Dataset Information

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

ABSTRACT: Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

SUBMITTER: Yan X

PROVIDER: S-EPMC7052873 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

Yan Xin X Fu Xun X Guo Zi-Xin ZX Liu Xiao-Ping XP Liu Tong-Zu TZ Li Sheng S

Journal of Cancer 20200117 7

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to con ...[more]

PMID: 32194788

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Project description:Purpose: To identify whether ferroptosis-related genes play predictive roles in bladder cancer patients and to develop a ferroptosis-related gene signature to predict overall survival outcomes. Materials and Methods: We downloaded the mRNA expression files and clinical data of 256 bladder samples (188 bladder tumour and 68 nontumour samples) from the GEO database and 430 bladder samples (411 bladder tumour and 19 nontumour samples) from the TCGA database. A multigene signature based on prognostic ferroptosis-related genes was constructed by least absolute shrinkage and selection operator Cox regression analysis in the GEO cohort. The TCGA cohort was used to validate the ferroptosis-related gene signature. Next, functional enrichment analysis, including both Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, was performed to elucidate the mechanism underlying the signature. The ssGSEA scores of 16 immune cells and 13 immune-related pathway activities between the high-risk and low-risk groups were also analysed in our study. Results: Thirty-three (67.3%) ferroptosis-related genes were differentially expressed between bladder tumour samples and nontumour samples in the GEO cohort. The intersection of prognostic ferroptosis-related genes and differentially expressed genes identified four prognostic targets, including ALOX5, FANCD2, HMGCR and FADS2. The least absolute shrinkage and selection operator Cox regression successfully built a 4-gene signature: risk score value = esum (each gene's normalized expression * each gene's coefficient). Univariate and multivariate Cox regression analyses were performed in both the GEO and TCGA cohorts to test the independent prognostic value of the 4-gene risk signature. Multivariate Cox regression analysis in the GEO cohort identified age (p < 0.001), grade (p = 0.129) and risk score (p = 0.016) as independent prognostic predictors for overall survival. Multivariate Cox regression analysis in the TCGA cohort also identified age (p = 0.002), stage (p < 0.001) and risk score (p = 0.006) as independent prognostic predictors for overall survival. The type II IFN response was determined to be significantly weakened in the high-risk group in both the GEO and TCGA cohorts. Conclusion: We successfully built a ferroptosis-related gene signature of significant predictive value for bladder cancer. These results suggest a novel research direction for targeted therapy of bladder cancer in the future.

Project description:Human bladder cancer (BCa) is the most common urogenital system malignancy. Patients with BCa have limited treatment efficacy in clinical practice. Novel biomarkers could provide more crucial information conferring to cancer diagnosis, treatment, and prognosis. Here, we aimed to explore and identify novel biomarkers associated with cancer-specific survival of patients with BCa to build a prognostic signature. Based on univariate Cox regression, Lasso regression, and multivariate Cox regression analysis, we conducted an integrated analysis in the training set (GSE32894) and established a six-gene signature to predict the cancer-specific survival for human BCa. The six genes were Cyclin Dependent Kinase 4 (CDK4), E2F Transcription Factor 7 (E2F7), Collagen Type XI Alpha 1 Chain (COL11A1), Bradykinin Receptor B2 (BDKRB2), Yip1 Interacting Factor Homolog B (YIF1B), and Zinc Finger Protein 415 (ZNF415). Then, we validated the prognostic value of the model by using two other datasets (GSE13507 and TCGA). Also, we conducted univariate and multivariate Cox regression analyses, and results indicated that the six-gene signature was an independent prognostic factor of cancer-specific survival of patients with BCa. Functional analysis was performed based on the differentially expressed genes of low- and high-risk patients, and we found that they were enriched in lipid metabolic and cell division-related biological processes. Meanwhile, the gene set enrichment analysis (GSEA) revealed that high-risk samples were enriched in cell cycle and cancer-related pathways [G2/M checkpoint, E2F targets, mitotic spindle, mTOR signaling, spermatogenesis, epithelial-mesenchymal transition (EMT), DNA repair, PI3K/AKT/mTOR signaling, unfolded protein response (UPR), and MYC targets V2]. Lastly, we detected the relative expression of each signature in BCa cell lines by quantitative real-time PCR (qRT-PCR). As far as we know, currently, the present study is the first research that developed and validated a cancer-specific survival prognostic index based on three independent cohorts. The results revealed that this six-gene signature has a predictive ability for cancer-specific prognosis. Moreover, we also verified the relative expression of these six signatures between the bladder cell line and four BCa cell lines by qRT-PCR. Nevertheless, experiments to further explore the function of six genes are lacking.

