scholarly journals Analysis of genome-wide mutation profile and establishment of risk signature for prognosis of bladder cancer

2020 ◽  
Author(s):  
Yanyun Zhao ◽  
Rong Ma ◽  
Fangxiao Liu ◽  
Liwen Zhang ◽  
Xuemei Lv ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Predictive Ability ◽  
Time Dependent ◽  
Net Benefit ◽  
Data Set ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Mutation Profile

Abstract Background: Emerging studies have shown that a variety of gene mutations occur in development and progression of cancer and highly mutation genes could play oncogenic or tumor suppressive roles in cancer. Therefore, our aim is to explore mutation genes which affect the prognosis of bladder.Methods: Mutation profile was obtained and analyzed from TCGA data set. A mutation-based signature was established by multivariable Cox regression analysis. Kaplan-Meier was performed to assess the prognostic power of signature. Time-dependent ROC was conducted to evaluate predictive accuracy of signature for bladder cancer patients.Results: There are 20177 genes have alteration in 403 bladder patients and 662 of them were frequently variation (mutation frequency > 5%). In this study, we assessed the prognostic predictive ability of 662 highly mutated genes and identified a mutation signature as an independent indicator for predicting the prognosis of bladder. The time-dependent ROC showed that AUC were 0.893, 0.896, 0.916 and 0.965 at 1, 3, 5 and 10 year, respectively. Stratified analysis and Multivariate Cox analysis showed that this mutation signature was reliable and independent biomarker. Furthermore, the nomogram predictive model can be used to effectively predict clinical prognosis of bladder patients. The decision analysis curve showed patients with risk threshold of 0.03-0.92 potentially yielded clinical net benefit. Finally, we identified several signaling pathways that associated with risk score by GSEA and KEGG analysis including PI3K-Akt signaling pathway and so on.Conclusions: In general, this study provide an optimal mutation signature as potential prognosis biomarker for bladder patients.

scholarly journals Construction and Validation of an Autophagy-Related Prognostic Signature and a Nomogram for Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Yan ◽  
Hua-Hui Wu ◽  
Zhao Chen ◽  
Guo-Wei Du ◽  
Xiao-Jie Bai ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Standard Model ◽  
Survival Probability ◽  
Cox Regression ◽  
Clinicopathological Features ◽  
Prognostic Biomarkers ◽  
Net Benefit ◽  
Cox Regression Analysis ◽  
The Standard Model

ObjectiveBladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients’ risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.MethodsWe screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.ResultsThree prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).ConclusionsThree ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.

scholarly journals Preoperative lymphocyte-to-monocyte ratio improves the prediction of survival outcomes after radical cystectomy for bladder cancer

2020 ◽  
Author(s):  
Hai Bi ◽  
Ye Yan ◽  
Zijian Qin ◽  
Guoliang Wang ◽  
Lulin Ma ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Prognostic Significance ◽  
Survival Outcomes ◽  
Operating Characteristics ◽  
Net Benefit ◽  
Term Survival

Abstract Purpose To determine the prognostic significance of preoperative lymphocyte-to-monocyte (LMR) in bladder cancer (BCa) patients undergoing radical cystectomy (RC), and to validate the prognostic benefit provided by LMR compared to the models relying on the clinicopathological factors alone. Materials and Methods Retrospective analysis of the 342 BCa patients undergoing RC at our institution from 2004 to 2017 was performed to evaluate the prognostic significance of the LMR. Overall survival (OS) and cancer-specific survival (CSS) was assessed by the Kaplan-Meier method. Cox regression models identified risk factors for survival outcomes. Two nomograms were developed based on the basal models to predict the OS and CSS at 1, 3 and 5 years after RC. The accuracy of the nomograms was assessed with receiver operating characteristics (ROC) curves and concordance-index. Decision curve analyses (DCA) were performed to identify the net benefit by the nomograms. Results Excellent long-term survival outcomes of patients were associated with higher LMR level patients. The median survival time for higher LMR level patients was 98.8 months in OS and over 120 months in CSS. In Cox regression multivariate analysis, preoperative LMR, as a continuous variable, is an independent survival outcome predictor ( p <0.001). The addition of LMR to standard model significantly improved its discrimination for prediction of OS by 5.8% and CSS by 5.4% (both p <0.001). Moreover, as shown in DCA, the use of the nomogram including LMR would incur a net benefit over the base models for predicting OS and CSS at 1, 3 and 5 years. Conclusions Elevated preoperative LMR among BCa patients undergoing RC is independently associated with significantly better OS and CSS. Moreover, the increase in predictive accuracy after the inclusion of LMR in multiparametric prediction tools is significant. Therefore, LMR may be useful in preoperative patient risk stratification to help patient counseling and clinical decision making.

