scholarly journals N6-Methyladenosine RNA Methylation Regulators Contribute to Malignant Progression and Survival Prediction in Chronic Lymphocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1738-1738 ◽  
Author(s):  
Ya Zhang ◽  
Xiaosheng Fang ◽  
Na Chen ◽  
Xiao Lv ◽  
Xueling Ge ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Lymphocytic Leukemia ◽  
Functional Enrichment ◽  
Biological Mechanism ◽  
Regression Analyses ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
M6a Rna Methylation

Introduction N6-methyladenosine (m6A) RNA methylation is the most abundant epitranscriptomic modification, dynamically installed by the m6A methyltransferases (termed as "writers"), reverted by the demethylases (termed as "erasers"), and recognized by m6A binding proteins (termed as "readers"). Emerging evidence suggests that m6A RNA methylation regulates RNA stability, and participates in the pathogenesis of multiple diseases including cancers. Nevertheless, the role of m6A RNA methylation in chronic lymphocytic leukemia (CLL) remains to be unveiled. Herein, we hypothesized that m6A RNA methylation contributed to the tumorigenesis and maintenance of CLL. Moreover, the risk-prediction model integrated with the m6A regulators could serve as a novel and effective prognostic indicator in CLL. This study aimed to identify robust m6A RNA methylation-associated fingerprints for risk stratification in patients with CLL. Methods A total of 714 de novo CLL patients from 4 cohorts (China, Spain, Germany and Italy) were enrolled with informed consents. EpiQuik m6A RNA methylation colorimetric quantification assay was utilized to assess m6A RNA methylation levels. LASSO Cox regression algorithm was performed to calculate m6A RNA methylation-associated risk score (short for "m6A risk score") in R software. Besides, Kaplan-Meier survival analysis with log-rank test, univariate and multivariate Cox regression analyses and ROC curve analysis of overall survival (OS) were conduct to explore the prognostic value of m6A signature in CLL. Furthermore, RNA-seq, MeRIP-seq, Ribo-seq, functional enrichment analyses in silico and preclinical experiments ex vivo were applied to confirm the biological mechanism of the m6A regulators in CLL. Results In the present study, we performed a comprehensive analysis to dissect the role of m6A RNA methylation regulators in CLL. Compared with normal B cells from healthy donors, obvious decreased level of m6A RNA methylation was observed in primary CLL cells (p<0.01; Figure 1A). In addition, down-regulated m6A RNA methylation was also detected in CLL cell lines MEC1 and EHEB (p<0.05; Figure 1A). Then, we further investigated the association of the m6A RNA methylation regulators with clinical outcomes of CLL patients. By LASSO Cox regression analysis in 486 CLL patients, the m6A risk score was established with the coefficients of fourteen m6A regulators at the minimum lambda value of 0.00892 (Figure 1B-C). Based on the median risk score as the cut-off value, a clear distribution pattern was delineated in CLL patients (Figure 1D). Kaplan-Meier curves showed stratified high-risk patients presented significantly shorter OS versus the low-risk group (HR=4.477, p<0.001; Figure 2A). Besides, m6A risk score also predicts inferior prognosis in stable subgroup (HR=3.097, p=0.037; Figure 2B), and progressed/ relapsed subgroup (HR=3.325, p=0.001; Figure 2C). Moreover, univariate, multivariate cox regression analyses and ROC curve confirmed high m6A risk score as an independent survival predictor in CLL patients (p<0.001; Figure 2D-E). Thereafter, the clinicopathological relevance and underlying mechanism of m6A risk score were explored. Significant elevated m6A risk score was detected in patients with unfavorable treatment responses compared with stable status (p<0.001; Figure 3A). Furthermore, CLL patients with advanced Binet stage, positive ZAP-70 and unmutated IGHV present increased m6A risk score (p<0.05; Figure 3B-C). Intriguingly, we also observed the significantly negative correlation between highrisk score and 13q14 deletion, in accordance with patients' inferior outcome (p=0.047; Figure 3D). Moreover, Pearson correlation analysis, STRING interactive network and functional enrichment analyses deciphered that the m6A regulators exerted crucial roles in CLL progression potentially via modulating RNA metabolism and oncogenic pathways (Figure 4A-C). Conclusion To date, our study provides evidence for the first time that reduced m6A RNA methylation contributes to the tumorigenesis of CLL. Distinct m6A risk scoreis demonstrated as an efficient tool facilitating prognosis evaluation in CLL patients. However, validation of the signature in more independent cohorts are warranted. Further interrogations will be elucidated on the biological mechanism of m6A regulators, highlighting insights into pathogenesis and therapy strategy of CLL. Disclosures No relevant conflicts of interest to declare.

