Dataset Information

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer.

ABSTRACT:

Abstract

Bladder cancer (BC) is one of the most common malignancies worldwide. Several biomarkers related to the prognosis of patients with BC have previously been identified. However, these prognostic models use only one gene and are thus not reliable or accurate enough. The purpose of our study was to develop an innovative gene signature that has greater prognostic value in BC. So, in this study, we performed mRNA expression profiling of glycolysis-related genes in BC (n?=?407) cohorts by mining data from The Cancer Genome Atlas (TCGA) database. The glycolysis-related gene sets were confirmed using the Gene Set Enrichment Analysis (GSEA). Using Cox regression analysis, a risk score staging model was built based on the genes that were determined to be significantly associated with BC outcome. Eventually, the system of risk score was structured to predict a patient's survival, and we identified four genes (CHPF, AK3, GALK1, and NUP188) that were associated with the outcomes of BC patients. According to the above-mentioned gene signature, patients were divided into two risk subgroups. The analysis showed that our constructed risk model was independent of clinical features and that the risk score was a highly powerful tool for predicting the overall survival (OS) of BC patients. Taking together, we identified a gene signature associated with glycolysis that could effectively predict the prognosis of BC patients. Our findings offer a new perspective for the clinical research and treatment of BC.

SUBMITTER: Wu Z

PROVIDER: S-EPMC7837905 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer.

Wu Zhengyuan Z Wen Zhenpei Z Li Zhengtian Z Yu Miao M Ye Guihong G

Medicine 20210101 3

<h4>Abstract</h4>Bladder cancer (BC) is one of the most common malignancies worldwide. Several biomarkers related to the prognosis of patients with BC have previously been identified. However, these prognostic models use only one gene and are thus not reliable or accurate enough. The purpose of our study was to develop an innovative gene signature that has greater prognostic value in BC. So, in this study, we performed mRNA expression profiling of glycolysis-related genes in BC (n = 407) cohorts ...[more]

PMID: 33545950

Similar Datasets

Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Project description:BackgroundFerroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined.MethodsFRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens.ResultsThirty-two significantly different FRGs were identified from TCGA-BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan-Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA.ConclusionHerein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

Project description:BackgroundGlobally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers.MethodsFirst, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples.ResultsPrognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets.ConclusionsBased on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.

Project description:BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.

Project description:BackgroundAutophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.MethodsFirst, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariate, LASSO and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, the key genes mRNA expression were validated using quantitative real-time PCR in clinical tissue samples.ResultsA total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (p < 0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. For clinical sample validation, we found that EIF4EBP1and ERBB2 had higher level in EC than that in normal tissues while CDKN1B, DLC1 and GRID1 had lower level, which was consistent with the results predicted.ConclusionsBased on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

Dataset Information

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer.

Abstract

Publications

Identification and prognostic value of a glycolysis-related gene signature in patients with bladder cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets