m6A RNA Methylation Regulators Contribute to Malignant Progression and Have Clinical Prognostic Impact in Gastric Cancer

Su, Yue; Huang, Jinqi; Hu, Jichang

doi:10.3389/fonc.2019.01038

Cited by 79 publications

(92 citation statements)

References 40 publications

(39 reference statements)

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“…Comparable results were also reported in other studies. It is reported that m6A regulator genes were extremely important in several cancer-related pathways, such as p53 signaling pathway [29,53], cell cycle [29,30,54], Ras [30], in ammatory response [29,53], and PPAR signaling pathway [53].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, m6A regulator genes are not only promising to become the potential molecular markers for cancer diagnosis and prognosis, but also quali ed to serve as the targets for the design of targeted anticancer drugs, which has been demonstrated in several literatures [26][27][28]. Several studies have investigated the role of m6A regulator genes in the survival of cancers, such as glioma [29], gastric cancer [30], and head and neck squamous cell carcinoma [31]. However, they did not include the newly discovered m6A regulator genes, seldom constructed a m6A-based nomogram, and never investigated the association with immunity Therefore, in this study, we systematically evaluated the expression of twenty m6A regulator genes in 535 lung ADC and 59 normal samples, from which we screened out seventeen differentially expressed genes for further study.…”

Section: Introductionmentioning

confidence: 99%

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Construction of a m6A RNA Methylation Regulator-related Signature for Prognosis and Immune Response in Lung Adenocarcinoma

Sheng

Xia

et al. 2020

Preprint

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BackgroundN6-methyladenosine (m6A) RNA methylation is related to cancer pathogenesis and development. However, few studies have investigated the role of m6A regulator genes in lung adenocarcinoma (ADC).MethodsGene expressions with clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) database. We compared the expression of twenty m6A regulator genes between tumor and normal samples. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression model were used to derive a multi-gene signature. A signature-based nomogram was developed, and the prediction performance was validated by an exterior validation set (GSE72094). Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network and identify the hub genes. The association of m6A signature with immunity was examined.ResultsSeventeen differentially expressed genes were identified. A five-gene prognostic signature (IGF2BP1, IGF2BP2, HNRNPA2B1, METTL3, and HNRNPC) was determined, and demonstrated as an unfavorable prognostic factor. A signature-based nomogram was developed to predict each patient’s survival probability, and the nomogram was well calibrated and showed a satisfactory discrimination. Turquoise was identified as the most risk-related module, and genes in this module were enriched in the pathway of cell cycle. Six genes were determined as the hub genes. High-risk patients had significantly higher expression of PD-L1, higher tumor mutational burden (TMB), higher proportion of immune checkpoint blockade (ICB) response, and lower proportion of T cells CD8.ConclusionsIn summary, the signature-based nomogram is useful for survival prediction, and high-risk patients were more sensitive to the ICB therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction of a m6A RNA Methylation Regulator-related Signature for Prognosis and Immune Response in Lung Adenocarcinoma

Sheng

Xia

et al. 2020

Preprint

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“…The RNA-sequencing transcriptome data and clinicopathological information of 160 ESCA patients and 11 normal samples were obtained from The Cancer Genome Atlas (https://cancergenome.nih.gov/) database. Data procession was performed as the previous studies [15,16]. GSE13898 dataset was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) for validation [17].…”

Section: Data Collection and Processionmentioning

confidence: 99%

“…According to the recent published literature of m 6 A RNA methylation regulators in human cancer [15,16], 13 m 6 A RNA methylation regulators (METTL3, METTL14, WTAP, KIAA1429, RBM15, ZC3H13, YTHDC1, YTHDC2, YTHDF1, YTHDF2, HNRNPC, FTO, and ALKBH5) with available expression pro les in the TCGA-ESCA dataset were included ( Table 1). The Wilcoxon signed-rank test was applied to screen the differentially expressed m 6 A RNA methylation regulators between ESCA and normal tissues with P < 0.05.…”

Section: Identi Cation Of Differentially Expressed M 6 a Rna Methylatmentioning

confidence: 99%

Identification and validation of a novel prognostic index based on m6A RNA methylation regulators in esophageal carcinoma

