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 Validation and Application of Prognostic Signature Based on Necroptosis-related Genes in Patients With Thyroid Carcinoma

2021 ◽  
Author(s):  
yiming tao ◽  
Hang Ruan ◽  
Hui Zhao ◽  
Wenpei Dang ◽  
Xinxin Xu ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Curve Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Clinical Variables

Abstract ObjectiveTo explore the relationship between thyroid carcinoma (TC) and necroptosis, and to construct a related prognostic signature to assist in diagnosis and treatment.Methods and ResultsA total of 159 necroptosis-related genes (NRGs) were screened for in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database; 38 differentially expressed NRGs (DENGs) of TC were identified from The Cancer Genome Atlas/Genomic Data Commons database (TCGA/GDC); and GO, KEGG, and GSEA enrichment analysis showed that they were mostly related to cell necrosis, autophagy, P53, and other signaling pathways. Univariate and multivariate Cox regression and Lasso regression were used to screen for DENGs associated with prognosis, and a prognostic signature about BID, H2AC12, STAT1, IFNA21, IL1A was established. The patients were then divided into high-risk and low-risk groups according to the median value of the prognostic signature, and their overall survival (OS) was analyzed via the Kaplan-Meier method. The predictive accuracy was also determined using receiver operating characteristic (ROC) curve analysis. Additionally, we performed stratification analyses based on different clinical variables and evaluated the correlations between risk score and clinical variables. The independent prognostic value of the signature was further confirmed by multivariate Cox regression analysis, and decision curve analysis (DCA) was employed to evaluate the quality of the prognostic model and its clinical utility.ConclusionWe successfully constructed a novel necroptosis-related signature for the prediction of prognosis in patients with TC.

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 Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

2021 ◽  
Author(s):  
Jixiang Cao ◽  
Xi Chen ◽  
Guang Lu ◽  
Haowei Wang ◽  
Xinyu Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Risk Scores ◽  
Tumor Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

scholarly journals Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Guangtao Sun ◽  
Kejian Sun ◽  
Chao Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nuclear Receptors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Prognostic Signature ◽  
Term Survival ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

scholarly journals Prediction of Overall Survival for Early-stage Non-small Cell Lung Cancer via a Novel 7-gene Prognostic Signature and Allele Frequency Deviation (AFD)

2020 ◽  
Author(s):  
Aisha AL-Dherasi ◽  
Yuwei Liao ◽  
Qi-Tian Huang ◽  
Yichen Wang ◽  
Rulin Hua ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Allele Frequency ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Small Cell ◽  
Prognostic Signature ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Nsclc Patients

Abstract Background Due to the late and poor prognosis of non-small lung cancer(NSCLC), the mortality of patients is high, underlines the need to identify a credible prognostic marker for NSCLC patients. The aim of our study is to examine the association of allele frequency deviation (AFD) with the patient's survival, as well as identification and validation of a new prognostic signature to predict NSCLC overall survival(OS).Methods First, we developed a new algorithm to calculate AFD from whole-exome sequencing(WES) data, then we compared the predictability of the patient's survival between AFD, tumor mutation burden (TMB) and change of variants allele frequency (dVAF). Second, we overlapped the differentially expressed genes (DEGs) from our data with the genes associated with the survival of The Cancer Genome Atlas (TCGA) database to confirm all genes significantly related to the survival of lung cancer. We identified 149 genes, 31 of which are new genes and have not been reported for lung cancer, that was used to develop a new prognostic model. Lung cancer adenocarcinoma (LUAD) data from the TCGA database was used to validate the gene-signature model. The prognostic model relating to the genes was established and validated in training and LUAD validation groups.Results There was a significant association found between the high AFD value and poor survival among non-small cell lung cancer (NSCLC) patients. A novel seven genes (UCN2, RIMS2, CAVIN2, GRIA1, PKHD1L1, PGM5, CLIC6) were obtained through multivariate Cox regression analysis and significantly associated with NSCLC patients survival. Cox regression analysis confirmed that AFD and 7-gene signature are an independent prognostic marker in NSCLC patients. The AUC for 5-year survival in AFD and the AUC for 3-year survival in both training and validation groups were greater than 0.7.Conclusion As a result, AFD and 7-gene signatures were identified as new independent predictive factors used for predicting the survival among NSCLC patients.

