The prognostic values of m6A RNA methylation regulators in Uveal melanoma

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 prognostic values of m6A RNA methylation regulators in Uveal melanoma

10.21203/rs.2.12974/v2 ◽

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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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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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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m6A RNA methylation regulators contribute to progression and impact the prognosis of breast cancer

10.1101/2020.10.13.20212332 ◽

2020 ◽

Author(s):

Wenjie Jiang ◽

Minglong Dong ◽

Zebin Hu ◽

Kaidi Wan ◽

Han Wang

Keyword(s):

Breast Cancer ◽

Risk Group ◽

Clinical Information ◽

High Risk Group ◽

Low Risk ◽

Lasso Regression ◽

Rna Methylation ◽

Prognostic Analysis ◽

The Difference ◽

M6a Rna Methylation

AbstractN6-methyladenosine (m6A) is the most commonly modified form of mRNA. M6A RNA methylation regulators are proved to be expressed clearly in some cancers by plenty of studies. Moreover, they also are proved to be indirectly involved in the growth of cancers. However, it remains unclear that the role of m6A RNA methylation regulator in the prognosis of breast cancer (BRCA). The data that we used in this study is the mRNA expression data obtained from the corresponding clinical information and the Tumor Genome Atlas (TCGA) database. And the goal we used the Wilcoxon rank-sum test was to evaluate the difference in the expression of m6A RNA methylation regulators in the normal group and the tumor group, and analyze the correlation between m6A RNA methylation regulators. We identified two subgroups of BRCA (cluster1 and 2) by using the K-mean algorithm and analyzing the correlation between clinic information and subgroups. The LASSO regression model then was used to figure out three m6A RNA methylation regulators, namely YTHDF3, ZC3H13, and HNRNPC. The riskScore of each patient was calculated according to the regression coefficients of the three m6A RNA methylation regulators. Base on the riskScore, we divided the patients into two groups, the high-risk group, and the low-risk group. After analyzing, we found that the overall survival rate (OS) of the low-risk group was higher than that of the other group. We conducted a univariate and multi-factor independent prognostic analysis of riskScore and three m6A RNA methylation regulators, and found that riskScore has a significant correlation with BRCA.In conclusion, the m6A RNA methylation regulator is closely related to the development of BRCA, and the prognostic factor riskScore obtained from the regression of the expression of the three m6A RNA methylation regulators in the human body are likely to guide the individualization of BRCA patients A useful prognostic biomarker for treatment.

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PD-L1 and Immune Infiltration of m6A RNA Methylation Regulators and Its miRNA Regulators in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5516100 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yingxue Lin ◽

Yinhui Yao ◽

Ying Wang ◽

Lingdi Wang ◽

Haipeng Cui

Keyword(s):

Risk Score ◽

Immune Cell ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Consensus Clustering ◽

Lasso Regression ◽

Rna Methylation ◽

Immune Infiltration ◽

Significant Difference ◽

M6a Rna Methylation

Background. The aim of this study was to systematically evaluate the relationship between the expression of m6A RNA methylation regulators and prognosis in HCC. Methods. We compared the expression of m6A methylation modulators and PD-L1 between HCC and normal in TCGA database. HCC samples were divided into two subtypes by consensus clustering of data from m6A RNA methylation regulators. The differences in PD-L1, immune infiltration, and prognosis between the two subtypes were further compared. The LASSO regression was used to build a risk score for m6A modulators. In addition, we identified miRNAs that regulate m6A regulators. Results. We found that fourteen m6A regulatory genes were significantly differentially expressed between HCC and normal. HCC samples were divided into two clusters. Of these, there are higher PD-L1 expression and poorer overall survival (OS) in cluster 1. There was a significant difference in immune cell infiltration between cluster 1 and cluster 2. Through the LASSO model, we obtained 12 m6A methylation regulators to construct a prognostic risk score. Compared with patients with a high-risk score, patients with a low-risk score had upregulated PD-L1 expression and worse prognosis. There was a significant correlation between risk score and tumor-infiltrating immune cells. Finally, we found that miR-142 may be the important regulator for m6A RNA methylation in HCC. Conclusion. Our results suggest that m6A RNA methylation modulators may affect the prognosis through PD-L1 and immune cell infiltration in HCC patients. In addition, the two clusters may be beneficial for prognostic stratification and improving immunotherapeutic efficacy.

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N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis

Journal of Translational Medicine ◽

10.1186/s12967-021-02981-5 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Fei Ye ◽

Tianzhu Wang ◽

Xiaoxin Wu ◽

Jie Liang ◽

Jiaoxing Li ◽

...