scholarly journals The role of m6A RNA methylation-related lncRNAs in the prognosis and tumor immune microenvironment of papillary thyroid carcinoma

2021 ◽  
Author(s):  
WenLong Wang ◽  
Cong Shen ◽  
Yunzhe Zhao ◽  
Botao Sun ◽  
Xiangyuan Qiu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Consensus Clustering ◽  
Papillary Thyroid ◽  
Immune Microenvironment ◽  
Rna Methylation ◽  
Tumor Immune Microenvironment ◽  
M6a Rna Methylation

Abstract Background: Emerging evidence has indicated that N6-methylandenosine (m6A) RNA methylation plays a critical role in cancer development. However, the function of m6A RNA methylation-related long noncoding RNAs (m6A-lncRNAs) in papillary thyroid carcinoma (PTC) has never been reported. This study aimed to investigate the role of m6A-lncRNAs in the prognosis and tumor immune microenvironment of PTC.Methods: The gene expression data of lncRNAs and 20 m6A methylation regulators with corresponding clinicopathological information download from the Cancer Genome Atlas database. Based on consensus clustering analysis, LASSO Cox regression, nivariate and multivariate Cox regression analysis were used to determine the role of m6A-lncRNA in the prognosis and tumor immune microenvironment of PTC.Results: Three subgroups (clusters 1, 2, and 3) were identified by consensus clustering of 19 prognosis-related m6A-lncRNA regulators，of which cluster 1 preferentially related with unfavorable prognosis, lower immune scores, and distinct immune infiltrate level. A risk-score model was established based on 8 prognosis-related m6A-lncRNAs. Patients with a high-risk score had a worse prognosis and the ROC indicated a reliable prediction performance for patients with PTC (AUC=0.802). As expected, the immune scores, infiltration levels of immune cells and ESTIMATE scores in the low-risk subgroups were notably higher (p < 0.001) compared with those of high-risk subgroups. Furthermore, GSEA analysis showed that tumor associated pathways, hallmarks, and biological processes were remarkably enriched in the high-risk subgroup. Further analysis indicated that the risk score and age were independent prognostic factors for PTC. An integrated nomogram was constructed that accurately predicted the survival status (AUC = 0.963). Moreover, a lncRNA–miRNA–mRNA regulated network was established based on seven prognosis-related m6A-lncRNAs. Additional, 30 clinical samples and different PTC cells were validated. Conclusions: This is the first study to reveal that m6A-lncRNAs play a vital role in the prognosis and TME of PTC. To a certain degree, m6A-lncRNAs can be considered as new, promising prognostic biomarkers and treatment targets.

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

2020 ◽  
Author(s):  
Rui Wang ◽  
Zian Feng ◽  
Jie Hu ◽  
Xiaodong He ◽  
Zuojun Shen
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
Prediction Efficiency

Abstract Background: 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. However, data on the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) are still lacking. This paper mainly discusses the role of m6A RNA methylation regulators in LUAD, to identify novel prognostic biomarkers.Methods: The gene expression data of 19 m6A methylation regulator in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm were performed to construct a risk signature and evaluated its prognostic prediction efficiency by using the receiver operating characteristic (ROC) curve. The risk score of each patient was calculated according to the risk signature, and LUAD patients were divided into high-risk group and low-risk group. Kaplan-Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of risk signature. Finally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the differential signaling pathways and cellular processes between the two groups.Results: The expression of 15 m6A RNA methylation regulators in LUAD tissues was significantly different than that in normal tissues. YTHDF3, YTHDF2, KIAA1429, HNRNPA2B1, RBM15, METTL3, HNRNPC, YTHDF1, IGF2BP2, IGF2BP3, IGF2BP1 were significantly up-regulated in LUAD, and the expressions of FTO, ZC3H13, WTAP, and METL14 were significantly down-regulated. We selected IGF2BP1, HNRNPC, and HNRNPA2B1 to construct the risk signature. ROC curve indicated the area under the curve (AUC) was 0.659, which means the risk signature had a good prediction efficiency. The results of Kaplan-Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD.Conclusions: The m6A RNA methylation regulators IGF2BP1, HNRNPC, and HNRNPA2B1 have a significant correlation with the clinicopathological characteristics of LUAD, which may be a promising prognostic feature and clinical treatment target.