scholarly journals Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Wei Sun ◽  
Ningmei Shen ◽  
Qiang Wang ◽  
Shouyu Wang ◽  
...  
Keyword(s):  
Clinical Features ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.

scholarly journals Identification of key N6-Methyladenosine-Related lncRNAs in Colon Adenocarcinoma using Bioinformatics Analysis

2021 ◽  
Author(s):  
Yuancheng Huang ◽  
Yanhua Yan ◽  
Chaoyuan Huang ◽  
Xiaotao Jiang ◽  
Zehong Yang ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Clinical Characteristics ◽  
Prognostic Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Prognostic Indicator ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Purpose: The purpose of this study was to investigate the role of m6A-related lncRNAs in colon adenocarcinoma (COAD) and determine their prognostic value.Material and methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas database. Correlation and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. A prognostic signature was established via least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Whether the prognostic model could serve as a prognostic indicator for overall survival (OS) in subgroups of patients with different clinical characteristics were explored. Next, We established a competing endogenous RNA network. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Ontology analysis were performed for biological functional analysis.Results: 36 lncRNAs that were highly correlated with OS of patients were identified. A prognostic signature comprising 11 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients . Univariate and Multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. This m6A-related lncRNA prognostic model could serve as a prognostic indicator for OS in subgroups of patients with different clinical characteristics. Biological processes and pathways associated with cancer were identified.Conclusion: We revealed the role and prognostic value of m6A-related lncRNAs in COAD. Our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and develop targeted therapy for COAD.

scholarly journals Biological Functions and Prognostic Value of Ferroptosis-Related Genes in Bladder Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Kezhen Yi ◽  
JingChong Liu ◽  
Yuan Rong ◽  
Cheng Wang ◽  
Xuan Tang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Protein Interactions ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Information ◽  
Cox Regression Analysis

Background: Every year, nearly 170,000 people die from bladder cancer worldwide. A major problem after transurethral resection of bladder tumor is that 40–80% of the tumors recur. Ferroptosis is a type of regulatory necrosis mediated by iron-catalyzed, excessive oxidation of polyunsaturated fatty acids. Increasing the sensitivity of tumor cells to ferroptosis is a potential treatment option for cancer. Establishing a diagnostic and prognostic model based on ferroptosis-related genes may provide guidance for the precise treatment of bladder cancer.Methods: We downloaded mRNA data in Bladder Cancer from The Cancer Genome Atlas and analyzed differentially expressed genes based on and extract ferroptosis-related genes. We identified relevant pathways and annotate the functions of ferroptosis-related DEGs using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology functions. On the website of Search Tool for Retrieving Interacting Genes database (STRING), we downloaded the protein-protein interactions of DEGs, which were drawn by the Cytoscape software. Then the Cox regression analysis were performed so that the prognostic value of ferroptosis-related genes and survival time are combined to identify survival- and ferroptosis-related genes and establish a prognostic formula. Survival analysis and receiver operating characteristic curvevalidation were then performed. Risk curves and nomograms were generated for both groups to predict survival. Finally, RT-qPCR was applied to analyze gene expression.Results: Eight ferroptosis-related genes with prognostic value (ISCU, NFE2L2, MAFG, ZEB1, VDAC2, TXNIP, SCD, and JDP2) were identified. With clinical data, we established a prognostic model to provide promising diagnostic and prognostic information of bladder cancer based on the eight ferroptosis-related genes. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues.Conclusion: This study found that the ferroptosis-related genes is associated with bladder cancer, which may serve as new target for the treatment of bladder cancer.

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