scholarly journals Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients With Colon Cancer

2021 ◽  
Author(s):  
ZhiHua Chen ◽  
YiLin Lin ◽  
SuYong Lin ◽  
YiSu Liu ◽  
Yan Zheng ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Copy Number ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Copy Number Variations ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Number Variation

Abstract Backgroud: Tumour recurrence and metastasis lead to poor prognosis in colon cancer (COAD). Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism.Methods: The lncRNA expression profile were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 into training and testing cohort. In the training, we performed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO. Results: An 11-lncRNA signature that was significantly associated with prognosis was constructed in the training (P<0.0001, HR=2.014) , And this signature was validated in the testing (P=0.0019, HR=3.374) and GSE17536 (P=0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-related risk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways.Conclusions: We identified an signature has strong robustness and can stably predict the prognosis of COAD in different platforms and may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker.

scholarly journals Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients with COAD

2021 ◽  
Author(s):  
ZhiHua Chen ◽  
YiLin Lin ◽  
SuYong Lin ◽  
Ji Gao ◽  
Shao-Qin Chen
Keyword(s):  
Copy Number ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Copy Number Variations ◽  
Integrative Analysis ◽  
Transcriptome Data ◽  
Cox Regression Analysis ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Internal Mechanism

Abstract Backgroud: Tumour recurrence and metastasis lead to poor prognosis incolon cancer(COAD). Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism.Methods: The lncRNA expression profile were collected fromThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 intotraining andtesting cohort. In the training, weperformed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO.Results: An 11-lncRNA signature that was significantly associated with prognosiswas constructed in the training (P<0.0001, HR=2.014) , And this signature was validated in the testing(P=0.0019, HR=3.374) and GSE17536(P=0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-relatedrisk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways.Conclusions: We identifiedansignature has strong robustness and can stably predict the prognosis of COAD in different platformsand may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker.

scholarly journals Integrative Analysis of ceRNA Network Reveals Functional lncRNAs in Intrahepatic Cholangiocarcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Dongkai Zhou ◽  
Bingqiang Gao ◽  
Qifan Yang ◽  
Yang Kong ◽  
Weilin Wang
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Adaptive Immune Response ◽  
Gene Expression Omnibus ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Messenger Rnas ◽  
Cox Regression Analysis ◽  
Cerna Network

Intrahepatic cholangiocarcinoma (ICC) is the second most common lethal liver cancer worldwide. Currently, despite the latest developments in genomics and transcriptomics for ICC in recent years, the molecular pathogenesis promoting ICC remains elusive, especially in regulatory mechanisms of long noncoding RNAs (lncRNAs), which acts as competing endogenous RNA (ceRNA). In order to elucidate the molecular mechanism of functional lncRNA, expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from The Cancer Genome Atlas (TCGA) database and an integrative analysis of the ICC-associated ceRNA network was performed. Moreover, gene oncology enrichment analyses for the genes in the ceRNA network were implemented and novel prognostic biomarker lncRNA molecules were identified. In total, 6,738 differentially expressed mRNAs (DEmRNAs), 2,768 lncRNAs (DElncRNAs), and 173 miRNAs (DEmiRNAs) were identified in tumor tissues and adjacent nontumor ICC tissues with the thresholds of adjusted P<0.01 and logFC>2. An ICC-specific ceRNA network was successfully constructed with 30 miRNAs, 16 lncRNAs, and 80 mRNAs. Gene oncology enrichment analyses revealed that they were associated with the adaptive immune response, T cell selection and positive regulation of GTPase activity categories. Among the ceRNA networks, DElncRNAs ARHGEF26-AS1 and MIAT were found to be hub genes in underexpressed and overexpressed networks, respectively. Notably, univariate Cox regression analysis indicated that DElncRNAs HULC significantly correlated with overall survival (OS) in ICC patients (P value < 0.05), and an additional survival analysis for HULC was reconfirmed in an independent ICC cohort from the Gene Expression Omnibus (GEO) database. These findings contribute to a more comprehensive understanding of the ICC-specific ceRNA network and provide novel strategies for subsequent functional studies of lncRNAs in ICC.

scholarly journals TEAD4 is a novel independent predictor of prognosis in LGG patients with IDH mutation

2021 ◽  
Vol 16 (1) ◽  
pp. 323-335
Author(s):  
Hai-Yan Yuan ◽  
Ya-Juan Lv ◽  
Yi Chen ◽  
Dan Li ◽  
Xi Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Disease Free Survival ◽  
Predictive Biomarkers ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Cox Regression Analysis ◽  
Study Results ◽  
Number Variation ◽  
Genome Atlas

