scholarly journals Weighted Gene Coexpression Network Analysis Reveals Cancer Stem Cell-Associated Metabolic Gene Signature in Glioma

2021 ◽  
Author(s):  
Yan Tang ◽  
Yao Jiang ◽  
Dan Zhang ◽  
Jia Fan ◽  
Juan Yao ◽  
...  
Keyword(s):  
Stem Cell ◽  
Network Analysis ◽  
Risk Stratification ◽  
Cancer Stem Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Roc Curves ◽  
Gene Signature ◽  
Metabolic Gene ◽  
Genome Atlas

Abstract Background: Isocitrate dehydrogenase (IDH) mutant glioma patients have a favorable prognosis, accompanying with metabolic alterations and glioma cell dedifferentiation. Recently, mRNA expression-based stemness index (mRNAsi) characteristic relation to IDH status of gliomas has yet illuminated. Thus, we aimed to establish a cancer stem cell-associated metabolic gene signature for risk stratification of gliomas. Methods: The glioma samples came from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Next, we performed the differential expression analysis between IDH mutant and IDH wild-type gliomas and also conducted weighted gene correlation network analysis (WGCNA) for determining the modules associated with cancer stem cell trait. Subsequently, multivariate Cox regression analysis with the Akaike information criterion (AIC) algorithm was employed to establish a stemness-related metabolic gene signature, which was validated using time-dependent receiver operating characteristic (ROC) curves and concordance index (C-index). Also, we developed a nomogram based on clinical traits and prognostic model. Additionally, according to the results of immunohistochemistry (IHC) staining, the protein levels of gene signature were consistent with the genes expression’s direction.Results: Low expression of mRNAsi was capable of predicting the unfavourable OS of gliomas with a 5-year survival rate of 14.08%. The blue module and its 1466 genes were pertinent to mRNAsi characteristic. Next, Kaplan-Meier (KM) survival curves revealed that cancer stem cell-associated metabolic genes exerted impact on gliomas’ prognosis. Subsequently, univariate and multivariate Cox regression analyses were implemented, and gene signature (LCAT, UST, GALNT13, and SMPD3) was constructed, with C-index of 0.798 (95%CI: 0.769-0.827). Notably, the prognostic model presented a superior predictive value for gliomas’ survival, with the area under the curve (AUC) of ROC curves at 1-year, 3-year as well as 5-year time-point of 0.845, 0.85 and 0.811, respectively. And forest plot uncovered its role as a potential independent predictor for gliomas (HR=2.840, 95%CI: 1.961-4.113, P <0.001). Nomogram also presented superior predictive performance for gliomas’ OS. Conclusion: The gene signature (LCAT, UST, GALNT13, and SMPD3) can be used for risk stratification and also can serve as an independent prognostic factor of glioma patients.

scholarly journals Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jimin He ◽  
Chun Zeng ◽  
Yong Long
Keyword(s):  
Risk Stratification ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Genome Atlas

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients’ overall survival (OS). The turquoise module ( cor = 0.67 ; P < 0.001 ) and its genes ( n = 1092 ) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome ( P < 0.0001 ). Also, this IRRS model was found to be an independent prognostic indicator of gliomas’ survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

scholarly journals The Systematic Landscape of Nectin Family and Nectin-Like Molecules: Functions and Prognostic Value in Low Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yunhe Han ◽  
Cunyi Zou ◽  
Chen Zhu ◽  
Tianqi Liu ◽  
Shuai Shen ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Roc Curves ◽  
Gene Signature ◽  
Low Grade ◽  
Kaplan Meier ◽  
Go Enrichment ◽  
The Relationship ◽  
Genome Atlas