scholarly journals Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Min Wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network. Results In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

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 m6A RNA Methylation Regulators Elicit Malignant Progression and Predict Clinical Outcome in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenli Li ◽  
Jun Liu ◽  
Zhanzhong Ma ◽  
Xiaofeng Zhai ◽  
Binbin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cell ◽  
Clinical Outcome ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Rna Methylation ◽  
M6a Rna Methylation

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and N6-methyladenosine (m6A) is a predominant internal modification of RNA in various cancers. We obtained the expression profiles of m6A-related genes for HCC patients from the International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Most of the m6A RNA methylation regulators were confirmed to be differentially expressed among groups stratified by clinical characteristics and tissues. The clinical factors (including stage, grade, and gender) were correlated with the two subgroups (cluster 1/2). We identified an m6A RNA methylation regulator-based signature (including METTL3, YTHDC2, and YTHDF2) that could effectively stratify a high-risk subset of these patients by univariate and LASSO Cox regression, and receiver operating characteristic (ROC) analysis indicated that the signature had a powerful predictive ability. Immune cell analysis revealed that the genes in the signature were correlated with B cell, CD4 T cell, CD8 T cell, dendritic cell, macrophage, and neutrophil. Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to HCC. Moreover, the nomogram was established based on the signature integrated with clinicopathological features. The calibration curve and the area under ROC also demonstrated the good performance of the nomogram in predicting 3- and 5-year OS in the ICGC and TCGA cohorts. In summary, we demonstrated the vital role of m6A RNA methylation regulators in the initial presentation and progression of HCC and constructed a nomogram which would predict the clinical outcome and provide a basis for individualized therapy.

scholarly journals Discovery of a Novel Five Immune-Associated Fingerprints for Classification and Survival Prediction in Chronic Lymphocytic Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4403-4403
Author(s):  
Ya Zhang ◽  
Ying Li ◽  
Xiangxiang Zhou ◽  
Xin Wang
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Lymphocytic Leukemia ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Training Set ◽  
Immune Related Gene ◽  
Gene Sets ◽  
Validation Series

