Dataset Information

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer.

ABSTRACT: To identify an immune-related prognostic signature based on long non-coding RNAs (lncRNAs) and find immunotherapeutic targets for bladder urothelial carcinoma, we downloaded RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis demonstrated bladder urothelial carcinoma was related to immune-related functions. We obtained 332 immune-related genes and 262 lncRNAs targeting immune-related genes. We constructed a signature based on eight lncRNAs in training cohort. Patients were classified as high-risk and low-risk according to signature risk score. High-risk patients had poor overall survival compared with low-risk patients (P < 0.001). Multivariate Cox regression suggested the signature was an independent prognostic indicator. The findings were further validated in testing, entire TCGA and external validation cohorts. Gene set enrichment analysis indicated significant enrichment of immune-related phenotype in high-risk group. Immunohistochemistry and online analyses validated the functions of 4 key immune-related genes (LIG1, TBX1, CTSG and CXCL12) in bladder urothelial carcinoma. Nomogram proved to be a good classifier for muscle-invasive bladder cancer through combining the signature. In conclusion, our immune-related prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for muscle-invasive bladder cancer.

SUBMITTER: Song Y

PROVIDER: S-EPMC7343518 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer.

Song Yuxuan Y Jin Donghui D Chen Jingyi J Luo Zhiwen Z Chen Guangyuan G Yang Yongjiao Y Liu Xiaoqiang X

Aging 20200624 12

To identify an immune-related prognostic signature based on long non-coding RNAs (lncRNAs) and find immunotherapeutic targets for bladder urothelial carcinoma, we downloaded RNA-sequencing data from The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis demonstrated bladder urothelial carcinoma was related to immune-related functions. We obtained 332 immune-related genes and 262 lncRNAs targeting immune-related genes. We constructed a signature based on eight lncRNAs in training ...[more]

PMID: 32579540

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Project description:BackgroundBladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients.MethodsThe lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < - 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features.ResultsWe established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance.ConclusionsOur results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.

Project description:BackgroundMuscle-invasive bladder cancer (MIBC) patients are subject to unfavorable treatment options and a high recurrence rate. The status of TP53 mutations played an essential role in the progression and the prognosis of MIBC. The present study proposed to investigate the association between TP53 mutations and immunophenotype in MIBC.ResultsWe established an immune prognostic model (IPM) ground on the immune-associated genes derived from variation analysis between wild-type TP53 and mutated TP53 TCGA-MIBC patients, and validated in another cohort from GEO database. Based on IPM, we divided MIBC patients into low and high risk subgroups. The high risk MIBC patients had higher proportions of macrophages M1, and lower proportions of T cells regulatory (Tregs) and activated dendritic cells than the low risk MIBC patients. Moreover, the expression of immune checkpoints genes (PD1, CTLA4, LAG3, HAVCR2 and TIGIT) was higher in the high risk patients than the low risk patients. In clinical application, IPM exhibited better survival prediction than conventional clinical characteristics.ConclusionsOur investigation presented practical prognostic significance for MIBC patients and displayed the overarching landscape of the immune response in the MIBC microenvironment.MethodsData were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) analysis between the TP53 mutated and wild-type MIBC patients was conducted. The CIBERSORT algorithm was performed to evaluate the proportion of immune cell types. Gene expression profiles from the TCGA and GEO were used as training and testing cohorts to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors.

Dataset Information

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer.

Publications

Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer.

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