Dataset Information

Screening and Identifying Immune-Related Cells and Genes in the Tumor Microenvironment of Bladder Urothelial Carcinoma: Based on TCGA Database and Bioinformatics.

ABSTRACT: Bladder cancer is the most common cancer of the urinary system and its treatment has scarcely progressed for nearly 30 years. Advances in checkpoint inhibitor research have seemingly provided a new approach for treatment. However, there have been issues predicting immunotherapeutic biomarkers and identifying new therapeutic targets. We downloaded the gene expression profile and clinical data of 408 cases bladder urinary cancer from the Cancer Genome Atlas (TCGA) portal, and the abundance ratio of immune cells for each sample was obtained via the "Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)" algorithm. Then, four survival-related immune cells were obtained via Kaplan-Meier survival analysis, and 933 immune-related genes were obtained via a variance analysis. Enrichment, protein-protein interaction, and co-expression analyses were performed for these genes. Lastly, 4 survival-related immune cells and 24 hub genes were identified, four of which were related to overall survival. More importantly, these immune cells and genes were closely related to the clinical features. These cells and genes may have research value and clinical application in bladder cancer immunotherapy. Our study not only provides cell and gene targets for bladder cancer immunotherapy, but also provides new ideas for researchers to explore the immunotherapy of various tumors.

SUBMITTER: Cao J

PROVIDER: S-EPMC6974676 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Screening and Identifying Immune-Related Cells and Genes in the Tumor Microenvironment of Bladder Urothelial Carcinoma: Based on TCGA Database and Bioinformatics.

Cao Jinlong J Yang Xin X Li Jianpeng J Wu Hao H Li Pan P Yao Zhiqiang Z Dong Zhichun Z Tian Junqiang J

Frontiers in oncology 20200115

Bladder cancer is the most common cancer of the urinary system and its treatment has scarcely progressed for nearly 30 years. Advances in checkpoint inhibitor research have seemingly provided a new approach for treatment. However, there have been issues predicting immunotherapeutic biomarkers and identifying new therapeutic targets. We downloaded the gene expression profile and clinical data of 408 cases bladder urinary cancer from the Cancer Genome Atlas (TCGA) portal, and the abundance ratio o ...[more]

PMID: 32010623

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Project description:BackgroundBladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment.MethodsImmune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database.ResultsA higher stromal score was associated with female (vs. malep = 0.037), age > 65 (vs.age ≤ 65 p = 0.015), T3/4 (vs. T1/2,p < 0.001), N status(p = 0.016), and pathological high grade (vs. low gradeP < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2.ConclusionsOur study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment.

Project description:BackgroundTo identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.MethodsWe downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.ResultsGene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.ConclusionsWe identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Project description:BackgroundUrothelial carcinoma of the bladder (UCB) is the most prevalent malignant tumor of the urinary system worldwide, which has a significant recurrence rate despite multiple treatment options available. As a unique and novel copper-dependent programmed cell death mechanism, the comprehensive impact of cuproptosis on the tumor immune microenvironment, clinicopathological characteristics and the prognosis of patients remains largely unclear.MethodsA total of 568 UCB samples were thoroughly examined for cuproptosis patterns using data downloaded from TCGA and GEO, based on 10 cuproptosis-related genes reported previously. Then, the univariate COX regression analysis was performed on the genes that differed across the various patterns. To measure individual cuproptosis pattern, a cuproptosis score system was constructed using a principal component analysis algorithm. To validate the scoring system, immunohistochemical staining was performed on tumor tissues with different pathological grades, and experiments in vitro were conducted about the differentially expressed genes related to prognosis. Finally, the capacity of scoring system to predict the response to immunotherapy was verified by using data from IMvigor 210 cohort.ResultsFour unique cuproptosis clusters and two gene clusters were finally found by the investigation. The clinical features and prognosis of patients, as well as the mRNA transcriptome, pathway enrichment, and immune cell infiltration in TME, varied dramatically between various cuproptosis clusters and gene clusters. To identify individual cuproptosis patterns in UCB patients, we also established a cuproptosis scoring system. After validation with multiple methods, it was indicated that the score system could predict the prognosis of UCB patients and was significantly connected to clinical features such TNM category, tumor grade, molecular type and ultimate survival status. The clinical outcomes of UCB patients were predicted effectively according to the tumor mutation burden in conjunction with the scoring system. Furthermore, we found that the cuproptosis score had a significant correlation with the response to immunotherapy and the sensitivity to chemotherapy.ConclusionThis study revealed the potential impact of cuproptosis on the UCB tumor immune microenvironment and clinical pathological characteristics. The cuproptosis score system could effectively predict the prognosis of patients and the response to chemotherapy and immunotherapy.

Project description:BackgroundThe tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.MethodsMolecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.ResultsWe identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).ConclusionsWe successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

Dataset Information

Screening and Identifying Immune-Related Cells and Genes in the Tumor Microenvironment of Bladder Urothelial Carcinoma: Based on TCGA Database and Bioinformatics.

Publications

Screening and Identifying Immune-Related Cells and Genes in the Tumor Microenvironment of Bladder Urothelial Carcinoma: Based on TCGA Database and Bioinformatics.

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