Keyword(s):

Gene Expression ◽

Multiple Sclerosis ◽

Clustering Analysis ◽

Roc Curves ◽

Progressive Multiple Sclerosis ◽

Rna Modification ◽

Diagnostic Model ◽

Consensus Clustering ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract Background Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

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The effect of m6A RNA methylation regulators on tumor microenvironment in clear cell renal cell carcinoma

10.21203/rs.3.rs-283399/v1 ◽

2021 ◽

Author(s):

Shuaishuai Huang ◽

Xiaodong Qing ◽

Qiuzi Lin ◽

Qiaoling Wu ◽

Xue Wang ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Tumor Microenvironment ◽

Immune Checkpoint ◽

Renal Cell ◽

Clear Cell ◽

Gene Signature ◽

Consensus Clustering ◽

Cell Renal Cell Carcinoma ◽

Rna Methylation ◽

M6a Rna Methylation

Abstract Background: m6A RNA methylation and tumor microenvironment (TME) have been reported to play important roles in the progression and prognosis of clear cell renal cell carcinoma (ccRCC). However, whether m6A RNA methylation regulators affect TME in ccRCC remains unknown. Thus, the current study is designed to comprehensively evaluate the effect of m6A RNA methylation regulators on TME in ccRCC.Methods: Transcriptome data of ccRCC was obtained from The Cancer Genome Atlas (TCGA) database. Consensus clustering analysis was conducted based on the expressions of m6A RNA methylation regulators. Survival differences were evaluated by Kaplan-Meier (K-M) analysis between the clusters. DESeq2 package was used to analyze the differentially expressed genes (DEGs) between the clusters. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were analyzed by ClusterProfiler R package. Immune, stromal and ESTIMATE scores were assessed by ESTIMATE algorithm. CIBERSORT algorithm was applied to evaluate immune infiltration. The expressions of human leukocyte antigen (HLA), immune checkpoint molecules, and Th1/IFNγ gene signature associated with TME were also compared between the clusters. TIDE algorithm and subclass mapping were used to analyze the clinical response of different clusters to PD-1 and CTLA-4 blockade. Results: The expressions of fifteen m6A regulators were significantly different between ccRCC and normal kidney tissues. Based on the expressions of those fifteen m6A regulators, two clusters were identified by consensus clustering, in which cluster 1 had better overall survival (OS). A total of 4,429 DEGs were found between the two clusters, and were enriched into immune-related biological processes. Further analysis of the two clusters’ TME showed that cluster 1 had lower immune and ESTIMATE scores, higher expressions of HLA and lower expressions of immune checkpoint molecules. Besides, immune infiltration and the expressions of Th1/IFNγ gene signature also have significant differences between two clusters. Conclusions: Our study revealed that m6A regulators were important participants in the development of ccRCC, with a close relationship with TME.

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

10.21203/rs.3.rs-113477/v1 ◽

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.

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Significance of N6-Methyladenosine RNA Methylation Regulators in Immune Infiltrates of Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.671179 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jing Gu ◽

Fangfang Bi

Keyword(s):

Ovarian Cancer ◽

Clustering Algorithm ◽

Checkpoint Inhibitors ◽

Advanced Ovarian Cancer ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Consensus Clustering ◽

Differential Analysis ◽

Rna Methylation ◽

M6a Rna Methylation

N6-methyladenosine (m6A) RNA methylation regulators play an important role in the occurrence and development of tumors. Here, we aimed to identify the potential roles of m6A RNA methylation regulators in immune infiltrates of ovarian cancer. We obtained two distinct m6A patterns (m6Acluster.A and m6Acluster.B) based on the expression levels of all 21 m6A RNA methylation regulators from The Cancer Genome Atlas (TCGA) database using a consensus clustering algorithm. Differential analysis of m6Acluster.A and m6Acluster.B identified 196 m6A-related genes. We further validated the m6A regulation mechanism based on the 196 m6A-related genes using another consensus clustering algorithm. Considering individual differences, principal component analysis algorithms were used to calculate an m6A score for each sample in order to quantify the m6A patterns. A low m6A score was associated with immune activation and enhanced response to immune checkpoint inhibitors, whereas a high m6A score was associated with tumor progression. Finally, we successfully verified the correlation between m6A regulators and immune microenvironment in OC using our microarray analysis data. In summary, m6A regulators play non-negligible roles in immune infiltrates of ovarian cancer. Our investigation of m6A patterns may help to guide future immunotherapy strategies for advanced ovarian cancer.

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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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