N6-Methyladenosine-Related RNA Signature Predicting the Prognosis of Ovarian Cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Jiao Jiao ◽  
Longyang Jiang ◽  
Yang Luo
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Prognostic Indicator ◽  
Gene Signature ◽  
Genetic Alterations ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Background: N6-Methyladenosine (m6A) RNA methylation is the most universal mRNA modification in eukaryotic cells. M6A mRNA modification affects almost every phases of RNA processing, including splicing, decay, export, translation and expression. Several patents have reported the application of m6A mRNA modification in cancer diagnosis and treatment. Ovarian cancer is the leading cause of death among all gynecological cancers. It is urgent to identify new biomarkers for early diagnosis and prognosis of ovarian cancer. Objective: In the current study, we aimed to evaluate the m6A RNA methylation regulators and m6A related genes and establish a new gene signature panel for prognosis of ovarian cancer. Method: We downloaded the Mutations data, FPKM data and corresponding clinical information of 373 patients with ovarian cancer (OC) from the TCGA database. We performed LASSO regression analysis and multivariate cox regression analysis to develop a risk-identifying gene signature panel. Results: A total of 317 candidate m6A RNA methylation related genes were obtained. Finally, 12 -genes (WTAP, LGR6, ZC2HC1A, SLC4A8, AP2A1, NRAS, CUX1, HDAC1, CD79A, ACE2, FLG2 and LRFN1) were selected to establish the signature panel. We analyzed the genetic alterations of the selected 12 -genes in OC using cBioPortal database. Among the 373 patients, 368 patients have mutations. The results showed that all queried genes were altered in 137 of 368 cases (37.23%). The 12-gene signature panel was confirmed as an independent prognostic indicator (P =2.29E-18, HR = 1.699, 95% CI = 1.508-1.913). Conclusion: We established an effective m6A-related gene signature panel consisted of 12 -genes, which can predict the outcome of patients with OC. The high risk score indicates unfavorable survival. Our study provided novel insights into the relationship between m6A and OC. This gene signature panel will be helpful in identifying poor prognostic patients with OC and could be a promising prognostic indicator in clinical practice.

scholarly journals Molecular Characterization and Clinical Relevance of m6A RNA Methylation Regulators in Cervical Cancer

2020 ◽  
Author(s):  
Jin Chen ◽  
Ji He ◽  
Xiaolei Ma ◽  
Xia Guo
Keyword(s):  
Cervical Cancer ◽  
Clinical Relevance ◽  
Cox Regression ◽  
Critical Role ◽  
The Cancer Genome Atlas ◽  
Rna Modification ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background: RNA modification, such as methylation of N6 adenosine (m6A), plays a critical role in many biological processes. However, the role of m6A RNA modification in cervical cancer (CC) remains largely unknown. Methods: The present study systematically investigated the molecular signatures and clinical relevance of 20 m6A RNA methylation regulators (writers, erasers, readers) in CC. The mRNA expression and clinical significance of m6A-related genes were investigated using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cervical cancer cohort. Mutations, copy number variation (CNV), differential expression, gene ontology analysis and the construction of a mRNA-microRNA regulatory network were performed to investigate the underlying mechanisms involved in the abnormal expression of m6A-related genes. Results: We found inclusive genetic information alterations among the m6A regulators and that their transcript expression levels were significantly associated with cancer hallmark-related pathways activity, such as the PI3K-AKT signaling pathway, microRNAs in cancer and the focal adhesion pathway, which were significantly enriched. Moreover, m6A regulators were found to be potentially useful for prognostic stratification and we identified FMR1 and ZC3H13 as potential prognostic risk oncogenes by LASSO regression. The ROC curves of 3, 5 and 10 years were 0.685, 0.726 and 0.741, respectively. The specificity for 3, 5 and 10 years were 0.598, 0.631 and 0.833, the sensitivity were 0.707, 0.752 and 0.811, respectively. Conclusions: Multivariable Cox regression analysis revealed that the risk score is an independent prognostic marker and can be used to predict the clinical and pathological features of CC.

