scholarly journals A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

2021 ◽  
Vol 20 ◽  
pp. 153303382199208
Author(s):  
Wentao Liu ◽  
Jiaxuan Zou ◽  
Rijun Ren ◽  
Jingping Liu ◽  
Gentang Zhang ◽  
...  
Keyword(s):  
Risk Score ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Cox Regression Analysis ◽  
Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

scholarly journals Serine Incorporator 2 (SERINC2) Expression Predicts an Unfavorable Prognosis of Low-Grade Glioma (LGG): Evidence from Bioinformatics Analysis

2020 ◽  
Vol 70 (10) ◽  
pp. 1521-1532
Author(s):  
Chunxiao Qi ◽  
Lei Lei ◽  
Jinqu Hu ◽  
Gang Wang ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Membrane Lipids ◽  
Transmembrane Protein ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Hub Genes ◽  
Cox Regression Analysis

Abstract Serine Incorporator 2 (SERINC2) is a transmembrane protein that incorporates serine into membrane lipids. The function of SERINC2 in tumors has been reported, but the role of SERINC2 in gliomas is not fully understood. RNA-sequencing data from The Cancer Genome Atlas (TCGA) (530 cases of low-grade glioma (LGG) and 173 cases of glioblastoma multiforme (GBM)) and microarray data from Gene Expression Omnibus (GEO) (Accession No. GSE16011, 284 cases gliomas were included) were acquired. Bioinformatics analysis was performed as the primary method to examine the function of SERINC2 and its correlated genes in glioma. SERINC2 was highly expressed in GBM compared with LGG and normal brain tissues. Elevated SERINC2 expression predicted shorter 5-, 10-, and 15-year overall survival (OS) of LGG patients and isocitrate dehydrogenase-1 (IDH-1) mutation-type LGG patients but had no effect on the OS of GBM patients. Cox regression analysis showed that SERINC2 was an independent factor in LGG OS. Methylation analysis found that 13 CpG methylation sites (methylation450k) correlated with SERINC2 expression in LGG. The mRNA expression level of SERINC2 was significant lower in the DNA deletion group than in the intact and amplification groups. A total of 390 copositive and 244 conegative correlation genes with SERINC2 were obtained from LGG in TCGA-LGG and GSE16011. Gene ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the copositive correlation genes were primarily enriched in the mitotic process and cell cycle. Combining the results from the protein-protein interaction (PPI) network of SERINC2 correlation genes and CytoHubba led to the selection of 10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). OncoLnc analysis confirmed that high expression levels of these hub genes were associated with poor OS in LGG. Our results suggested that aberrant SERINC2 expression existed in glioma and that its expression might be a potential prognostic marker in LGG patients. CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.

scholarly journals Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jingwei Zhao ◽  
Le Wang ◽  
Bo Wei
Keyword(s):  
Energy Metabolism ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Normal Brain ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Lower Grade Glioma ◽  
Genome Atlas

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

scholarly journals Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yin Qiu Tan ◽  
Yun Tao Li ◽  
Teng Feng Yan ◽  
Yang Xu ◽  
Bao Hui Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Survival Rates ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Sequencing Analysis ◽  
Six Genes ◽  
Genome Atlas ◽  
Overall Survival Rates

BackgroundThe immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.MethodsWe collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients’ overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).ResultsSix genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.ConclusionJoint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).

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 Prognostic Value of Risk Score Based on Immunogenenomic Landscape Analysis in Glioma

2020 ◽  
Author(s):  
Haitao Luo ◽  
Kai Huang ◽  
Chuming Tao ◽  
Mioaojing Wu ◽  
Minhua Ye ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Low Grade ◽  
Clinical Prognosis ◽  
Cox Analysis ◽  
Genome Atlas