Yang¹,

Duan²,

Zhao³

et al. 2020

Preprint

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Background The incidence and mortality rate of esophageal carcinoma (ESCA) remains high. This study proposed to explore the promising prognostic markers based on m6A RNA methylation regulators, and finally to improve the prognostic assessment for ESCA patients. Methods The RNA sequencing and relevant clinical data of ESCA and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. Then, we evaluated the expression pattern of 13 m6A methylation regulators in ESCA and normal samples. Two groups of ESCA were divided by the consensus clustering analysis. STRING database and R package were used to construct the protein-protein interaction network and conduct correlation analysis, respectively. Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop the multiple-gene risk signature. Kaplan-Meier method and receiver operating characteristic (ROC) curves were used to assess the accuracy of the model. The clinical nomogram combining clinicpathological factors and gene signature was built to predict survival rate of ESCA patients. Gene set enrichment analysis (GSEA) and networks prediction analysis were conducted to explore the signaling pathways that related to the risk genes. Results Eight m6A methylation regulators (HNRNPC, YTHDF1, METTL3, YTHDF2, WTAP, YTHDC1, KIAA1429, and RBM15) were significantly upregulated in ESCA. Two clusters of ESCA with obvious differences in tumor stage were identified via the consensus clustering analysis. A two-gene signature, ALKBH5 and HNRNPC, was established for predicting the prognosis of ESCA patients. Kaplan-Meier curves illustrated that the overall survival of patients in low-risk group was obviously longer than that of patients in high-risk group (P = 1.411e-02). Importantly, risk score and tumor stage were identified as the independent prognostic indicators. The testing dataset GSE13898 showed that the nomogram had a good capacity to assess the prognosis of ESCA patients. Cell cycle, mTOR pathway, and p53 signaling pathway were found to be related to the dysregulation of risk genes. Conclusions m6A RNA methylation regulators ALKBH5 and HNRNPC could act as prognostic indicators and therapeutic targets for prognostic analysis and cancer treatment of ESCA.

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“…Given the critical role of epigenetic regulation of DNA and histone (proteins) methylation in the underlying biological processes of mammals, the methyladenosine (mA) chemical modification of RNA may also be used as an novel epigenetic marker of far-reaching biological significance (1,2). N6-methyladenosine (m6A) is the most prevalent internal chemical modification of mRNAs in higher eukaryotes, at a frequency of approximately three sites per mRNA (3)(4)(5).…”

Section: Introductionmentioning

confidence: 99%

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

Du¹,

Hou²,

Mi³

et al. 2020

Front. Oncol.

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N6-methyladenosine (m6A) RNA methylation, the most common form of mRNA modification and regulated by the m6A RNA methylation regulators ("writers," "erasers," and "readers"), has been reported to be associated with the progression of the malignant tumor. However, its role in glioblastoma (GBM) has been poorly known. This study aimed to identify the expression, potential functions, and prognostic values of m6A RNA methylation regulators in GBM. Here, we revealed that the 13 central m6A RNA methylation regulators were firmly related to the clinical and molecular phenotype of GBM. Taking advantage of consensus cluster analysis, we obtained two categories of GBM samples and found malignancy-related processes of m6A methylation regulators and compounds that specifically targeted the malignant processes. Besides, we also obtained a list of genes with poor prognosis in GBM. Finally, we derived a risk-gene signature with three selected m6A RNA methylation regulators, which allowed us to extend the in-depth study and dichotomized the OS of patients with GBM into high-and low-risk subgroups. Notably, this risk-gene signature could be used as independent prognostic markers and accurate clinicopathological parameter predictors. In conclusion, m6A RNA methylation regulators are a type of vital participant in the malignant progression of GBM, with a critical potential in the prognostic stratification and treatment strategies of GBM.

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m6A RNA Methylation Regulators Contribute to Malignant Progression and Have Clinical Prognostic Impact in Gastric Cancer

Cited by 79 publications

References 40 publications

Construction of a m6A RNA Methylation Regulator-related Signature for Prognosis and Immune Response in Lung Adenocarcinoma

Construction of a m6A RNA Methylation Regulator-related Signature for Prognosis and Immune Response in Lung Adenocarcinoma

Identification and validation of a novel prognostic index based on m6A RNA methylation regulators in esophageal carcinoma

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

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