Immune Estimation Resource database to explore immune-related potential mechanisms of the signature.

scholarly journals Construction of Circular RNA–MicroRNA–Messenger RNA Regulatory Network of Recurrent Implantation Failure to Explore Its Potential Pathogenesis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiahuan Luo ◽  
Li Zhu ◽  
Ning Zhou ◽  
Yuanyuan Zhang ◽  
Lirong Zhang ◽  
...  
Keyword(s):  
Messenger Rna ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Implantation Failure ◽  
Ppi Network ◽  
Recurrent Implantation Failure ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Regulatory

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

scholarly journals Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

2020 ◽  
Vol 29 ◽  
pp. 096368972096517
Author(s):  
Changgang Guo ◽  
Ting Shao ◽  
Dadong Wei ◽  
Chunsheng Li ◽  
Fengjun Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Urothelial Carcinoma ◽  
Survival Rates ◽  
Disease Free Survival ◽  
Hub Genes ◽  
Poor Overall Survival ◽  
Free Survival ◽  
Bladder Urothelial Carcinoma ◽  
Muscle Invasive

Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

scholarly journals A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and Immport Data Sets

2020 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Yue An ◽  
Xue Yao ◽  
Mingjun Sun
Keyword(s):  
T Cells ◽  
Colorectal Adenocarcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Data Sets ◽  
Immune Gene ◽  
Immune Genes ◽  
Follicular Helper

Abstract BackgroundAs one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.MethodsThe differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.ConclusionWe developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

scholarly journals Construction of a Prognostic Model of Non-small Cell Lung Cancer Based on TCGA and ImmPort Databases and Analysis of Its Correlation With Tumor Microenvironment

2021 ◽  
Author(s):  
Peng Song ◽  
Xiaobin Ma ◽  
Dongliang Yang
Keyword(s):  
Risk Score ◽  
Risk Group ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Immune Gene ◽  
Overall Survival Rate ◽  
Small Cell Lung ◽  
Immune Genes ◽  
Prognosis Model ◽  
Nsclc Patients

Abstract PurposeBioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC.MethodsThe transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed.ResultsA total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P <0.05). After including 19 differentially expressed immune genes with P<0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan-Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic (ROC) model curve confirmed that the prediction model had a certain accuracy (AUC=0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment.ConclusionThe NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

scholarly journals A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Dongliang Yang ◽  
Xiaobin Ma ◽  
Peng Song
Keyword(s):  
Risk Score ◽  
Risk Group ◽  
Assessment Model ◽  
Differentially Expressed ◽  
Immune Gene ◽  
Overall Survival Rate ◽  
Small Cell Lung ◽  
Immune Genes ◽  
Prognosis Model ◽  
Nsclc Patients

AbstractBioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan–Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

scholarly journals Development and validation of a prognostic nomogram for lower-grade glioma based on an autophagy-related lncRNA signature

2022 ◽  
Author(s):  
Jiazhe Lin ◽  
Nuan Lin ◽  
Wei-jiang Zhao
Keyword(s):  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lower Grade ◽  
Lasso Regression ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Kaplan Meier

IntroductionGliomas account for 75% of the primary malignant brain tumors. The prognosis and treatment planning vary in lower-grade gliomas (LGG) due to their heterogeneous clinical behaviors. The dysregulation of autophagy-related (ATG) lncRNAs plays a crucial role in LGG. We aimed to develop and validate an ATG lncRNA risk signature, and a survival nomogram with integration of novel prognostic for LGG patients.Material and methodsDifferentially expressed ATG lncRNAs were screened out based on TCGA and GTEx RNA-seq databases. ATG lncRNA prognostic signature was then established by Kaplan–Meier, univariate Cox proportional hazards regression, Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. Kaplan–Meier, univariate Cox regression and multivariate Cox proportional hazards regression were used to screen out clinical and molecular variables. A nomogram was developed and internally validated by ROC and calibration plots.ResultsAn ATG lncRNA risk signature was constructed with six differentially expressed lncRNAs (LINC00599, LINC02609, AC021739.2, AL118505.1, AL354892.2, and AL590666.2). Based on the risk signature, a nomogram was developed by addition of the significant prognostic clinical variables (age and grade) and molecular variables (IDH status and MGMT status).ConclusionsWe identified an ATG lncRNA risk signature and develop a nomogram for individualized survival prediction in LGG patients. A user-friendly free online calculator to facilitate the use of this nomogram among clinicians is also provided: https://linstu2009.shinyapps.io/LGGPRODICTORapp/?_ga=2.3154800.1506830296.1588641469-159983587.1588641469.

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