The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in rectosigmoid cancer
Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc..
N6-methyladenosine (m6A) methylation plays an important role in the occurrence and development of tumors. This study aimed to explore the effects of m6A methylation regulatory genes on rectosigmoid cancer (RSC). RNA-seq data and related clinical information in The Cancer Genome Atlas database were analyzed. The Wilcoxon test was used to analyze the different expression levels of m6A methylation regulatory genes between the tumor and normal samples. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a risk prognosis model between the m6A methylation regulatory genes and RSC. The median risk score was used to classify RSC patients into high and low-risk groups. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to evaluate the sensitivity and specificity of the prediction model. The expression of m6A methylation regulation genes was different between the tumor and normal samples, 6 genes were overexpressed in tumor and 2 genes were down-regulated. Four m6A methylation regulatory genes, YTHDF3, KIAA1429, ALKBH5 and METTL3, were screened by least absolute shrinkage and selection operator Cox regression analysis. The overall survival of high-risk group was significantly lower than that of low-risk group (P = 4.681 × 10-4). The area under the curve value in the receiver operating characteristic curve was 0.935, indicating that the prediction model was effective. Univariate and multivariate Cox regression were used to test the effectiveness of the model. m6A methylation regulators YTHDF3, KIAA1429, ALKBH5, and METTL3 can be used to construct predictive models to predict overall survival in different clinical subgroups of RSC patients.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:101 |
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| Enthalten in: |
Medicine - 101(2022), 51 vom: 23. Dez., Seite e32328 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Wei [VerfasserIn] |
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| Links: |
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| Themen: |
EC 2.1.1.- |
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| Anmerkungen: |
Date Completed 05.01.2023 Date Revised 11.01.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1097/MD.0000000000032328 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 245 | 1 | 4 | |a The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in rectosigmoid cancer |
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| 520 | |a Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. | ||
| 520 | |a N6-methyladenosine (m6A) methylation plays an important role in the occurrence and development of tumors. This study aimed to explore the effects of m6A methylation regulatory genes on rectosigmoid cancer (RSC). RNA-seq data and related clinical information in The Cancer Genome Atlas database were analyzed. The Wilcoxon test was used to analyze the different expression levels of m6A methylation regulatory genes between the tumor and normal samples. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a risk prognosis model between the m6A methylation regulatory genes and RSC. The median risk score was used to classify RSC patients into high and low-risk groups. Kaplan-Meier survival analysis and receiver operating characteristic curves were used to evaluate the sensitivity and specificity of the prediction model. The expression of m6A methylation regulation genes was different between the tumor and normal samples, 6 genes were overexpressed in tumor and 2 genes were down-regulated. Four m6A methylation regulatory genes, YTHDF3, KIAA1429, ALKBH5 and METTL3, were screened by least absolute shrinkage and selection operator Cox regression analysis. The overall survival of high-risk group was significantly lower than that of low-risk group (P = 4.681 × 10-4). The area under the curve value in the receiver operating characteristic curve was 0.935, indicating that the prediction model was effective. Univariate and multivariate Cox regression were used to test the effectiveness of the model. m6A methylation regulators YTHDF3, KIAA1429, ALKBH5, and METTL3 can be used to construct predictive models to predict overall survival in different clinical subgroups of RSC patients | ||
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| 700 | 1 | |a Fan, Daiming |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Liu |e verfasserin |4 aut | |
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