Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

Dong, Bingqi; Liang, Jiaming; Li, Ding; Song, Wenping; Zhao, Shufang; Ma, Yongkang; Song, Jinbo; Zhu, Mingkai; Yang, Tiejun

doi:10.3389/fgene.2021.696912

Cited by 8 publications

(7 citation statements)

References 41 publications

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“…Finally, our model focuses on the prognosis of BLCA, TME, and effectiveness of immunotherapy compared with other models. We found that our model was more valuable in facilitating individualized treatment showing better AUC value and C-index compared to previous BCLA signatures that were studied under the modi ed conditions of ferroptosis (6), m6A (37), hypoxia (38), and immunity (39).…”

Section: Discussionmentioning

confidence: 87%

“…The results of ROC analysis in the TCGA cohort showed AUC values of 0.783, 0.823, and 0.811 at 1, 3, and 5 years, respectively (Figure 5A), while those of the GSE32894 cohort showed AUC values of 0.772, 0.709, and 0.705 at 1, 3, and 5 years, respectively (Figure 5B), thereby demonstrating the high accuracy of our model. In addition, the results of t-SNE analysis also demonstrated that there were signi cant differences in the distribution of patients based on LAG-score (Figure 5C-D (39) in BLCA patients. C-index, also known as consistency index, it and ROC curve are common methods to evaluate the model's accuracy (40,41).…”

Section: A Risk Signature Based On Lactate-related Clustersmentioning

confidence: 77%

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A Novel Defined Risk Signature Based on Lactate-Related Genes can Predicts Prognosis of Bladder Cancer

Jin

et al. 2022

Preprint

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Background Lactate is an important carbon source for cell metabolism as well as a signaling molecule in normal, chronic inflammatory and cancer tissues. However, the relationship between lactate and bladder cancer (BLCA) has not been studied yet. Methods Expression profiles of the patients with bladder cancer from The Cancer Genome Atlas were divided into different clusters using non-negative matrix factorization clustering to obtain differentially expressed genes (DEGs). Univariate Cox analysis, Lasso-Cox analysis, Kaplan-Meier survival analysis, and time-dependent receiver operating characteristics curves were used to developed and validated a prognostic signature for the survival rate of patients. "CIBERSORT" and "ESTIMATE" algorithms were used to quantify the immunocyte content and evaluate tumor purity for different clusters and subgroups. Online tool "Tumor Immune Dysfunction and Exclusion" was used to predict the immune escape ability of patients in different scoring subgroups after immunotherapy. Results The different clusters according to the expression pattern of lactate-associated genes (LAGs) had significant differences in the survival rate and the tumor microenvironment. Besides, a risk signature constructed by DEGs based on different clusters and verified by the GSE32894 dataset could not only significantly distinguish between the prognosis and clinical features of patients with different scores but was also found to be related to the tumor microenvironment and immunotherapy. Finally, a high accuracy nomogram model was established. Conclusions Overall, this research has revealed the relationship between lactate and patient prognosis, tumor microenvironment, and immunotherapy, which may provide novel prognostic biomarkers for patients with BLCA.

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Section: Discussionmentioning

confidence: 87%

Section: A Risk Signature Based On Lactate-related Clustersmentioning

confidence: 77%

A Novel Defined Risk Signature Based on Lactate-Related Genes can Predicts Prognosis of Bladder Cancer

Jin

et al. 2022

Preprint

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“…inhibitors have been reported to be evaluated in several tumor treatments with promising results [24][25][26] . In this study, we also evaluated the correlation between risk models and immunotherapy response in bladder cancer, and the results showed that the clinical bene ts of immunotherapy against PD-1 and CTLA4 differed signi cantly in the low-and high-risk groups, and also indicated that the risk model we constructed could accurately re ect the therapeutic effects of immune checkpoint blockade.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Shen

Yan

Yang

et al. 2023

Funct Integr Genomics

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BackgroundAutophagy has an important association with tumorigenesis, progression and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagyrelated genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. MethodsWe rst downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and nally obtained differential prognosisassociated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by Receiver operating characteristic (ROC) and calibration curves, and then Nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into high and low risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell in ltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. ResultsSix risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STM3 and ANXA2) were nally screened by lasso regression algorithm and multivariate COX regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had signi cantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation Page 3/29 analysis (GSVA) and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLA) wer...

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“…A series of immune-related prognostic gene signatures for bladder cancers have been constructed based on the public datasets obtained from TCGA and GEO databases [36][37][38][39][40][41][42][43]. They usually employed the TCGA-BLCA dataset to build the model and then validated it with a single external GEO dataset.…”

Section: Discussionmentioning

confidence: 99%

An Immune-Related Clinical-Gene-Risk-Score (CGRS) prognosis Model for Bladder Cancer

Liu

Duan

Wang

et al. 2022

Preprint

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Background Immunotherapy for bladder cancer is prevalent, while efficacy is uncertain. To predict the outcomes of immunotherapy in bladder cancer patients, an immune-related prognosis model was conducted in this study. Methods Single sample Gene Set Enrichment Analysis was used to delineate the landscape of the tumor immune microenvironment based on two independent public databases. Hierarchical clustering analysis and t-distributed Stochastic Neighbor Embedding cluster map were used to classify the bladder cancers in the training cohort as well as the validating cohort into the immune high and immune low subtypes. Differentially expression analysis and The least absolute shrinkage and selection operator regression analysis identified 23 differentially expressed immune-related genes between the two subtype patients to build a prognostic gene risk score model. Further combining the model with other clinicopathologic signatures, a Clinical-Gene-Risk-Score (CGRS) prognosis model was developed. Results Validations of the two models in independent cohorts revealed good accuracy. Conclusions Our model could provide prognostic indicators and potential immunotherapeutic targets for bladder cancers.

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Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

Cited by 8 publications

References 41 publications

A Novel Defined Risk Signature Based on Lactate-Related Genes can Predicts Prognosis of Bladder Cancer

A Novel Defined Risk Signature Based on Lactate-Related Genes can Predicts Prognosis of Bladder Cancer

Construction and validation of a bladder cancer risk model based on autophagy-related genes

An Immune-Related Clinical-Gene-Risk-Score (CGRS) prognosis Model for Bladder Cancer

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