scholarly journals Expression Status and Prognostic Value of m6A RNA Methylation Regulators in Lung Adenocarcinoma

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 619
Author(s):  
Xiuhong Li ◽  
Zian Feng ◽  
Rui Wang ◽  
Jie Hu ◽  
Xiaodong He ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
M6a Rna Methylation ◽  
Prediction Efficiency

N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. This paper mainly discusses the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) to identify novel prognostic biomarkers. The gene expression data of 19 m6A methylation regulators in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. We selected three significantly differentially expressed m6A regulators in LUAD to construct the risk signature, and evaluated its prognostic prediction efficiency using the receiver operating characteristic (ROC) curve. Kaplan–Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of the risk signature. The ROC curve indicated that the area under the curve (AUC) was 0.659, which means that the risk signature had a good prediction efficiency. The results of the Kaplan–Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD. In addition, we explored the differential signaling pathways and cellular processes related to m6A methylation regulators in LUAD.

scholarly journals Development and Validation of a 23-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Jichang Liu ◽  
Yadong Wang ◽  
Weiqing Zhong ◽  
Yong Liu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Protein Interactions ◽  
Cox Regression ◽  
External Validation ◽  
Predictive Performance ◽  
Gene Signature ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Abstract Background: Lung cancer remains the most fatal tumorous disease in the worldwide. Among that, lung adenocarcinoma (LUAD) was the most common histological type. A precise and concise prognostic model was urgently needed of LUAD. We developed a 23-gene signature for prognosis prediction based on EMT, immune and stromal datasets.Methods: Univariate Cox regression analysis was performed to select genes which were significantly associated with overall survival (OS) of the TCGA LUAD cohorts. LASSO regression and multivariate Cox regression analysis was used to build the multi-gene signature. Enrichment analyses and a protein-protein interactions (PPI) network were performed to show the interaction and functions of the signature. A nomogram was developed based on risk score and other clinical features. Predictive performance of the signature was externally validated in two independent datasets from Gene Expression Omnibus (GSE37745 and GSE13213).Results: A total of 1334 EMT, immune and stromal associated genes were obtained. After LASSO regression and multivariate Cox regression analysis, a 23-gene signature for risk stratification was built. K-M curves showed that the patients with high risk had a poorer outcome. Finally, a nomogram was built to predict prognosis. The predictive performance of the 23-gene signature was confirmed in internal and external validation.Conclusion: We developed and verified a 23-gene signature based on EMT, immune and stromal gene sets. It provided a convenient and concise tool for risk stratificationand individual medicine.

scholarly journals A Five-Gene Signature for Recurrence Prediction of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zeyu Wang ◽  
Ningning Zhang ◽  
Jiayu Lv ◽  
Cuihua Ma ◽  
Jie Gu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Early Stage ◽  
Gene Signature ◽  
Functional Enrichment ◽  
High Recurrence Rate ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Recurrence Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim. To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods. The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results. We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion. The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

scholarly journals Prognostic Value of an m6A RNA Methylation Regulator-Based Signature in Patients with Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaomin Wu ◽  
Xiaojing Zhang ◽  
Leilei Tao ◽  
Xichao Dai ◽  
Ping Chen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
Cluster 2

Purposes. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Recent researches have demonstrated that m6A methylation regulators play a key role in various cancers, such as gastric cancer and colon adenocarcinoma. Several m6A methylation regulators are reported to predict the prognosis of HCC. Therefore, there is a need to further identify the predictive value of m6A methylation regulators in HCC. Methods. We utilized The Cancer Genome Atlas (TCGA) database to obtain the gene expression profile of m6A RNA methylation regulators and clinical information for patients with HCC. Besides, we identified two clusters of HCC with various clinical factors by consensus clustering analysis. Then the least absolute shrinkage and selection operator (LASSO) and the Cox regression analysis were applied to construct a prognostic signature. Results. Except for ZC3H13 and METTL14, a majority of the thirteen m6A RNA methylation regulators were significantly overexpressed in HCC specimens. HCC patients were classified into two groups (cluster 1 and cluster 2). The cluster 1 was with a significantly worse prognosis than cluster 2, and most of the 13 known m6A RNA methylation regulators were upregulated in cluster 1. Besides, we developed a prognostic signature consisting of YTHDF2, YTHDF1, METTL3, KIAA1429, and ZC3H13, which could successfully differentiate high-risk patients. More importantly, univariate and multivariate Cox regression analysis indicated that the signature-based risk score was an independent prognostic factor for patients with HCC. Conclusions. Our study showed these five m6A RNA methylation regulators can be used as practical and reliable prognostic tools of HCC, which might have potential value for therapeutic strategies.