scholarly journals Nomogram Predict Overall Survival of Patients with Thymic Epithelial Tumors After Surgery

2021 ◽  
Author(s):  
Yang-Yu Huang ◽  
Lei-Lei Wu ◽  
Xuan Liu ◽  
Shen-Hua Liang ◽  
Guo-Wei Ma
Keyword(s):  
Overall Survival ◽  
Patient Population ◽  
Cox Regression ◽  
Early Stage ◽  
Predictive Ability ◽  
Net Benefit ◽  
Thymic Epithelial Tumors ◽  
Cox Regression Analysis ◽  
Epithelial Tumors ◽  
Novel Approach

Abstract Background: Hematological indicators and clinical characteristics play an important role in the evaluation of the progression and prognosis of thymic epithelial tumors. Therefore, we aimed to combine these potential indicators to establish a prognostic nomogram to determine the overall survival (OS) of patients with thymic epithelial tumors undergoing thymectomy. Methods: This retrospective study was conducted on 167 patients who underwent thymectomy between May 2004 and August 2015. Cox regression analysis were performed to determine the potential indicators related to prognosis and combine these indicators to create a nomogram for visual prediction. The prognostic predictive ability of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic (ROC) curve, and risk stratification. Decision curve analysis was used to evaluate the net benefits of the model. Results: Preoperative albumin levels, neutrophil-to-lymphocyte ratio (NLR), T stage, and underlying diseases (with hypertension and/or diabetes) were included in the nomogram. In the training cohort, the nomogram showed a stronger prognostic predictive ability than the T staging (C index: 0.886 vs 0.725). Calibration curves for the overall survival (OS) were in good agreement with the standard lines in cohorts. The net benefit of the nomogram was higher than that of the T staging model. Conclusions: The nomogram showed better performance in predicting the prognosis and survival of this patient population than the T staging prediction model. And it has potential to identify high-risk patients at an early stage. This is a relatively novel approach for the prediction of OS in this patient population.

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

2020 ◽  
Author(s):  
Keying Zhang ◽  
Jingwei Wang ◽  
Chao Xu ◽  
Jingliang Zhang ◽  
Shaojie Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Prognostic Model ◽  
Present Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Therapeutic Targets ◽  
Underlying Mechanism ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables

Abstract 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.

scholarly journals Prognostic Value and Underlying Mechanism of Autophagy-related Genes in Bladder Cancer

2021 ◽  
Author(s):  
Shiyuan Peng ◽  
Shanjin Ma ◽  
Fa Yang ◽  
Chao Xu ◽  
Hongji Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Present Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Therapeutic Targets ◽  
Underlying Mechanism ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables

Abstract Bladder cancer (BLCA) is the most common malignancy whose early diagnosis can ensure a 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. 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 the aforementioned PDEARGs-based model. 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. PDEARGs are valuable prognostic predictors and potential therapeutic targets for BLCA patients.

scholarly journals Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

2021 ◽  
Author(s):  
Huazhen Tang ◽  
Zhenpeng Yang ◽  
Xibo Sun ◽  
Shuai Lu ◽  
Bing Wang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cancer Patients ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Tumor Stage ◽  
Metabolic Reprogramming ◽  
Smoking History ◽  
Individual Treatment ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Shaojie Chen ◽  
Feifei Huang ◽  
Shangxiang Chen ◽  
Yinting Chen ◽  
Jiajia Li ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Time Dependent ◽  
Concordance Index ◽  
Cox Regression Analysis ◽  
Score Model

ObjectiveGrowing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer.MethodsWe applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index.ResultsThe overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level.ConclusionsThis study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.

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