Dataset Information

An EMT-related gene signature for the prognosis of human bladder cancer.

ABSTRACT: The transition from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial-mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT-related gene signature in BLCA. The patients with high risk scores had significantly worse overall survival (OS) and disease-free survival (DFS) than those with low risk scores. The EMT-related gene signature also performed favourably in the accuracy of prognosis and in the subtype survival analysis. Univariate and multivariate Cox regression analyses demonstrated that the EMT-related gene signature, pathological N stage and age were independent prognostic factors for predicting survival in BLCA patients. Furthermore, the predictive nomogram model was able to effectively predict the outcome of BLCA patients by appropriately stratifying the risk score. In conclusion, we developed a novel EMT-related gene signature that has tumour-promoting effects, acts as a negative independent prognostic factor and might facilitate personalized counselling and treatment in BLCA.

SUBMITTER: Cao R

PROVIDER: S-EPMC6933372 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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An EMT-related gene signature for the prognosis of human bladder cancer.

Cao Rui R Yuan Lushun L Ma Bo B Wang Gang G Qiu Wei W Tian Ye Y

Journal of cellular and molecular medicine 20191028 1

The transition from non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial-mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT-related gene signature in BLCA. The patients with high ...[more]

PMID: 31657881

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Project description:BackgroundAccumulating evidences demonstrated tumor microenvironment (TME) of bladder cancer (BLCA) may play a pivotal role in modulating tumorigenesis, progression, and alteration of biological features. Currently we aimed to establish a prognostic model based on TME-related gene expression for guiding clinical management of BLCA.MethodsWe employed ESTIMATE algorithm to evaluate TME cell infiltration in BLCA. The RNA-Seq data from The Cancer Genome Atlas (TCGA) database was used to screen out differentially expressed genes (DEGs). Underlying relationship between co-expression modules and TME was investigated via Weighted gene co-expression network analysis (WGCNA). COX regression and the least absolute shrinkage and selection operator (LASSO) analysis were applied for screening prognostic hub gene and establishing a risk predictive model. BLCA specimens and adjacent tissues from patients were obtained from patients. Bladder cancer (T24, EJ-m3) and bladder uroepithelial cell line (SVHUC1) were used for genes validation. qRT-PCR was employed to validate genes mRNA level in tissues and cell lines.Results365 BLCA samples and 19 adjacent normal samples were selected for identifying DEGs. 2141 DEGs were identified and used to construct co-expression network. Four modules (magenta, brown, yellow, purple) were regarded as TME regulatory modules through WGCNA and GO analysis. Furthermore, seven hub genes (ACAP1, ADAMTS9, TAP1, IFIT3, FBN1, FSTL1, COL6A2) were screened out to establish a risk predictive model via COX and LASSO regression. Survival analysis and ROC curve analysis indicated our predictive model had good performance on evaluating patients prognosis in different subgroup of BLCA. qRT-PCR result showed upregulation of ACAP1, IFIT3, TAP1 and downregulation of ADAMTS9, COL6A2, FSTL1,FBN1 in BLCA specimens and cell lines.ConclusionsOur study firstly integrated multiple TME-related genes to set up a risk predictive model. This model could accurately predict BLCA progression and prognosis, which offers clinical implication for risk stratification, immunotherapy drug screen and therapeutic decision.

Project description:BackgroundGlobally, endometrial cancer is the fourth most common malignant tumor in women and the number of women being diagnosed is increasing. Tumor progression is strongly related to the cell survival-promoting functions of autophagy. We explored the relationship between endometrial cancer prognoses and the expression of autophagy genes using human autophagy databases.MethodsThe Cancer Genome Atlas was used to identify autophagy related genes (ARGs) that were differentially expressed in endometrial cancer tissue compared to healthy endometrial tissue. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were referenced to identify important biological functions and signaling pathways related to these differentially expressed ARGs. A prognostic model for endometrial cancer was constructed using univariate and multivariate Cox, and Least Absolute Shrinkage and Selection Operator regression analysis. Endometrial cancer patients were divided into high- and low-risk groups according to risk scores. Survival and receiver operating characteristic (ROC) curves were plotted for these patients to assess the accuracy of the prognostic model. Using immunohistochemistry the protein levels of the genes associated with risk were assessed.ResultsWe determined 37 ARGs were differentially expressed between endometrial cancer and healthy tissues. These genes were enriched in the biological processes and signaling pathways related to autophagy. Four ARGs (CDKN2A, PTK6, ERBB2 and BIRC5) were selected to establish a prognostic model of endometrial cancer. Kaplan-Meier survival analysis suggested that high-risk groups have significantly shorter survival times than low-risk groups. The area under the ROC curve indicated that the prognostic model for survival prediction was relatively accurate. Immunohistochemistry suggested that among the four ARGs the protein levels of CDKN2A, PTK6, ERBB2, and BIRC5 were higher in endometrial cancer than healthy endometrial tissue.ConclusionsOur prognostic model assessing four ARGs (CDKN2A, PTK6, ERBB2, and BIRC5) suggested their potential as independent predictive biomarkers and therapeutic targets for endometrial cancer.

Project description:BackgroundBladder cancer is an aggressive and heterogeneous disease associated with high morbidity and mortality. And poliovirus receptor (PVR or CD155) played crucial roles in tumor immune microenvironment and cancer development. However, their association remains obscure.MethodsA total of 797 patients from TCGA and GEO databases were employed in our study, in which 285 cases were set as the training cohort and 512 were defined as the validation cohort. Our own Xiangya cohort with 57 samples was also used for the validation. Survival differences were evaluated by Kaplan-Meier analysis between groups. The immune infiltration was evaluated by ESTIMATE, TIMER, and CIBERSORT algorithms. The risk signature was constructed by LASSO Cox regression analysis. And a nomogram model was generated subsequent to the multivariate Cox proportional hazards analysis to predict 3- and 5-year survival of patients with bladder cancer.ResultsPVR was overexpressed across various cancers including bladder cancer and related to poorer overall survival in bladder urothelial carcinoma (BLCA). Samples with higher World Health Organization (WHO) grade or higher tumor stage tended to express higher level of PVR. And PVR-related genes were involved in several immune processes and oncological pathways. When the patients were divided into low- and high-risk groups based on their risk scores, we found that patients in the high-risk group had shorter overall survival time. Besides, samples with high risk were consistently correlated with tumor hallmarks and higher abundance of immune infiltration. Additionally, chemotherapy showed potent efficacy in high-risk group. Moreover, a nomogram including clinicopathologic features and the established risk signature could predict 3- and 5-year survival in patients with bladder cancer.ConclusionOur study revealed that PVR was overexpressed and related to poor prognosis in bladder cancer. A risk signature and nomogram model based on PVR-related genes could predict the prognosis and therapeutic efficacy and were also associated with the immune infiltration in bladder cancer.

Project description:Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients. Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed. Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel. Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

Dataset Information

An EMT-related gene signature for the prognosis of human bladder cancer.

Publications

An EMT-related gene signature for the prognosis of human bladder cancer.

Similar Datasets

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