scholarly journals Identification of the Signature Associated With m6A RNA Methylation Regulators and m6A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Bingzhou Guo ◽  
Hongliang Zhang ◽  
Jinliang Wang ◽  
Rilige Wu ◽  
Junyan Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Test Group ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Chi Square Analysis ◽  
Random Survival Forest

BackgroundN6-methyladenosine (m6A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m6A RNA methylation regulators and m6A-related genes to understand the prognosis of early lung adenocarcinoma.MethodsThe relevant datasets were utilized to analyze 21 m6A RNA methylation regulators and 5,486 m6A-related genes in m6Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan–Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed.ResultsRespectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m6A methylation.ConclusionThe findings of this study suggested that associated with m6A RNA methylation regulators and m6A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.

scholarly journals Prognosis Implication of a Novel Metabolism-Related Gene Signature in Ewing Sarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhuo-Yuan Chen ◽  
Huiqin Yang ◽  
Jie Bu ◽  
Qiong Chen ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Ewing Sarcoma ◽  
Cox Regression ◽  
Roc Curves ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Prognostic Indicators ◽  
Immune Checkpoints ◽  
Lasso Regression ◽  
Cox Regression Analysis

Ewing sarcoma (ES) is one of the most common bone cancers in adolescents and children. Growing evidence supports the view that metabolism pathways play critical roles in numerous cancers (He et al. (2020)). However, the correlation between metabolism-associated genes (MTGs) and Ewing sarcoma has not been investigated systematically. Here, based on the univariate Cox regression analysis, we get survival genes from differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) cohort. Multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to establish the MTG signature. Comprehensive survival analyses including receiver operating characteristic (ROC) curves and Kaplan–Meier analysis were applied to estimate the independent prognostic value of the signature. The ICGC cohort served as the validation cohort. A nomogram was constructed based on the risk score of the MTG signature and other independent clinical variables. The CIBERSORT algorithm was applied to estimate immune infiltration. In addition, we explored the correlation between MTG signature and immune checkpoints. Collectively, this work presents a novel MTG signature for prognostic prediction of Ewing sarcoma. It also suggests six genes that are potential prognostic indicators and therapeutic targets for ES.

scholarly journals Identification of a New Prognostic Risk Signature of Clear Cell Renal Cell Carcinoma Based on N6-Methyladenosine RNA Methylation Regulators

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Yan Zhang ◽  
Yao Yao ◽  
Xiaochen Qi ◽  
Jianyi Li ◽  
Meihong Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

As the most prevalent internal eukaryotic modification, N6-methyladenosine (m6A) is installed by methyltransferases, removed by demethylases, and recognized by readers. However, there are few studies on the role of m6A in clear cell renal cell carcinoma (ccRCC). In this study, we researched the RNA-seq transcriptome data of ccRCC in the TCGA dataset and used bioinformatics analyses to detect the relationship between m6A RNA methylation regulators and ccRCC. First, we compared the expression of 18 m6A RNA methylation regulators in ccRCC patients and normal tissues. Then, data from ccRCC patients were divided into two clusters by consensus clustering. LASSO Cox regression analysis was used to build a risk signature to predict the prognosis of patients with ccRCC. An ROC curve, univariate Cox regression analysis, and multivariate Cox regression analysis were used to verify this risk signature’s predictive ability. Then, we internally validated this signature by random sampling. Finally, we explored the role of the genes in the signature in some common pathways. Gene distribution between the two subgroups was different; cluster 2 was gender-related and had a worse prognosis. IGF2BP3, IGF2BP2, HNRNPA2B1, and METTL14 were chosen to build the risk signature. The overall survival of the high- and low-risk groups was significantly different ( p = 7.47 e − 12 ). The ROC curve also indicated that the risk signature had a decent predictive significance ( AUC = 0.72 ). These results imply that the risk signature has a potential value for ccRCC treatment.