Abstract Introduction Current staging methods do not accurately predict clinical outcome of patients with chronic lymphocytic leukemia (CLL) especially in the new era of immunotherapy. Recent studies suggested that immune-related gene signature predicted survival in glioblastoma, breast cancer and colorectal cancer. However, none related studies have been elucidated in CLL. Here, we hypothesized that risk-prediction model integrated with the immune signature could serve as an effective prognostic indicator in CLL. This study aimed to identify reliable and distinct immune-associated fingerprints for robust classification and survival prediction in patients with CLL. Methods A total of 720 de novo CLL patients from multiple cohorts were enrolled with informed consents in the present study. LASSO Cox regression model was utilized to calculate immune-associated risk score (I-score) in R software. Principal Component Analysis (PCA) were performed to present the distribution of risk score. Moreover, the prognostic capability of the five immune-related fingerprints was demonstrated by PCR and ROC curve analysis with leave-one-out cross validation in the training set. Functional enrichment analyses of GO and KEGG in gene expression datasets were performed. Association between I-score and hallmark gene sets from the Molecular Signatures Database (MSigDB) were analyzed using GSEA software. Furthermore, preclinical experiments were conduct to examine the pathological mechanism of constitutive genes of I-score in CLL cell lines (MEC1, EHEB) and primary cells. Additionally, genomic regulatory network was displayed in Cytoscape software. Results In the present study, we performed a comprehensive analysis to dissect the immune-associated fingerprints in CLL. A total of 305 clinical annotated CLL patients and 56 healthy donors with gene expression data were obtained from three independent cohorts. Two gene sets (immune system process, M13664 and immune response, M19817) were extracted from the MSigDB and combined to integrate the immune-related gene set containing 322 genes. Illustrated in the volcano plots, differentiated expressed genes of CLL cells comparing with normal B cells were calculated by limma test (|Log2Fold Change|>1, p<0.01; Figure 1A-C). Venn diagram was delineated to generate the specific CLL immune-associated gene expression panel, with 8 genes were identified (Figure 1D). PCA showed a different distribution pattern, confirming the enhanced immune phenotype in CLL. Then, we further investigate the association of the immuno-signature with clinical outcomes of CLL patients. By Lasso Cox regression analysis, the prognostic immune-related fingerprints were identified in the training set (Figure 2A). The risk score method was established: I-score = (-0.538)*CD3D expression+(-0.077)*CD83 expression+0.364*LAX1 expression+0.191*IL2RA expression+0.362*AIM2 expression, consisting of protective genes (CD3D, CD83) and risky genes (LAX1, IL2RA and AIM2; Figure 2B). Based on the median level of I-score as cut-off value, stratified high-risk patients were observed with significantly shorter overall survival compared with the low-risk group (Hazard Ratio, HR=5.493, p<0.001; Figure 3A). Univariate and multivariate cox regression analyses confirmed high I-score as an independent prognosis biomarker in CLL patients. To reduce disparity of diverse populations, we evaluated I-score in two independent centers of US and Germany with the same formula. The prognostic value of the immune fingerprints were corroborated in the internal validation series (HR=2.200, p=0.023; Figure 3B) and validation series (HR=1.769, p=0.004; Figure 3C). Intriguingly, we also observed the significant correlation between high I-score and 17p13 deletion (p=0.0316; Figure 3D), in accordance with patients' inferior outcome. Moreover, functional enrichment analyses of differentiated expressed genes stratified by I-score indicated that immune related BCR signaling pathway contributed to pathogenesis of CLL (Figure 4). Conclusion To date, our study provides evidence for the first time that distinct immuno-related fingerprints predict survival in CLL. I-score is demonstrated as an efficient classification tool and robust method for prognosis evaluation, greatly facilitating risk stratification and individualized management of CLL patients. Disclosures No relevant conflicts of interest to declare.

scholarly journals Identification of a Two-m6A RNA Methylation Regulator Risk Signature as an Independent Prognostic Biomarker in Papillary Renal Cell Carcinoma by Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Feilong Yang ◽  
Guojiang Zhao ◽  
Liyuan Ge ◽  
Yimeng Song ◽  
Kai Hong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Papillary Renal Cell Carcinoma ◽  
Bioinformatic Analysis ◽  
Clinicopathological Features ◽  
Rna Methylation ◽  
M6a Rna Methylation ◽  
Score Model

N6-Methyladenosine (m6A), the most common form of mRNA modification, is dynamically regulated by the m6A RNA methylation regulators, which play an important role in regulating the gene expression and phenotype in both health and disease. However, the role of m6A in papillary renal cell carcinoma (pRCC) is unknown. The purpose of this work is to investigate the prognostic value of m6A RNA methylation regulators in pRCC; thus, we can build a risk score model based on m6A RNA methylation regulators as a risk signature for predicting the prognosis of pRCC. Here, we investigated the expression and corresponding clinical data by bioinformatic analysis based on 289 pRCC tissues and 32 normal kidney tissues obtained from TCGA database. As a result, we identified the landscape of m6A RNA methylation regulators in pRCC. We grouped all pRCC patients into two clusters by consensus clustering to m6A RNA methylation regulators, but we found that the clusters were not correlated to the prognosis and clinicopathological features of pRCC. Therefore, we additionally built a two-m6A RNA methylation regulator risk score model as a risk signature by the univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. The risk signature was constructed as follows: 0.031 HNRNPC + 0.199 KIAA 1429 . It revealed that the risk score was associated with the clinicopathological features such as pT status and pN status of pRCC. More importantly, the risk score was an independent prognostic marker for pRCC patients. Thus, m6A RNA methylation regulators contributed to the malignant progression of pRCC influencing its prognosis.

scholarly journals Development and Validation of an Rna Binding Protein-associated Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Min wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

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.

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