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 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 &lt; 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.

scholarly journals Clinical and Biological Significances of a Ferroptosis-Related Gene Signature in Glioma

2020 ◽  
Vol 10 ◽  
Author(s):  
Shenghua Zhuo ◽  
Zhimin Chen ◽  
Yibei Yang ◽  
Jinben Zhang ◽  
Jianming Tang ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Clinical Features ◽  
Expression Profiles ◽  
Tumor Therapy ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
The Relationship ◽  
Genome Atlas

Ferroptosis is a form of cell death characterized by non-apoptosis induced by small molecules in tumors. Studies have demonstrated that ferroptosis regulates the biological behaviors of tumors. Therefore, genes that control ferroptosis can be a promising candidate bioindicator in tumor therapy. Herein, functions of ferroptosis-related genes in glioma were investigated. We systematically assessed the relationship between ferroptosis-related genes expression profiles and prognosis in glioma patients based on The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) RNA sequencing datasets. Using the non-negative matrix factorization (NMF) clustering method, 84 ferroptosis-related genes in the RNA sequencing data were distinctly classified into two subgroups (named cluster 1 and cluster 2) in glioma. The least absolute shrinkage and selection operator (LASSO) was used to develop a 25 gene risk signature. The relationship between the gene risk signature and clinical features in glioma was characterized. Results show that the gene risk signature associated with clinical features can be as an independent prognostic indicator in glioma patients. Collectively, the ferroptosis-related risk signature presented in this study can potentially predict the outcome of glioma patients.

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 Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories

2021 ◽  
Vol 9 ◽  
Author(s):  
Renshen Xiang ◽  
Jincheng Fu ◽  
Yuhang Ge ◽  
Jun Ren ◽  
Wei Song ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cell Differentiation ◽  
Clinical Decision Making ◽  
Molecular Subtypes ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Gene Signature ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Sequencing Data

Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis was conducted on the GSE148345 dataset, and CDRG-based molecular subtypes were identified from the GSE39582 dataset. A CDRG-based prognostic RS signature was constructed using The Cancer Genome Atlas as the training set and GSE39582 as the validation set. Two subsets with distinct differentiation states, involving 40 hub CDRGs regulated by YY1 and EGR2, were identified by single-cell RNA sequencing data, of which subset I was related to hypoxia, metabolic disorders, and inflammation, and subset II was associated with immune responses and ferroptosis. The CDRG-based molecular subtypes could successfully predict the clinical outcomes of the patients, the tumor microenvironment status, the immune infiltration status, and the potential response to immunotherapy and chemotherapy. A nomogram integrating a five-CDRG-based RS signature and prognostic clinicopathological characteristics could successfully predict overall survival, with strong predictive performance and high accuracy. The study emphasizes the relevance of CC cell differentiation for predicting the prognosis and therapeutic response of patients to immunotherapy and chemotherapy and proposes a promising direction for CC treatment and clinical decision-making.

scholarly journals The Pancreatic Microbiome is Associated with Carcinogenesis and Worse Prognosis in Males and Smokers

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2672
Author(s):  
Jaideep Chakladar ◽  
Selena Z. Kuo ◽  
Grant Castaneda ◽  
Wei Tse Li ◽  
Aditi Gnanasekar ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Immune Suppression ◽  
Large Scale ◽  
The Cancer Genome Atlas ◽  
Human Pancreatic Cancer ◽  
Sequencing Data ◽  
Cancer Genome Atlas ◽  
And Gender ◽  
Worse Prognosis ◽  
Genome Atlas

An intra-pancreatic microbiota was recently discovered in several prominent studies. Since pancreatic adenocarcinoma (PAAD) is one of the most lethal cancers worldwide, and the intratumor microbiome was found to be a significant contributor to carcinogenesis in other cancers, this study aims to characterize the PAAD microbiome and elucidate how it may be associated with PAAD prognosis. We further explored the association between the intra-pancreatic microbiome and smoking and gender, which are both risk factors for PAAD. RNA-sequencing data from The Cancer Genome Atlas (TCGA) were used to infer microbial abundance, which was correlated to clinical variables and to cancer and immune-associated gene expression, to determine how microbes may contribute to cancer progression. We discovered that the presence of several bacteria species within PAAD tumors is linked to metastasis and immune suppression. This is the first large-scale study to report microbiome-immune correlations in human pancreatic cancer samples. Furthermore, we found that the increased prevalence and poorer prognosis of PAAD in males and smokers are linked to the presence of potentially cancer-promoting or immune-inhibiting microbes. Further study into the roles of these microbes in PAAD is imperative for understanding how a pro-tumor microenvironment may be treated to limit cancer progression.

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 Prognostic Model and Nomogram Construction Based on a Novel Ferroptosis-Related Gene Signature in Lower-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junsheng Zhao ◽  
Zhengtao Liu ◽  
Xiaoping Zheng ◽  
Hainv Gao ◽  
Lanjuan Li
Keyword(s):  
Risk Model ◽  
Survival Curve ◽  
External Validation ◽  
Enrichment Analysis ◽  
Predictive Performance ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Low Grade ◽  
Lower Grade ◽  
Genome Atlas

Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years.Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals’ one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell’s concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature.Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan–Meier (K−M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG.Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

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