Objective: Nectin and nectin-like molecules (Necls) are molecules that are involved in cell–cell adhesion and other vital cellular processes. This study aimed to determine the expression and prognostic value of nectin and Necls in low grade glioma (LGG).Materials and Methods: Differentially expressed nectin and Necls in LGG samples and the relationship of nectin family and Necls expression with prognosis, clinicopathological parameters, and survival were explored using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases. Univariate and multivariate Cox analysis models were performed to construct the prognosis-related gene signature. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves and multivariate Cox regression analysis, were utilized to evaluate the prognostic capacity of the four-gene signature. Gene ontology (GO)enrichment analysis and Gene Set Enrichment Analyses (GSEA) were performed to further understand the underlying molecular mechanisms. The Tumor Immune Estimation Resource (TIMER) was used to explore the relationship between the four-gene signature and tumor immune infiltration.Results: Several nectin and Necls were differentially expressed in LGG. Kaplan–Meier survival analyses and Univariate Cox regression showed patients with high expression of NECTIN2 and PVR and low expression of CADM2 and NECTIN1 had worse prognosis among TCGA, CGGA, and REMBRANDT database. Then, a novel four-gene signature was built for LGG prognosis prediction. ROC curves, KM survival analyses, and multivariate COX regression indicated the new signature was an independent prognostic indicator for overall survival. Finally, GSEA and GO enrichment analyses revealed that immune-related pathways participate in the molecular mechanisms. The risk score had a strong negative correlation with tumor purity and data of TIMER showed different immune cell proportions (macrophage and myeloid dendritic cell) between high- and low-risk groups. Additionally, signature scores were positively related to multiple immune-related biomarkers (IL 2, IL8 and IFNγ).Conclusion: Our results offer an extensive analysis of nectin and Necls levels and a four-gene model for prognostic prediction in LGG, providing insights for further investigation of CADM2, NECTIN1/2, and PVR as potential clinical and immune targets in LGG.

scholarly journals Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bide Liu ◽  
Xun Li ◽  
Jiuzhi Li ◽  
Hongyong Jin ◽  
Hongliang Jia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Stem Cell ◽  
High Risk ◽  
Cancer Stem Cell ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Gene Set ◽  
Validation Set

Background. Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR. Methods. The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR. Results. By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, P < 0.001 ). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, P = 0.005 ). Time-dependent ROC at 1 year suggested that the CSC gene signature ( AUC = 0.800 ) possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature. Conclusions. We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

scholarly journals A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Ziming Hou ◽  
Jun Yang ◽  
Hao Wang ◽  
Dongyuan Liu ◽  
Hongbing Zhang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Training Set ◽  
Prognostic Gene ◽  
Prognostic Gene Signature ◽  
Genome Atlas

Objective. This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.Methods. Based on the GBM information in the Cancer Genome Atlas (TCGA, training set), prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox-Proportional Hazards (Cox-PH). Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low- and high-risk groups according to the median prognosis index (PI). GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.Results. We identified that the prognostic 14-gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high- and low-risk groups showed the significantly different OS (P = 7.47 × 10−9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037). The model B in training set was significant but failed in testing sets.Conclusion. The prognostic model which was constructed based on the prognostic 14-gene signature presented a high predictive ability for GBM. The 14-gene signature may have clinical implications in the subclassification of GBM.

CTNI-39. IDENTIFICATION OF AN AUTOPHAGY-RELATED GENE SIGNATURE AND ESTABLISHMENT OF A NOMOGRAM PREDICTING SURVIVAL OF LOWER-GRADE GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi68-vi68
Author(s):  
Lei Wen ◽  
hui Wang ◽  
Mingyao Lai ◽  
Changguo Shan ◽  
Linbo Cai
Keyword(s):  
Risk Stratification ◽  
Cox Regression ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Survival Prediction ◽  
Lower Grade ◽  
Related Gene ◽  
Prognostic Signature ◽  
Genome Atlas

Abstract OBJECTIVE The aim of our study was to establish an autophagy-related signature for individualized risk stratification and prognosis prediction in LGG. METHODS RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. The 232 ARGs were obtained from the Human Autophagy Database (HADb). Univariate and Lasso regression were employed to identify differentially expressed autophagy-related genes (ARGs) and establish a prognostic signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index) and calibration curve. RESULTS Fifty-three autophagy-related DEGs were identified. Four autophagy-related genes (DIRAS3, GNAI3, PTK6, and BIRC5) were selected to establish the prognostic signature and verified in the CGGA validation cohorts. Univariate and multivariate Cox regression indicated that the autophagy signature (HR, 95%CI, P) was an independent predictor of prognosis in LGG. Finally, a prognostic nomogram incorporating age, grade, targeted therapy, new event, tumor status and autophagy signature achieved excellent predicative performance (AUC 0.907, 0.865 and 0.858 for 1-year, 3-year and 5-year survival, respectively) verified by Time-dependent ROC, C-index (0.844, 95% CI, 0.799 to 0.889; P = 1.01e-12) and calibration plots. CONCLUSION The present study constructed a robust four autophagy-related gene signature. A prognostic nomogram in risk stratification and prediction of overall survival in LGG was established. The findings may be beneficial to individualized survival prediction and medical decision-making for LGG.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuohui Chen ◽  
Tong Wu ◽  
Zhouyi Yan ◽  
Mengqi Zhang
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Related Gene ◽  
Roc Curve Analysis ◽  
Small Molecule Drugs ◽  
The Relationship ◽  
Genome Atlas

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules.

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