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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
Chengliang yin
PLA
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: N6-methyladenosine (m6A) RNA modification play critical roles in tumorigenesis because it can change gene expression and even the function in multiple levels including the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. In this study, we aim to conduct comprehensive investigation on m6A RNA methylation regulators and m6A-related genes and their association with prognosis in early-stage Lung adenocarcinoma (LUAD).
Methods: The relevant datasets which were used to analyze 21 m6A RNA methylation regulators and 887 m6A-related genes in m6Avar were downloaded from Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate cox regression analysis, random survival forest analysis, Kaplan-Meier anylysis, STRING and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on five feature genes was constructed.
Results: Respectively, we treated GSE31210 (n=226) as training set, GSE50081 (n=128) and TCGA data (n=461) as test set. By performing univariable cox regression and random survival forest algorithm in the training group, five prognosis-related genes including DENND1A, KBTBD6, KIF4A, BMPER, and YTHDC2 were screened out, which could divide LUAD patients into low-risk group and high-risk group (log rank P < 0.001). The predictive efficacy of these genes was confirmed in the test group GSE50081 (log rank P < 0.01) and TCGA datasets (log rank P < 0.001). Cox analysis showed that this five-gene signature was an independent risk factor in LUAD. Further, genes in the signature were also external validated using online database. YTHDC2 played vital role of readers in m6A methylation.
Conclusion: The findings of this study suggested that associated with m6A-related genes and m6A RNA methylation regulators, five-gene signature was reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.
Keywords
Lung adenocarcinoma, m6A RNA methylation regulators, m6A-related genes, prognostic signature
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This is a list of supplementary files associated with this preprint. Click to download.
- SupplementaryFig.S1.pdf
Figure S1 a-e The five predictive genes expression levels in LUAD. Data was from the GEPIA database. T: tumor, N: normal tissue.
- SupplementaryFig.S1.pdf
Figure S1 a-e The five predictive genes expression levels in LUAD. Data was from the GEPIA database. T: tumor, N: normal tissue.
- SupplementaryFig.S1.pdf
Figure S1 a-e The five predictive genes expression levels in LUAD. Data was from the GEPIA database. T: tumor, N: normal tissue.
- SupplementaryFig.S1.pdf
Figure S1 a-e The five predictive genes expression levels in LUAD. Data was from the GEPIA database. T: tumor, N: normal tissue.
- SupplementarytableS1.doc
Table S1 The list of the 21 m6A RNA methylation regulators from publications
- SupplementarytableS1.doc
Table S1 The list of the 21 m6A RNA methylation regulators from publications
- SupplementarytableS1.doc
Table S1 The list of the 21 m6A RNA methylation regulators from publications
- SupplementarytableS1.doc
Table S1 The list of the 21 m6A RNA methylation regulators from publications
- SupplementarytableS2.xls
Table S2 m6A gene sets of Univariate Cox regression analysis in the GSE31210 dataset (P<0.05, AUC>0.6, n = 226)
- SupplementarytableS2.xls
Table S2 m6A gene sets of Univariate Cox regression analysis in the GSE31210 dataset (P<0.05, AUC>0.6, n = 226)
- SupplementarytableS2.xls
Table S2 m6A gene sets of Univariate Cox regression analysis in the GSE31210 dataset (P<0.05, AUC>0.6, n = 226)
- SupplementarytableS2.xls
Table S2 m6A gene sets of Univariate Cox regression analysis in the GSE31210 dataset (P<0.05, AUC>0.6, n = 226)
- SupplementarytableS3.xls
Table S3 The signature composed of m6A regulators in the GSE31210 dataset (n=226)
- SupplementarytableS3.xls
Table S3 The signature composed of m6A regulators in the GSE31210 dataset (n=226)
- SupplementarytableS3.xls
Table S3 The signature composed of m6A regulators in the GSE31210 dataset (n=226)
- SupplementarytableS3.xls
Table S3 The signature composed of m6A regulators in the GSE31210 dataset (n=226)
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Research
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
Chengliang yin
PLA
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: N6-methyladenosine (m6A) RNA modification play critical roles in tumorigenesis because it can change gene expression and even the function in multiple levels including the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. In this study, we aim to conduct comprehensive investigation on m6A RNA methylation regulators and m6A-related genes and their association with prognosis in early-stage Lung adenocarcinoma (LUAD).
Methods: The relevant datasets which were used to analyze 21 m6A RNA methylation regulators and 887 m6A-related genes in m6Avar were downloaded from Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate cox regression analysis, random survival forest analysis, Kaplan-Meier anylysis, STRING and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on five feature genes was constructed.
Results: Respectively, we treated GSE31210 (n=226) as training set, GSE50081 (n=128) and TCGA data (n=461) as test set. By performing univariable cox regression and random survival forest algorithm in the training group, five prognosis-related genes including DENND1A, KBTBD6, KIF4A, BMPER, and YTHDC2 were screened out, which could divide LUAD patients into low-risk group and high-risk group (log rank P < 0.001). The predictive efficacy of these genes was confirmed in the test group GSE50081 (log rank P < 0.01) and TCGA datasets (log rank P < 0.001). Cox analysis showed that this five-gene signature was an independent risk factor in LUAD. Further, genes in the signature were also external validated using online database. YTHDC2 played vital role of readers in m6A methylation.
Conclusion: The findings of this study suggested that associated with m6A-related genes and m6A RNA methylation regulators, five-gene signature was reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target.
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