The prognostic values of m6A RNA methylation regulators in Uveal melanoma

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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The prognostic values of m6A RNA methylation regulators in Uveal melanoma

10.21203/rs.2.12974/v3 ◽

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 Purpose: 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. Result: 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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The prognostic values of m6A RNA methylation regulators in Uveal melanoma

10.21203/rs.2.12974/v1 ◽

2019 ◽

Author(s):

Qi Wan ◽

Jing Tang ◽

Jianqun Lu

Keyword(s):

Uveal Melanoma ◽

Clinical Information ◽

Strategy Development ◽

Lower Percentage ◽

Consensus Clustering ◽

Lasso Regression ◽

Chi Square ◽

Rna Methylation ◽

Mtorc1 Signaling ◽

M6a Rna Methylation

Abstract Purpose The aim of this study were 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. Survival analysis was performed using log-rank tests and LASSO regression model. The association between mutations and m6A regulators was assessed by student t tests and clinical characteristics was examined by using chi-square test. Result Totally, we identified two molecular subtypes of UM (C1/2) by applying consensus clustering to m6A RNA methylation regulators. Contrasted to the C1 subtype, the C2 subtype associates with a better prognosis, longer survival time and lower percentage of Monosomy 3. The malignant hallmarks of mTORC1 signaling, oxidative phosphorylation, interferon-a response and apoptosis signaling are also significantly enriched in the C1 subtype. Moreover, a 2-m6A regulators signature was screened out by LASSO method, which can robustly predict UM patients survival. 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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The N6-Methyladenosine- (m6A-) Associated Genes Act as Strong Key Biomarkers for the Prognosis of Pancreatic Adenocarcinoma

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8715823 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Fei Li ◽

Ping Zhang

Keyword(s):

Pancreatic Adenocarcinoma ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Pancreatic Tumors ◽

Molecular Characteristics ◽

Main Role ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Background. Pancreatic adenocarcinoma (PAAD) has become the major cause of cancer-related deaths globally. The m6A (N6-methyladenosine) alteration plays a crucial function in carcinogenesis and tumor progression. The role of genes related to m6A and their expression level in pancreatic cancer is not identified yet. The objective of this research analysis is a demonstration of the m6A RNA methylation regulators based as biomarkers for the PAAD diagnosis. Methods. About 23 extensively reported m6A RNA methylation regulators were identified through the Cancer Genome Atlas (TCGA) database. This identification was based on consensus clustering analysis, protein-protein integration (PPI) analysis, risk prognostic model, Cox-regression analysis, String Spearman analysis, and LASSO Cox-regression. Results. Herein, we conclude that 23 m6A methylation regulators have a strong link with the clinical and molecular characteristics of PAAD. The three subgroups (1/2) of pancreatic adenocarcinoma were identified using the clustering of 23 m6A regulators. Subgroup cluster 2 had a lower survival rate than the subgroup of cluster 1, and the difference in grades between the two groups was substantial. An assessment was performed using the 23 reported m6A methylation regulators. Eight of these can be used as independent PAAD prognostic markers. The consequences of variable IGF2BP3 expression in PAAD were then investigated further. Conclusions. The key finding of this study was that the m6A methylation regulator gene has the main role in pancreatic tumors, and it may be used as a biomarker in the prognosis of the PAAD and for therapy purposes.

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

Life ◽

10.3390/life11070619 ◽

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.

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

Recent Patents on Anti-Cancer Drug Discovery ◽

10.2174/1574892816666210615164645 ◽

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.

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Identification of a m6A RNA methylation regulators-based signature for predicting the prognosis of esophageal adenocarcinoma

10.21203/rs.3.rs-50655/v1 ◽

2020 ◽

Author(s):

Yue Zhou ◽

Shuyan Li ◽

Liqing Zou ◽

Tiantian Guo ◽

Xi Yang ◽

...

