Expression patterns and a prognostic model of m6A-associated regulators in prostate adenocarcinoma

2020 ◽  
Vol 14 (18) ◽  
pp. 1717-1731
Author(s):  
Song Ou-Yang ◽  
Ji-Hong Liu ◽  
Qin-Zhang Wang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Clinical Biomarkers

Aim: To study the expression patterns and prognostic value of the m6A-associated regulators in prostate adenocarcinoma (PRAD). Materials & methods: The mRNA expression and clinical data were downloaded from ‘The Cancer Genome Atlas database’. The m6A-associated variants were downloaded from m6AVar database, and combined with 14 common m6A regulators for subsequent analysis. One-way analysis of variance, univariate Cox regression analysis and least absolute shrinkage and selection operator algorithm were successively applied to obtain the ultimate regulators and prognostic model. Finally, consensus clustering, protein–protein interaction (PPI) and enrichment analysis were performed. Result: Nine regulators were obtained. PRAD patients could be classified into two risk groups and subclasses with significant survival differences by the prognostic model and consensus clustering, respectively. Conclusion: All these nine regulators were related to prognosis in PRAD, and could be used as clinical biomarkers.

scholarly journals The Prognostic Value of m6A-related LncRNAs in Patients with HNSCC

2021 ◽  
Author(s):  
Liu-qing Zhou ◽  
Jie-yu Zhou ◽  
Yao Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

Abstract Background: N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. m6A modifications are known to modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood.Methods: Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a risk prognostic model, and consensus clustering analysis, we analyzed the 12 m6A-related lncRNAs in HNSCC samples data using the data from The Cancer Genome Atlas (TCGA) database.Results: We found twelve m6A-related lncRNAs in the training cohort and validated in all cohorts by Kaplan-Meier and Cox regression analyses, and revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC prognosis. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs.Conclusions: In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC prognosis and provide potential prediction outcome and new therapeutic target for HNSCC patients.

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

scholarly journals The prognostic value of m6A-related LncRNAs in patients with HNSCC: bioinformatics analysis of TCGA database

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Liu-qing Zhou ◽  
Jin-xiong Shen ◽  
Jie-yu Zhou ◽  
Yao Hu ◽  
Hong-jun Xiao
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cancer Genome Atlas

AbstractN6-methyladenosine (m6A) modifications play an essential role in tumorigenesis. These modifications modulate RNAs, including mRNAs and lncRNAs. However, the prognostic role of m6A-related lncRNAs in head and neck squamous cell carcinoma (HNSCC) is poorly understood. Based on LASSO Cox regression, enrichment analysis, univariate and multivariate Cox regression analysis, a prognostic risk model, and consensus clustering analysis, we analyzed 12 m6A-related lncRNAs in HNSCC sample data from The Cancer Genome Atlas (TCGA) database. We found 12 m6A-related lncRNAs in the training cohort and validated them in all cohorts by Kaplan–Meier and Cox regression analyses, revealing their independent prognostic value in HNSCC. Moreover, ROC analysis was conducted, confirming the strong predictive ability of this signature for HNSCC survival. GSEA and detailed immune infiltration analyses revealed specific pathways associated with m6A-related lncRNAs. In this study, a novel risk model including twelve genes (SAP30L-AS1, AC022098.1, LINC01475, AC090587.2, AC008115.3, AC015911.3, AL122035.2, AC010226.1, AL513190.1, ZNF32-AS1, AL035587.1 and AL031716.1) was built. It could accurately predict HNSCC outcomes and could provide new therapeutic targets for HNSCC patients.

scholarly journals Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Man Jiang ◽  
Na Zhou ◽  
Zhixuan Ren ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Integrated Analysis ◽  
Consensus Clustering ◽  
Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals The Identification of Critical m6A RNA Methylation Regulators as Malignant Prognosis Factors in Prostate Adenocarcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Jiaju Xu ◽  
Yuenan Liu ◽  
Jingchong Liu ◽  
Tianbo Xu ◽  
Gong Cheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Consensus Clustering ◽  
Human Beings ◽  
Rna Methylation

RNA methylation accounts for over 60% of all RNA modifications, and N6-methyladenosine (m6A) is the most common modification on mRNA and lncRNA of human beings. It has been found that m6A modification occurs in microRNA, circRNA, rRNA, and tRNA, etc. The m6A modification plays an important role in regulating gene expression, and the abnormality of its regulatory mechanism refers to many human diseases, including cancers. Pitifully, as it stands there is a serious lack of knowledge of the extent to which the expression and function of m6A RNA methylation can influence prostate cancer (PC). Herein, we systematically analyzed the expression levels of 35 m6A RNA methylation regulators mentioned in literatures among prostate adenocarcinoma patients in the Cancer Genome Atlas (TCGA), finding that most of them expressed differently between cancer tissues and normal tissues with the significance of p < 0.05. Utilizing consensus clustering, we divided PC patients into two subgroups based on the differentially expressed m6A RNA methylation regulators with significantly different clinical outcomes. To appraise the discrepancy in total transcriptome between subgroups, the functional enrichment analysis was conducted for differential signaling pathways and cellular processes. Next, we selected five critical genes by the criteria that the regulators had a significant impact on prognosis of PC patients from TCGA through the last absolute shrinkage and selection operator (LASSO) Cox regression and obtained a risk score by weighted summation for prognosis prediction. The survival analysis curve and receiver operating characteristic (ROC) curve showed that this signature could excellently predict the prognosis of PC patients. The univariate and multivariate Cox regression analyses proved the independent prognostic value of the signature. In summary, our effort revealed the significance of m6A RNA methylation regulators in prostate cancer and determined a m6A gene expression classifier that well predicted the prognosis of prostate cancer.

scholarly journals The Prognostic Value of Metabolic Genes in Gastric Adenocarcinoma

2021 ◽  
Author(s):  
Sanling Huang ◽  
Zefu Liu ◽  
Fangzhou Xu ◽  
Chongxiang Chen ◽  
Hongyu Zhang
Keyword(s):  
Gastric Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background: A great number of metabolic genes have been discovered in gastric cancer (GC); however, their prognostic roles remain incompletely clear so far. Methods: The Cancer Genome Atlas-stomach adenocarcinoma, and GSE84437 dataset collected via the Gene Expression Omnibus (GEO) database were utilized for retrieving those clinicopathological data and RNA expression patterns. Besides, the signature was obtained through the lasso Cox regression model and univariate Cox regression analysis. A novel 13-gene metabolic signature (including GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for predicting the prognosis for gastric adenocarcinoma. Results: The results suggested that, the survival in high risk group was remarkably dismal compared with that in low risk group. In addition, that as-constructed signature had been identified as a factor to independently predict the prognosis for gastric adenocarcinoma patients. Moreover, the signature-based nomogram showed certain benefits in predicting the overall survival (OS). Besides, results of gene set enrichment analysis (GSEA) suggested that some pathways were markedly enriched, which help to illustrate the possible mechanisms. Conclusions: The new 13-gene metabolic model is constructed in this study to predict the prognosis for GC. It probably reflects dysregulation in the metabolic microenvironment, in the meantime of providing metabolic treatment biomarkers, and predicting the treatment response to gastric adenocarcinoma.

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 of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Pingxiao Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

Sign in / Sign up

Export Citation Format

Share Document