Abstract Background: Glioma is a lethal intracranial tumor, and inflammation plays an important role in the initiation and development of glioma. Hence, there is an urgent need to conduct a bioinformatics analysis of immune-related genes (IRGs) for glioma. The present study aims to explore the association of the risk score with clinical outcomes and predict the prognosis with glioma. Methods: In The Cancer Genome Atlas (TCGA) database, 462 low grade glioma (LGG) samples and 166 glioblastoma (GBM) samples were reviewed, and IRGs correlated with the prognosis were selected by performing a survival analysis and establishing a Cox regression model. The potential molecular mechanism of these IRGs were also explored with assistance of computational biology. The risk score based on seven survival-associated IRGs was determined with the help of the multivariable Cox analysis, the patients were divided into two subgroups according to their risk score. Results: It was found that these differentially expressed IRGs were involved with the cytokine-cytokine receptor through functional enrichment analysis. The risk score based on the seven IRGs (SSTR5、CXCL10、CCL13、SAA1、CCL21、CCL27 and HTR1A) performed well in predicting patient’s the overall survival (OS), and correlated with age, 1p/19q codeletion status, IDH status, and WHO grades, both in the training (TCGA) datasets and the validation ((Chinese Glioma Genome Atlas) CGGA) datasets. The risk score also could reflect infiltration through several types of immune cells. Conclusions: This present study screened some IRGs associated with the patient’s clinical characteristic and prognosis, connect to the immune repertoire, demonstrated the importance of the risk score as a promising biomarker for estimating the clinical prognosis of glioma.

Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Epigenomics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 907-920
Author(s):  
Wei Song ◽  
Jun Ren ◽  
Wen-Jie Wang ◽  
Chun-Tao Wang ◽  
Tao Fu
Keyword(s):  
Overall Survival ◽  
Survival Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Clinicopathologic Factors ◽  
Cancer Genome Atlas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

scholarly journals Comprehensive Analysis Reveals a 4-Gene Signature in Predicting Response to Temozolomide in Low-Grade Glioma Patients

2019 ◽  
Vol 26 (1) ◽  
pp. 107327481985511 ◽  
Author(s):  
Qi Wang ◽  
Zongze He ◽  
Yong Chen
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Homologous Recombination ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Primary Therapy

Low-grade gliomas (LGGs) are a highly heterogeneous group of slow-growing, lethal, diffusive brain tumors. Temozolomide (TMZ) is a frequently used primary chemotherapeutic agent for LGGs. Currently there is no consensus as to the optimal biomarkers to predict the efficacy of TMZ, which calls for decision-making for each patient while considering molecular profiles. Low-grade glioma data sets were retrieved from The Cancer Genome Atlas. Cox regression and survival analyses were applied to identify clinical features significantly associated with survival. Subsequently, Ordinal logistic regression, co-expression, and Cox regression analyses were applied to identify genes that correlate significantly with response rate, disease-free survival, and overall survival of patients receiving TMZ as primary therapy. Finally, gene expression and methylation analyses were exploited to explain the mechanism between these gene expression and TMZ efficacy in LGG patients. Overall survival was significantly correlated with age, Karnofsky Performance Status score, and histological grade, but not with IDH1 mutation status. Using 3 distinct efficacy end points, regression and co-expression analyses further identified a novel 4-gene signature of ASPM, CCNB1, EXO1, and KIF23 which negatively correlated with response to TMZ therapy. In addition, expression of the 4-gene signature was associated with those of genes involved in homologous recombination. Finally, expression and methylation profiling identified a largely unknown olfactory receptor OR51F2 as potential mediator of the roles of the 4-gene signature in reducing TMZ efficacy. Taken together, these findings propose the 4-gene signature as a novel panel of efficacy predictors of TMZ therapy, as well as potential downstream mechanisms, including homologous recombination, OR51F2, and DNA methylation independent of MGMT.

scholarly journals A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

2020 ◽  
Vol 10 ◽  
Author(s):  
Xuegang Niu ◽  
Jiangnan Sun ◽  
Lingyin Meng ◽  
Tao Fang ◽  
Tongshuo Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Therapeutic Efficacy ◽  
Cox Regression ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Normal Brain ◽  
Score Model ◽  
Genome Atlas

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

Sign in / Sign up

Export Citation Format

Share Document