Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer

Lv, Jia; Zhu, Yongze; Ji, Alin; Zhang, Qi; Liao, Guodong

doi:10.1042/bsr20194337

Cited by 97 publications

(83 citation statements)

References 50 publications

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“…KLF15 has been reported to inhibit cell growth in lung adenocarcinoma (37), gastric cancer (38), and colorectal cancer (39), and can be used to predict prognosis. High ADRA2A expression was associated with poor overall survival for breast (40) and bladder cancer (41). As a genetic marker, RNASEL has been linked to lethal prostate cancer (42).…”

Section: Discussionmentioning

confidence: 99%

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Wang

Xue

et al. 2020

Front. Oncol.

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Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid-and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high-and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high-and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.

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Section: Discussionmentioning

confidence: 99%

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Wang

Xue

et al. 2020

Front. Oncol.

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“…Moreover, TMB was independent of the expression of PD-L1, which was the strongest feature associated with a therapeutic effect in multivariate analysis (37). Lv et al (2020) revealed that the prognosis of bladder cancer patients with higher TMB was better so that TMB was considered to be a powerful predictor of tumor behavior and response to immunotherapy (38). However, in some types of cancer, such as clear cell renal cell carcinoma, patients with high TMB levels showed worse survival outcomes (24).…”

Section: Discussionmentioning

confidence: 99%

Biological Omics Analysis of Tumor Mutation Burden Combined with Immune Infiltrates in Uterine Corpus Endometrial Carcinoma

Zhuang

Liu

Huang

et al. 2020

Preprint

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Background: In various malignancies, whether tumor mutation burden (TMB) is associated with improved survival outcomes or enhanced immunotherapy remains controversial. We are committed to exploring the prognosis of TMB and its potential association with immune infiltration of uterine corpus endometrial cancer (UCEC).Methods: We downloaded transcriptome data and somatic mutation data from the Cancer Genome Atlas (TCGA) database, and visualized the mutation profiles using the "maftools" package. TMB was calculated and the samples were divided into two groups by the median. Kaplan-Meier analysis and Wilcoxon test were used to compare the differences in survival and clinicopathological characteristics. Furthermore, we performed functional enrichment analysis to screen for significant contributing pathways. The "CIBERSORT" package was used to estimate the abundance of immune components. Differential analysis was performed using the "limma" package to compare gene expression profiles. Multivariate analysis was used to identify risk genes related to TMB. Based on these genes, a risk model was established, and the receiver operating characteristic (ROC) curve was drawn to assess the predictive accuracy. Finally, we evaluated the relationship between risk genes and immune infiltration using the Timer database.Results: The mutation data included 542 UCEC patients. The waterfall plot summarized the specific mutation information. Higher TMB levels indicated improved overall survival (OS) (p =0.048) and were associated with advanced grades. Pathway analysis showed that the differential signals were enriched in multiple immune-related crosstalks. We screened 108 differentially expressed genes in two TMB groups and identified four risk-related genes. The model based on these four risk genes had good predictive value, the area under the curve (AUC) = 0.670, and patients with higher risk scores showed worse OS (p <0.001). Additionally, CD8 T cells memory-activated, CD4 T cells, and M1 macrophages in the high TMB group showed higher infiltrates, while regulatory T cells (Tregs) and M2 macrophages showed lower infiltrates.Conclusions: Higher TMB was associated with better survival outcomes and increased the immune infiltration in the tumor microenvironment of UCEC.

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“…The ‘maftools’ software package was utilized to analyze and visualize the immune cell biomarker-associated mutation data [ 103 ]. The immune cell biomarker-associated mutation data were compared between one group and the other groups using the chi-square test.…”

Section: Methodsmentioning

confidence: 99%

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

Luo

Vögeli

2020

Cancers

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Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy.

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Mining TCGA database for tumor mutation burden and their clinical significance in bladder cancer

Cited by 97 publications

References 50 publications

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Biological Omics Analysis of Tumor Mutation Burden Combined with Immune Infiltrates in Uterine Corpus Endometrial Carcinoma

A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes

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