Project description:Introduction Bladder cancer (BLCA) is the ninth most common malignancy worldwide and the fourth most common cancer in men. Copper levels are significantly altered in patients with thyroid, breast, lung, cervical, ovarian, pancreatic, oral, gastric, bladder, and prostate cancers. Outcomes can be predicted by constructing signatures using lncRNA-related genes associated with outcomes. Methods We identified lncRNAs related to outcomes, those differentially expressed in bladder cancer, and cuproptosis-related lncRNAs from TCGA. We identified the intersection to obtain 12 genes and established a prognostic risk signature consisting of eight genes using LASSO-penalized multivariate Cox analysis. We constructed a training set, performed survival analysis on the high-and low-risk groups, and performed validation in the test and full sets. There existed a substantial contrast in the likelihood of survival among the cohorts of high and low risk. An in-depth analysis of the gene mutations associated with tumors was conducted to evaluate the risk of developing cancer. We also performed gene analysis on neoadjuvant chemotherapy. We conducted experimental validation on the key gene UBE2Q1-AS1 in our prognostic signature. Results The risk signature we constructed shows significant differences between the high-risk group and the low-risk group. Univariate survival analysis of the eight genes in our signature showed that each gene distinguished between high- and low-risk groups. Sub-group analysis revealed that our risk score differed significantly in tumor stage, age, and gender. The analysis results of the tumor mutation burden (TMB) showed a significant difference in the TMB between the low- and high-risk groups, which had a direct impact on the outcomes. These findings highlight the importance of TMB as a potential prognostic marker in cancer detection and prevention. We analyzed the immune microenvironment and found significant differences in immune function, validation responses, immunotherapy-related positive markers, and critical steps in the tumor immunity cycle between the high- and low-risk groups. We found that the effect of anti-CTLA4 and PD-1 was higher in the high-risk group than in the low-risk group.Gene analysis of neoadjuvant chemotherapy revealed that the treatment effect in the high-risk group was better than in the low-risk group. The key gene UBE2Q1-AS1 in our prognostic signature can significantly influence the cell viability, migration, and proliferation of cancer cells. Discussion We established a signature consisting of eight genes constructed from cuproptosis-related lncRNAs that have potential clinical applications for outcomes prediction, diagnosis, and treatment.

Project description:BackgroundPancreatic cancer is one of the most common malignant tumors of the digestive tract, and it has a poor prognosis. Traditional methods are not effective to accurately assess the prognosis of patients with pancreatic cancer. Immunotherapy is a new promising approach for the treatment of pancreatic cancer; however, some patients do not respond well to immunotherapy, which may be related to tumor microenvironment regulation. In this study, we use gene expression database to mine important immune genes and establish a prognostic prediction model for pancreatic cancer patients. We hope to provide a feasible method to evaluate the prognosis of pancreatic cancer and provide valuable targets for pancreatic cancer immunotherapy.ResultsWe used univariate COX proportional hazard regression analysis, the least absolute shrinkage and selection operator, and multivariate COX regression analysis to screen 8 genes related to prognosis from the 314 immune-related genes, and used them to construct a new clinical prediction model in the TCGA pancreatic cancer cohort. Subsequently, we evaluated the prognostic value of the model. The Kaplan-Meier cumulative curve showed that patients with low risk scores survived significantly longer than patients with high risk scores. The area under the ROC curve (AUC value) of the risk score was 0.755. The univariate COX analysis showed that the risk score was significantly related to overall survival (HR 1.406, 95% CI 1.237-1.598, P?<?0.001), and multivariate analysis showed that the risk score was an independent prognostic factor (HR 1.400, 95% CI 1.287-1.522, P?<?0.001). Correlation analysis found that immune genes are closely related to tumor immune microenvironment.ConclusionsBased on the TCGA-PAAD cohort, we identified immune-related markers with independent prognostic significance, validated, and analyzed their biological functions, to provide a feasible method for the prognosis of pancreatic cancer and provide potentially valuable targets for pancreatic cancer immunotherapy.

Dataset Information

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

Publications

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

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