scholarly journals Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Qiang Zhang ◽  
Wenhao Liu ◽  
Shun-Bin Luo ◽  
Fu-Chen Xie ◽  
Xiao-Jun Liu ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Clinical Information ◽  
Predictive Performance ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Good Prospect ◽  
Genome Atlas

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

Development of a Prognostic Five-Gene Signature with Radiotherapy Guidance Significance for Diffuse Lower-Grade Glioma Patients Based on Large-Scale Sequencing Data

2020 ◽  
Author(s):  
Qiang Zhang ◽  
Shun-Bin Luo ◽  
Fu-Chen Xie ◽  
Xiao-Jun Liu ◽  
Ren-ai Xu
Keyword(s):  
Large Scale ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Good Prospect ◽  
Sequencing Data ◽  
Genome Atlas

Abstract Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein coding genes (PCGs) is widely used as tumor markers. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with significantly different overall survival (Log Rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker with radiotherapy guidance significance for LGG patients and has a good prospect in clinical application.

scholarly journals Dexamethasone-mediated oncogenicity in vitro and in an animal model of glioblastoma

2018 ◽  
Vol 129 (6) ◽  
pp. 1446-1455 ◽  
Author(s):  
Markus M. Luedi ◽  
Sanjay K. Singh ◽  
Jennifer C. Mosley ◽  
Islam S. A. Hassan ◽  
Masumeh Hatami ◽  
...  
Keyword(s):  
Ex Vivo ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Boyden Chamber ◽  
Ki 67 ◽  
Marker Selection

OBJECTIVEDexamethasone, a known regulator of mesenchymal programming in glioblastoma (GBM), is routinely used to manage edema in GBM patients. Dexamethasone also activates the expression of genes, such as CEBPB, in GBM stem cells (GSCs). However, the drug’s impact on invasion, proliferation, and angiogenesis in GBM remains unclear. To determine whether dexamethasone induces invasion, proliferation, and angiogenesis in GBM, the authors investigated the drug’s impact in vitro, in vivo, and in clinical information derived from The Cancer Genome Atlas (TCGA) cohort.METHODSExpression profiles of patients from the TCGA cohort with mesenchymal GBM (n = 155) were compared with patients with proneural GBM by comparative marker selection. To obtain robust data, GSCs with IDH1 wild-type (GSC3) and with IDH1 mutant (GSC6) status were exposed to dexamethasone in vitro and in vivo and analyzed for invasion (Boyden chamber, human-specific nucleolin), proliferation (Ki-67), and angiogenesis (CD31). Ex vivo tumor cells from dexamethasone-treated and control mice were isolated by fluorescence activated cell sorting and profiled using Affymetrix chips for mRNA (HTA 2.0) and microRNAs (miRNA 4.0). A pathway analysis was performed to identify a dexamethasone-regulated gene signature, and its relationship with overall survival (OS) was assessed using Kaplan-Meier analysis in the entire GBM TCGA cohort (n = 520).RESULTSThe mesenchymal subgroup, when compared with the proneural subgroup, had significant upregulation of a dexamethasone-regulated gene network, as well as canonical pathways of proliferation, invasion, and angiogenesis. Dexamethasone-treated GSC3 demonstrated a significant increase in invasion, both in vitro and in vivo, whereas GSC6 demonstrated a modest increase. Furthermore, dexamethasone treatment of both GSC3 and GSC6 lines resulted in significantly elevated cell proliferation and angiogenesis in vivo. Patients with mesenchymal GBM had significant upregulation of dexamethasone-regulated pathways when compared with patients with proneural GBM. A prognostic (p = 0.0007) 33-gene signature was derived from the ex vivo expression profile analyses and used to dichotomize the entire TCGA cohort by high (median OS 12.65 months) or low (median OS 14.91 months) dexamethasone signature.CONCLUSIONSThe authors present evidence that furthers the understanding of the complex effects of dexamethasone on biological characteristics of GBM. The results suggest that the drug increases invasion, proliferation, and angiogenesis in human GSC-derived orthotopic tumors, potentially worsening GBM patients’ prognoses. The authors believe that careful investigation is needed to determine how to minimize these deleterious dexamethasone-associated side effects in GBM.

