RNA m6A methylation regulators in ovarian cancer

Guo, Jialu; Zheng, Jianfeng; Zhang, Huizhi; Tong, Jinyi

doi:10.1186/s12935-021-02318-8

Cited by 34 publications

(28 citation statements)

References 174 publications

(205 reference statements)

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“…There are more than 150 types of RNA modification methods reported, among which m1A, m6A, m5C, etc., are the most common and play an important role in the occurrence and development of tumors. Guo et al has found that YTHDF1 regulates the translation of eIF3C in an m6A-dependent manner, enhances protein synthesis, and promotes tumorigenesis of ovarian cancer cells [ 31 ]. The study by Whongsiri et al [ 32 ] found that m5C was decreased in BLCA tissues, but the expression of 8-OHdG, H3K9me3, and HP1 α increased, indicating that the bladder tissues of BLCA patients have overall DNA hypomethylation, increased oxidative stress, and inhibitory chromatin changes.…”

Section: Discussionmentioning

confidence: 99%

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

Wang

Chen

et al. 2022

Disease Markers

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The role of 5-methylcytosine-related long noncoding RNAs (m5C-lncRNAs) in bladder cancer (BLCA) remains unclear. Here, we aim to study the prognostic value, gene expression characteristics, and correlation between the m5C-lncRNA risk model and the tumor microenvironment, immune infiltration, and tumor mutations in BLCA. After collecting BLCA patient RNA sequence transcriptome data, clinical information and mutation data from the Cancer Genome Atlas (TCGA) database, 17 m5C-related lncRNAs independently correlated with OS were obtained by Lasso and multivariate Cox regression analysis, and a risk model was constructed. Univariate Cox, multivariate Cox regression analysis, and the C-index curve proved that the risk model was a significant independent prognostic indicator for patients with BLCA. ESTIMATE and CIBERSORT indicated that the higher the number of immune cells and stromal cells in TME, the higher the prognostic risk. We found that in the low-risk group, the expression levels of immune cells that predicted a good prognosis were higher, including plasma cells, regulatory T cells, and CD 8 T cells. There is a negative correlation between TMB and risk score. The TMB of the low-risk group is significantly higher than that of the high-risk group. In conclusion, the m5C-related risk model is crucial to predict the prognosis of patients with BLCA.

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Section: Discussionmentioning

confidence: 99%

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

Wang

Chen

et al. 2022

Disease Markers

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“…Conversely, erasers remove m6A from the mRNA to cause the modification, preventing readers from recognizing and playing its molecular biological roles, thereby up-regulating or down-regulating the expression of oncogene and tumor suppressor gene[ 24 ]. Some m6A regulators are abnormally expressed in OC, such as YTHDF1, YTHDF2, METTL3, ALKBH5 etc., and promote or disrupt the development or maintenance of tumour phenotypes [ 25 ]. As the most common mRNA and lncRNA modification, m6A methylation plays different roles in a variety of tumors, including occurrence, proliferation, invasion, and metastasis [ 3 , 24 , 26 ].…”

Section: Discussionmentioning

confidence: 99%

N6-methyladenosine-related lncRNAs is a potential marker for predicting prognosis and immunotherapy in ovarian cancer

Nie

Tan

2022

Hereditas

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Background With a lack of specific symptoms, ovarian cancer (OV) is often diagnosed at an advanced stage. This coupled with inadequate prognostic indicators and treatments with limited therapeutic effect make OV the deadliest type of gynecological tumor. Recent research indicates that N6-methyladenosine (m6A) and long-chain non-coding RNA (lncRNA) play important roles in the prognosis of OV and the efficacy of immunotherapy. Results Using the Cancer Genome Atlas (TCGA) OV-related data set and the expression profiles of 21 m6A-related genes, we identified two m6A subtypes, and the differentially expressed genes between the two. Based on the differentially expressed lncRNAs in the two m6A subtypes and the lncRNAs co-expressed with the 21 m6A-related genes, single-factor cox and LASSO regression were used to further isolate the 13 major lncRNAs. Finally, multi-factor cox regression was used to construct a m6A-related lncRNA risk score model for OV, with good performance in patient prognosis. Using risk score, OV tumor samples are divided into with high- and low-score groups. We explored the differences in clinical characteristics, tumor mutational burden, and tumor immune cell infiltration between the two groups, and evaluated the risk score’s ability to predict the benefit of immunotherapy. Conclusion Our m6A-based lncRNA risk model could be used to predict the prognosis and immunotherapy response of future OV patients.

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“…In addition, m6A modification is also found in most ncRNAs, including miRNA, lncRNA, and circRNA, which participated in multiple roles in chromatin remodeling, transcription, posttranscriptional modifications, and signal transduction (Yang et al, 2020). M6A is dynamically and reversibly regulated by an m6A regulator, including methyltransferases (writers), demethylases (erasers), and binding proteins (readers) to add, remove, or recognize m6A-modified sites, respectively, thereby altering important biological processes accordingly (Guo et al, 2021). The m6A methyltransferases (writers) mediate the process of methylation modification of RNA, which mainly includes methyltransferaselike 3 (METTL3), methyltransferase-like 14 (METTL14), and Wilms' tumor 1-associating protein (WTAP) (Meyer and Jaffrey, 2017).…”

Section: Introductionmentioning

confidence: 99%

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma

2022

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Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers of LGG have been established, few are used in clinical practice. M6A methylation significantly affects the biological behavior of LGG tumors. Therefore, establishment of an LGG prognostic model based on m6A methylation regulatory genes is of great interest.Methods: Data from 495 patients from The Cancer Genome Atlas (TCGA) and 172 patients from the Chinese Glioma Genome Atlas (CGGA) were analyzed. Univariate Cox analysis was used to identify methylation regulatory genes with prognostic significance. LASSO Cox regression was used to identify prognostic genes. Receiver operating characteristic (ROC) and Kaplan–Meier curves were used to verify the accuracy of the model. Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify cellular pathways that were significantly associated with the prognosis of LGG.Results: A glioma prognostic model based on five methylation regulatory genes was established. Compared with low-risk patients, patients identified as high risk had a poorer prognosis. There was a high degree of consistency between the internal training and internal validation CGGA cohorts and the external validation TCGA cohort. Furthermore, KEGG and GSEA analyses showed that the focal adhesion and cell cycle pathways were significantly upregulated in high-risk patients. This signature could be used to distinguish among patients with different immune checkpoint gene expression levels, which may inform immune checkpoint inhibitor (ICI) immunotherapy.Conclusion: We comprehensively evaluated m6A methylation regulatory genes in LGG and constructed a prognostic model based on m6A methylation, which may improve prognostic prediction for patients with LGG.

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RNA m6A methylation regulators in ovarian cancer

Cited by 34 publications

References 174 publications

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

N6-methyladenosine-related lncRNAs is a potential marker for predicting prognosis and immunotherapy in ovarian cancer

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma

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