Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

Qiu, Huaide; Hu, Xiaorong; He, Chuan; Yu, Bin; Li, Yongqiang; Li, Jianan

doi:10.3389/fgene.2020.00012

Cited by 77 publications

(73 citation statements)

References 53 publications

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“…Jinlong Cao et al identi ed CDH7, LUZP1, PSD2, and UGT2B15 were related to survival of BLCA patients [38]. Huaide Qiu et al demonstrated that RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2 classify BLCA patients into different groups with different survival rates [39]. All these reported prognostic IRGs does not overlap with the IRGs involved in the present signature.…”

Section: Discussionmentioning

confidence: 88%

Integrated Analysis Identifies and Validates a Novel Signature Based on Immune Related Gene Predicting Survival in Bladder Urothelial Carcinoma

Mao

Chen

2020

Preprint

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BackgroundThis study aimed to develop a prognostic signature based on immune related gene(IRG) predicting survival for patients with bladder urothelial carcinoma(BLCA).Methods1534 IRG’s expression data of 996 BLCA patients form Gene Expression Omnibus database and The Cancer Genome Atlas database were used for development and validation of the prognostic signature. Univariate Cox regression model and Random Survival Forest Variable Hunting algorithm were employed to achieve the variable selection. The independently prognostic ability of the signature was validated by Multivariate Cox model and Kaplan–Meier analysis in independent datasets. A nomogram was established to improve prognosis stratification. The relationship between tumor-infiltrating immune cells and the signature was analyzed using data retrieved from EPIC resource.FindingsA prognostic model consisting of 10 IRGs was developed as our immune signature. Based on this signature, patients were separated into low- and high- risk groups with different survival in both training and validation sets(HR : range from 1.1 [95% CI: 1–1.2; p = 0.038] to 1.3 [95% CI: 1.3–1.5; p <0.001]). Multivariable analyses demonstrated that this signature was an independent prognostic predictor and was strongly associated with important clinicopathological factors. The signature also showed a significantly positive correlation with immune checkpoint molecules, and had a superior prognostic value compared with some important targets. In addition, our signature was found to correlate the enhancement of tumor microenvironment positively.ConclusionsThis signature predicts prognosis for BLCA patients, which may promote individualized treatment and provide potential targets for immunotherapy.

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Section: Discussionmentioning

confidence: 88%

Integrated Analysis Identifies and Validates a Novel Signature Based on Immune Related Gene Predicting Survival in Bladder Urothelial Carcinoma

Mao

Chen

2020

Preprint

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“…Transcriptome sequencing results have been widely used in the prognostic model establishment for tumor patients. Prognostic models based on immune-related genes have been developed and proved to have excellent predictive e cacy in bladder cancer patients [16,17]. Using lncRNA to construct a prognostic model may be an important supplement to the prognosis prediction of bladder cancer.…”

Section: Discussionmentioning

confidence: 99%

Development and verification of immune-related long non-coding RNA prognostic signature in bladder cancer