scholarly journals Integrin Alpha-2 as a Potential Prognostic and Predictive Biomarker for Patients With Lower-Grade Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Li Lin ◽  
Kai Huang ◽  
Zewei Tu ◽  
Xingen Zhu ◽  
Jingying Li ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Clinical Information ◽  
Predictive Biomarker ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Western Blot Assay ◽  
Clinical Model ◽  
Cell Functions ◽  
Novel Target ◽  
Genome Atlas

Diffuse gliomas are the most common malignant brain tumors with the highest mortality and recurrence rate in adults. Integrin alpha-2 (ITGA2) is involved in a series of biological processes, including cell adhesion, stemness regulation, angiogenesis, and immune/blood cell functions. The role of ITGA2 in lower-grade gliomas (LGGs) is not well defined. Firstly, we downloaded RNA sequencing and relevant clinical information from The Cancer Genome Atlas cohort, the Chinese Glioma Genome Atlas cohort, and related immune cohorts. Next, prognosis analysis, difference analysis, clinical model construction, enrichment analysis, and immune infiltration analysis are performed for this study. These analyses indicated that ITGA2 may have clinical application value and research value in LGG immunotherapy. We also detected the mRNA and protein expression of ITGA2 in three LGG cell lines and normal glial cells using quantitative real-time polymerase chain reaction assay and western blot assay. Our study not only offers a novel target for LGG immunotherapy but also can better comprehend the mechanism of the development and progression of patients with LGG. This study revealed that ITGA2 may be a potential prognostic and predictive biomarker for LGG, which can bring new insights into targeted immunotherapy.

scholarly journals An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weifeng Zheng ◽  
Chaoying Chen ◽  
Jianghao Yu ◽  
Chengfeng Jin ◽  
Tiemei Han
Keyword(s):  
Energy Metabolism ◽  
Cox Regression ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Kaplan Meier ◽  
Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

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 Comprehensive Molecular Analyses of a Six-Gene Signature for Predicting Late Recurrence of Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuyuan Zhang ◽  
Zaoqu Liu ◽  
Xin Li ◽  
Long Liu ◽  
Libo Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Groups ◽  
Late Recurrence ◽  
Gene Signature ◽  
Postoperative Recurrence ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Molecular Alterations ◽  
Number Of Patients ◽  
Liver Hepatocellular Carcinoma

A larger number of patients with stages I–III hepatocellular carcinoma (HCC) experience late recurrence (LR) after surgery. We sought to develop a novel tool to stratify patients with different LR risk for tailoring decision-making for postoperative recurrence surveillance and therapy modalities. We retrospectively enrolled two independent public cohorts and 103 HCC tissues. Using LASSO logical analysis, a six-gene model was developed in the The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) and independently validated in GSE76427. Further experimental validation using qRT-PCR assays was performed to ensure the robustness and clinical feasible of this signature. We developed a novel LR-related signature consisting of six genes. This signature was validated to be significantly associated with dismal recurrence-free survival in three cohorts TCGA-LIHC, GSE76427, and qPCR assays [HR: 2.007 (1.200–3.357), p = 0.008; HR: 2.171 (1.068, 4.412), p-value = 0.032; HR: 3.383 (2.100, 5.450), p-value &lt;0.001]. More importantly, this signature displayed robust discrimination in predicting the LR risk, with AUCs being 0.73 (TCGA-LIHC), 0.93 (GSE76427), and 0.85 (in-house cohort). Furthermore, we deciphered the specific landscape of molecular alterations among patients in nonrecurrence (NR) and LR group to analyze the mechanism contributing to LR. For high-risk group, we also identified several potential drugs with specific sensitivity to high- and low-risk groups, which is vital to improve prognosis of LR-HCC after surgery. We discovered and experimentally validated a novel gene signature with powerful performance for identifying patients at high LR risk in stages I–III HCC.

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