scholarly journals Comprehensive Analysis of m6A RNA Methylation Regulators in the Prognosis and Immune Microenvironment of Multiple Myeloma

2021 ◽  
Vol 11 ◽  
Author(s):  
Rui Liu ◽  
Ying Shen ◽  
Jinsong Hu ◽  
Xiaman Wang ◽  
Dong Wu ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Immune Infiltration ◽  
M6a Rna Methylation

BackgroundN6-methyladenosine is the most abundant RNA modification, which plays a prominent role in various biology processes, including tumorigenesis and immune regulation. Multiple myeloma (MM) is the second most frequent hematological malignancy.Materials and MethodsTwenty-two m6A RNA methylation regulators were analyzed between MM patients and normal samples. Kaplan–Meier survival analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were employed to construct the risk signature model. Receiver operation characteristic (ROC) curves were used to verify the prognostic and diagnostic efficiency. Immune infiltration level was evaluated by ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA).ResultsHigh expression of HNRNPC, HNRNPA2B1, and YTHDF2 and low expression of ZC3H13 were associated with poor survival. Based on these four genes, a prognostic risk signature model was established. Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic factor of MM. Enrichment analysis showed that cell cycle, immune response, MYC, proteasome, and unfold protein reaction were enriched in high-risk MM patients. Furthermore, patients with higher risk score exhibited lower immune scores and lower immune infiltration level.ConclusionThe m6A-based prognostic risk score accurately and robustly predicts the survival of MM patients and is associated with the immune infiltration level, which complements current prediction models and enhances our cognition of immune infiltration.

scholarly journals The prognostic values of m6A RNA methylation regulators in Uveal melanoma

2020 ◽  
Author(s):  
Jing Tang ◽  
Qi Wan ◽  
Jianqun Lu
Keyword(s):  
Regression Analysis ◽  
Uveal Melanoma ◽  
Cox Regression ◽  
Strategy Development ◽  
Lower Percentage ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Mtorc1 Signaling ◽  
M6a Rna Methylation

Abstract Background: The aim of this study was to identify gene signatures and prognostic values of m6A methylation regulators in uveal melanoma (UM).Methods: The RNA sequencing dataset and corresponding clinical information were downloaded from TCGA and GEO database. Based on the expression of m6A RNA methylation regulators, the patients were further clustered into different groups by applying the “ClassDiscovery” algorithm. Best survival analysis was performed to select prognostic m6A regulators and multivariate cox regression analysis was applied to constructed m6A regulators signature. The association between mutations and m6A regulators was assessed by Kruskal−Wallis tests and clinical characteristics were examined by using chi-square test.Results: Totally, we identified two molecular subtypes of UM (C1/2) by applying consensus clustering to m6A RNA methylation regulators. In contrast to the C1 subtype, the C2 subtype associates with a better prognosis, have higher percentage of subtype 1 and lower percentage of Monosomy 3 which have been regarded as the well established prognostic markers for UM. The malignant hallmarks of mTORC1 signaling, oxidative phosphorylation, interferon-a response and apoptosis signaling are also significantly enriched in the C1 subtype. Moreover, a 3-m6A regulators signature was constructed by multivariate cox regression analysis method, which closely correlated with chromosome 3 status, subtype 1 of UM and can robustly predict patients’ overall survival time.Conclusions: m6A RNA methylation regulators take a crucial role in the potential malignant progression and prognostic value of UM and might be regarded as a new promising biomarker for UM prognosis and treatment strategy development.

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