Lai¹,

Wu²,

Li³

et al. 2020

Preprint

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Background: Bladder cancer is the second most common malignant tumor in urogenital system. The research aimed to investigate the prognostic role of immune-related long non-coding RNA (lncRNA) in bladder cancer. Methods: We extracted 411 bladder cancer samples from The Cancer Genome Atlas database. Single-sample gene set enrichment analysis was employed to assess the immune cell infiltration of these samples. We recognized differentially expressed lncRNAs between tumors and paracancerous tissues, and differentially expressed lncRNAs between the high and low immune cell infiltration groups. Venn diagram analysis detected differentially expressed lncRNAs that intersected the above groups. LncRNAs with prognostic significance were identified by regression analysis and survival analysis. Multivariate Cox analysis was used to establish the risk score model. The nomogram was established and evaluated by receiver operating characteristic (ROC) curve analysis, concordance index (C-index) analysis, calibration chart, and decision curve analysis (DCA). Additionally, we performed gene set enrichment analysis to explore the potential functions of the screened lncRNAs in tumor pathogenesis.Results: Three hundred and twenty differentially expressed lncRNAs were recognized. We randomly divided patients into the training data set and the testing data set at a 2: 1 ratio. In the training data set, 9 immune-related lncRNAs with prognostic significance were identified. The risk score model was constructed to classify patients as high- and low-risk cohorts. Patients in the low-risk cohort had better survival outcomes than those in the high-risk cohort. The nomogram was established based on the indicators including age, gender, TNM stage, and risk score. The model’s predictive performance was confirmed by ROC curve analysis, C-index analysis, calibration chart, and DCA. The testing data set also achieved similar results. Bioinformatics analysis suggested that the 9-lncRNA signature was involved in modulation of various immune responses, antigen processing and presentation, and T cell receptor signaling pathway.Conclusions: The immune-related lncRNAs have the potential to predict the prognosis of bladder cancer and may play a key role in bladder cancer biology.Trial registration: It was a retrospective study and the gene expression data were obtained from the TCGA database. Trial registration was not needed.

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“…Individuals were separated into high-risk or low-risk groups according to median risk score. Subsequently, survival analysis and receiver operating characteristic (ROC) analysis were performed as reported [5]. The expression level of the ve selected PDEARGs between two risk groups was analyzed with R software as well.…”

Section: Prognostic Signature Construction and Validationmentioning

confidence: 99%

“…However, the predictive accuracy of commonly used predictors, including patients' general condition, histological grade and pathological stage, is insu cient to identify the patients who need invasive treatment [4]. Recently, biomarker-based signature is regarded as a promising tool to predict the prognosis of BLCA patients and assist clinical decision [5].…”

Section: Introductionmentioning

confidence: 99%

Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

Zhang

Wang²,

et al. 2020

Preprint

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Background Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure better prognosis. However, the predictive accuracy of commonly used predictors, including patients’ general condition, histological grade and pathological stage, is insufficient to identify the patients who need invasive treatment. Autophagy is regarded as a vital factor in maintaining mitochondrial function and energy homeostasis in cancer cells. Whether autophagy-related genes (ARGs) can predict the prognosis of BLCA patients deserves to be investigated. Methods Based on BLCA data retrieved from the Cancer Genome Atlas (TCGA) and ARGs list obtained from the Human Autophagy Database (HADb) website, we identified prognosis-related differentially expressed ARGs (PDEARGs) through Wilcox text and constructed a PDEARGs-based prognostic model through multivariate Cox regression analysis. The predictive accuracy, independent forecasting capability, and the correlation between present model and clinical variables or tumor microenvironment (TME) were evaluated through R software. Enrichment analysis of PDEARGs was performed to explore the underlying mechanism, and a systematic prognostic signature with nomogram was constructed by integrating clinical variables and aforementioned PDEARGs-based model. Results We identified several PDEARGs and constructed a PDEARGs-based prognostic model, which could precisely predict the prognosis of BLCA patients. Then, we found that the risk score generated by PDEARGs-based model could effectively reflect deteriorated clinical variables and tumor-promoting microenvironment. Additionally, several immune-related gene ontology (GO) terms were significantly enriched by PDEARGs, which might provide insights for present model and propose potential therapeutic targets for BLCA patients. Finally, a systematic prognostic signature with promoted clinical utility and predictive accuracy was constructed to assist clinician decision. Conclusion PDEARGs are valuable prognostic predictor and potential therapeutic targets for BLCA patients.

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Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

Cited by 77 publications

References 53 publications

Integrated Analysis Identifies and Validates a Novel Signature Based on Immune Related Gene Predicting Survival in Bladder Urothelial Carcinoma

Integrated Analysis Identifies and Validates a Novel Signature Based on Immune Related Gene Predicting Survival in Bladder Urothelial Carcinoma

Development and verification of immune-related long non-coding RNA prognostic signature in bladder cancer

Prognostic value and underlying mechanism of autophagy-related genes in bladder cancer

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