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

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

10.21203/rs.3.rs-120253/v1 ◽

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.

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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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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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Associations between TAB2 gene polymorphisms and dilated cardiomyopathy in a Chinese population

Biomarkers in Medicine ◽

10.2217/bmm-2019-0384 ◽

2020 ◽

Author(s):

Can Shen ◽

Yue Zhong ◽

Xingming Huang ◽

Yanyun Wang ◽

Ying Peng ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Dilated Cardiomyopathy ◽

Gene Polymorphisms ◽

Chinese Population ◽

Cox Regression ◽

Bonferroni Correction ◽

Cox Regression Analysis ◽

Kaplan Meier

Aim: The present study aimed to investigate the role of TAB2 gene polymorphisms in dilated cardiomyopathy (DCM) susceptibility and prognosis in a Chinese population. Materials & methods: A total of 343 DCM patients and 451 controls were enrolled and had their blood genotyped. Survival analysis was evaluated with Kaplan-Meier curves and Cox regression analysis. Results: G carriers (AG/GG) and AG genotype of rs237028 had a higher DCM susceptibility as well as a worse DCM prognosis. Additionally, C carriers (CT/CC) of rs652921 and G carriers (TG/GG) of rs521845 had a higher DCM risk and CC homozygote of rs652921 had a worse DCM prognosis. These associations were still significant after adjustment for the Bonferroni correction. Conclusion: TAB2 gene polymorphisms were associated with DCM susceptibility and prognosis in the Chinese population.

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Microtubule-Associated Protein 4 Is a Prognostic Factor and Promotes Tumor Progression in Lung Adenocarcinoma

Disease Markers ◽

10.1155/2018/8956072 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Xiaochun Xia ◽

Chao He ◽

Anqing Wu ◽

Jundong Zhou ◽

Jinchang Wu

Keyword(s):

Regression Analysis ◽

Prognostic Factor ◽

Lung Adenocarcinoma ◽

Cancer Progression ◽

Cox Regression ◽

Prognostic Significance ◽

Microtubule Assembly ◽

Migration And Invasion ◽

Cox Regression Analysis ◽

Kaplan Meier

Microtubule-associated protein 4 (MAP4) plays an important role in microtubule assembly and stabilization. The purpose of this study was to investigate the level of expression of MAP4 in lung adenocarcinoma (LADC) samples and to evaluate its prognostic value and the influence on cancer progression in LADC patients. The expression of MAP4 protein was analyzed using immunohistochemistry. The clinical significance and the prognostic significance of MAP4 expression were assessed by Kaplan-Meier analysis and Cox regression analysis. The roles of MAP4 in the migration and invasion of LADC cells were detected by wound-healing assays and transwell assays, respectively. We found the expression levels of MAP4 protein in LADC tissues to be significantly higher than those in noncancerous tissues. MAP4 expression was significantly correlated with differentiation, pathological T stage, and TNM stage. Kaplan-Meier survival analysis indicated that patients with high MAP4 expression had significantly poorer overall survival (OS). Cox regression analysis revealed that MAP4 expression level was an independent prognostic factor for OS. Functionally, in vitro studies showed that MAP4 knockdown efficiently suppressed the migration and invasion of LADC cells. Our data indicated that MAP4 protein may represent a novel prognostic biomarker and a potential therapeutic target for LADC.

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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 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

Frontiers in Genetics ◽

10.3389/fgene.2021.656114 ◽

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.

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Development and Validation of a Robust Ferroptosis-Related Prognostic Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-230598/v1 ◽

2021 ◽

Author(s):

Yen-ting Lin ◽

Can-Xuan Li ◽

Jie Chen

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Roc Curve ◽

Prognostic Value ◽

Cox Regression ◽

Expression Patterns ◽

The Cancer Genome Atlas ◽

Entire Group ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Background: Ferroptosis is a novel defined type of programmed cell death (PCD) with widespread functions involved in physical conditions or multiple diseases including malignancies. However, the relationship between ccRCC and ferroptosis-related regulators remains poorly known. Herein, we investigate the prognostic values and potential mechanisms of ferroptosis-related genes (FRGs) in ccRCC.Methods: Ferroptosis-related genes were obtained from FerrDb database, GeneCards database and previously published literatures. The gene expression profile of ferroptosis-related regulators and corresponding clinicopathological information were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed ferroptosis-related genes (DE-FRGs) were screened between ccRCC specimens and noncancerous specimens. Among these genes, prognostic DE-FRGs were identified using univariate COX analysis and LASSO regression analysis. Further multivariate COX regression was employed to identify prognosis-related hub DE-FRGs and establish a prognostic model. Results: We identified seven hub genes (HMGCR, MT1G, BID, EIF4A1, FOXM1, TFAP2C and CHAC1) from the DE-FRGs using univariate Cox regression analysis, LASSO and multivariate Cox regression analysis, and used them to establish a novel clinical predictive model in the TCGA train cohort (n = 374). Subsequently, we assessed the prognostic value of the model. Survival analysis showed that high-risk patients had a reduced overall survival (OS), the time-dependent receiver operating characteristic (ROC) curve analysis confirmed the signature's diagnostic performance. Additionally, multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor. Additionally, we verified the prognostic performance of the risk model in the testing cohort (n=156), and the entire group (n=530) using Kaplan-Meier curve and ROC curve analyses. Functional analysis indicated that several carcinogenic pathways were enriched, and tumor-infiltrating immune cell abundances, and the expression levels of immunosuppressive molecules were different between two risk groups. Finally, external databases (ONCMINE, GEPIA, HPA, Kaplan-Meier plotter and cbioportal) were used to confirm the expression patterns, prognostic value, and genetic mutations of 7 hub FRGs in ccRCC.Conclusions: Collectively, we successfully constructed a novel ferroptosis-related risk signature that was significantly associated with the prognosis of ccRCC.

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Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

Cancer Cell International ◽

10.1186/s12935-021-02385-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengxin Wu ◽

Jinshui Tan ◽

Yifan Zhuang ◽

Mengya Zhong ◽

Yubo Xiong ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Survival Analysis ◽

Risk Model ◽

Cox Regression ◽

Clinical Samples ◽

Pyrimidine Metabolism ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

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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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