Abstract TEA domain family members (TEADs) play important roles in tumor progression. Till now, the genomic status of TEADs in patients with glioma has not been well investigated. To confirm whether the genomic status of TEADs could affect the prognosis of patients with glioma, the copy number variation (CNV), mutation and expression data of glioma cohorts in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas were comprehensively analyzed. Results showed that TEAD CNV frequency in lower grade gliomas (LGGs) was higher than in glioblastoma multiforme (GBM). Multivariate cox regression analysis showed that TEAD4 CNV increase was significantly associated with overall survival (OS) and disease-free survival (DFS) in LGGs (OS p = 0.022, HR = 1.444, 95% CI: 1.054–1.978; DFS p = 0.005, HR = 1.485, 95% CI: 1.124–1.962), while not in GBM. Patients with TEAD4 CNV increase showed higher expression level of TEAD4 gene. In LGG patients with IDH mutation, those with higher TEAD4 expression levels had shorter OS and DFS. Integrating TEAD4 CNV increase, IDH mutations, TP53 mutation, ATRX mutation and 1p19q co-deletion would separate patients with LGG into four groups with significant differences in prognosis. These study results suggested that TEAD4 variations were independent predictive biomarkers for the prognosis in patients with LGG with IDH mutation.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Identification and validation of immunotypes for clear cell RCC prognosis

2021 ◽  
Author(s):  
Zhaolin Yang ◽  
Jiale Zhou ◽  
Yizheng Xue ◽  
Yu Zhang ◽  
Kaijun Zhou ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Clear Cell ◽  
Survival Rates ◽  
Prognostic Index ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

scholarly journals A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhentao Liu ◽  
Hao Zhang ◽  
Hongkang Hu ◽  
Zheng Cai ◽  
Chengyin Lu ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Methylation Status ◽  
Survival Rates ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

scholarly journals Identification and Validation of a Glycolysis Related Metabolic Reprogramming Gene Signature for Predicting Survival in Colon Cancer Patients

2021 ◽  
Author(s):  
Gang Liu ◽  
Xiaowang WU ◽  
Jian Chen
Keyword(s):  
Colon Cancer ◽  
Prediction Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Prognosis Model ◽  
The Relationship

Abstract Background Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with high mortality rate. Because of malignancy and easily metastasis feather, and limited treatments, the prognosis of CC remains poor. Glycolysis is a metabolic process of glucose in anoxic environments which is an important way to provide energy for tumor. The role of glycolysis in CC largely remains unknown and is necessary to be explored. Method In our study, we analyzed glycolysis related genes expression in CC, patients gene expression and corresponding clinical data were downloaded from GEO dataset, glycolysis related genes sets were collected from Msigdb. Through COX regression analysis, prognosis model based on glycolysis-related genes was established. The efficacy of gene model was tested by Survival analysis, ROC analysis and PCA analysis. Furthermore, the relationship between risk scores and clinical characteristic was researched. Results Our findings identified 13 glycolysis related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2 and OGDHL) consisted prognostic prediction model with relative high accuracy. The relationship between prediction model and clinical feathers were specifically studied, results showed age > 65years, TNM III-IV, T3-4, N1-3, M1 and high-risk score were independent prognostic risk factors with poorer prognosis. Finally, model genes were significantly expressed and EMT were activated in CC patients. Conclusion This study provided a new aspect to advance our understanding in the potential mechanism of glycolysis in CC.

scholarly journals Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jie Zhao ◽  
Rixiang Zhao ◽  
Xiaocen Wei ◽  
Xiaojing Jiang ◽  
Fan Su
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Ovarian Cancer Risk ◽  
Cox Regression Analysis ◽  
The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

scholarly journals Comprehensive Analysis and Prognosis Prediction of N6-methyladenosine-related Lncrnas in Immune Microenvironment Infiltration of Gastric Cancer

2021 ◽  
Author(s):  
Jianfeng Huang ◽  
Wenzheng Chen ◽  
Changyu Chen ◽  
Tao Xiao ◽  
Zhigang Jie
Keyword(s):  
Gastric Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Cytotoxic T Lymphocyte ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Rna Modification ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Abstract BackgroundN6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. MethodsIn this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. ResultsThe results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, our risk model showed that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (e.g., paclitaxel and oxaliplatin). ConclusionsThis evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and my lead to more effective immunotherapy and chemotherapy for patients with GC.

scholarly journals An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Qian Xu ◽  
Yurong Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Staging System ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Net Benefit ◽  
Cox Regression Analysis

Aging is an inevitable time-dependent process associated with a gradual decline in many physiological functions. Importantly, some studies have supported that aging may be involved in the development of lung adenocarcinoma (LUAD). However, no studies have described an aging-related gene (ARG)-based prognosis signature for LUAD. Accordingly, in this study, we analyzed ARG expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). After LASSO and Cox regression analyses, a six ARG-based signature (APOC3, EPOR, H2AFX, MXD1, PLCG2, and YWHAZ) was constructed using TCGA dataset that significantly stratified cases into high- and low-risk groups in terms of overall survival (OS). Cox regression analysis indicated that the ARG signature was an independent prognostic factor in LUAD. A nomogram based on the ARG signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Moreover, to visualize the prediction results, we established a web-based calculator yurong.shinyapps.io/ARGs_LUAD/. Calibration plots showed good consistency between the prediction of the nomogram and actual observations. Receiver operating characteristic curve and decision curve analyses indicated that the ARG nomogram had better OS prediction and clinical net benefit than the staging system. Taken together, these results established a genetic signature for LUAD based on ARGs, which may promote individualized treatment and provide promising novel molecular markers for immunotherapy.

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