Keyword(s):

Esophageal Adenocarcinoma ◽

Clustering Analysis ◽

Cox Regression ◽

Area Under The Curve ◽

Clinical Information ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract BackgroundN6-methyladenosine (m6A) is an abundant modification in RNAs that affects RNA metabolism, and it is reported to be closely related to cancer occurrence and metastasis. The aim of this study was to identify novel prognostic biomarkers by using m6A RNA methylation regulators capable of improving the risk-stratification criteria of survival for esophageal adenocarcinoma patients.MethodsThe gene expression data of 16 m6A methylation regulators and its relevant clinical information were extracted from The Cancer Genome Atlas (TCGA) database. The expression pattern of these m6A methylation regulators was evaluated. Consensus clustering analysis was conducted to identify clusters of esophageal adenocarcinoma patients with different prognosis. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were performed to construct multiple-gene risk signature. A survival analysis was carried out to determine the prognosis significance.ResultsTen m6A methylation regulators (HNRNPA2B1, HNRNPC, YTHDF1, METTL3, YTHDF2, RBM15, YTHDC1, WTAP, KIAA1429 and YTHDF3) showed significant up-regulation in tumor tissue. Consensus clustering analysis identified three clusters of esophageal adenocarcinoma patients with different overall survival. A five-gene signature, HNRNPA2B1, KIAA1429, WTAP, METTL16 and ALKBH5, was constructed to serve as a prognostic indicator for distinguish esophageal adenocarcinoma patients with different prognosis. The receiver operator characteristic (ROC) curve which indicated the area under the curve (AUC) were 0.803, demonstrated that the prognostic signature had preferable prediction efficiency.Conclusionsm6A methylation regulators exert as potential biomarkers for prognostic stratification of esophageal adenocarcinoma patients and might help clinicians make individualized therapy for this patient population.

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

BioMed Research International ◽

10.1155/2020/2053902 ◽

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.

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

Journal of Immunology Research ◽

10.1155/2021/6617841 ◽

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.

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

Frontiers in Oncology ◽

10.3389/fonc.2021.731957 ◽

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.

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A m6A-related lncRNA Signature as a Novel Prognostic Factor for Gastric Cancer

10.21203/rs.3.rs-101884/v1 ◽

2020 ◽

Author(s):

Haixu Wang ◽

Qingkai Meng ◽

Bin Ma

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Cox Regression Analysis ◽

Rna Methylation ◽

Complete Dataset ◽

M6a Rna Methylation

Abstract Background: N6-methyladenosine (m6A)isa common form of mRNA modification regulated by m6A RNA methylation regulators. However, studies have not explored the role of m6A-related lncRNA in gastric cancer (GC). This study aimed atexploring biological and prognostic roles of m6A-related lncRNA in GC. Methods: We identified 800 m6A-related lncRNAs through correlation analysis of 13 main m6A RNA methylation regulators and all lncRNAs expressed in GC. We further categorized patients into train group and testing group equally. Results: A total of 11 m6A-related lncRNA signature associated with prognosis of GC were identified through univariate cox regression analysis and LASSO analysis which was validated using the testing dataset and complete dataset, respectively. More deaths and shorter survival time were reported for patients in the high-risk group compared to low-risk group. lncRNA signature is an independent prognosis predictor as shown by cox regression analysis of the complete dataset. Moreover, genes involved in base excision repair were highly expressed in patients in the high-risk group as shown by gene set enrichment analysis (GSEA) result whereas ECM receptor interaction and focal adhesion pathway were enriched in low-risk group. A nomogram on independent factors showed clinical net benefit asan overall survival predictor of GC. In addition, we identified four subgroups of GC patients with significant differences in overall survival (OS).Subgroup C1 and C2 responded well to immunotherapy, compared to subgroup C3 and C4. Conclusions: m6A-related lncRNA signature and four molecular subgroups provide information on the underlying molecular mechanism of GC and provide for a basis for development ofpersonalized therapy.

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