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Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer

BACKGROUND: Endometrial cancer (EC) is a multifactorial disease, and its progression may be driven by abnormal genetic methylation. To clarify the underlying molecular mechanisms and sensitive biomarkers for EC, this study used an integrated bioinformatic analysis to explore the methylation-driven g...

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Autores principales: Lu, Yiling, Tang, Weichun, Wang, Xiaoyu, Kang, Xinyi, You, Jun, Chen, Liping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725853/
https://www.ncbi.nlm.nih.gov/pubmed/35002309
http://dx.doi.org/10.2147/IJGM.S341771
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author Lu, Yiling
Tang, Weichun
Wang, Xiaoyu
Kang, Xinyi
You, Jun
Chen, Liping
author_facet Lu, Yiling
Tang, Weichun
Wang, Xiaoyu
Kang, Xinyi
You, Jun
Chen, Liping
author_sort Lu, Yiling
collection PubMed
description BACKGROUND: Endometrial cancer (EC) is a multifactorial disease, and its progression may be driven by abnormal genetic methylation. To clarify the underlying molecular mechanisms and sensitive biomarkers for EC, this study used an integrated bioinformatic analysis to explore the methylation-driven genes of EC. METHODS: The mRNA expression data, methylation data and corresponding clinical information of EC samples were downloaded from The Cancer Genome Atlas (TCGA) database. MethylMix algorithm was used to screen out methylation-driven genes in EC. Functional and pathway enrichment analysis and the protein–protein interaction (PPI) analysis were conducted to demonstrate the functions and interactions between these genes. Then, prognosis-related methylated genes were screened out by using univariate and multivariate Cox analyses, and a prognostic risk assessment model for EC was constructed. The methylation sites and expression profiles of candidate genes were further investigated. RESULTS: A total of 127 methylated genes were identified in EC. Four genes (RP11-968O1.5, DCAF12L1, MSX1 and ALS2CR11) were selected as candidate genes to construct a reliable prognostic risk model. The univariate and multivariate Cox proportional hazards regression analyses showed that the risk score based on four genes was an independent prognostic indicator for OS among EC patients. A nomogram was established and the calibration plot analysis indicated the good performance and clinical utility of the nomogram. In addition, the methylation and expression of MSX1 and DCAF12L1 were significantly associated with EC survival rate. The joint ROC analysis revealed that the AUC of DCAF12L1-MSX1 was 0.867, which suggested both have a good EC-diagnosing efficiency. We then coped DCAF12L1 and MSX1 with GESA analysis, finding both were mainly associated with the KRAS signaling pathway. CONCLUSION: This bioinformatic study combs the methylated genes involved in EC development for the first time, finding that MSX1 and DCAF12L1 could serve as EC prognostic markers and drug targets.
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spelling pubmed-87258532022-01-06 Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer Lu, Yiling Tang, Weichun Wang, Xiaoyu Kang, Xinyi You, Jun Chen, Liping Int J Gen Med Original Research BACKGROUND: Endometrial cancer (EC) is a multifactorial disease, and its progression may be driven by abnormal genetic methylation. To clarify the underlying molecular mechanisms and sensitive biomarkers for EC, this study used an integrated bioinformatic analysis to explore the methylation-driven genes of EC. METHODS: The mRNA expression data, methylation data and corresponding clinical information of EC samples were downloaded from The Cancer Genome Atlas (TCGA) database. MethylMix algorithm was used to screen out methylation-driven genes in EC. Functional and pathway enrichment analysis and the protein–protein interaction (PPI) analysis were conducted to demonstrate the functions and interactions between these genes. Then, prognosis-related methylated genes were screened out by using univariate and multivariate Cox analyses, and a prognostic risk assessment model for EC was constructed. The methylation sites and expression profiles of candidate genes were further investigated. RESULTS: A total of 127 methylated genes were identified in EC. Four genes (RP11-968O1.5, DCAF12L1, MSX1 and ALS2CR11) were selected as candidate genes to construct a reliable prognostic risk model. The univariate and multivariate Cox proportional hazards regression analyses showed that the risk score based on four genes was an independent prognostic indicator for OS among EC patients. A nomogram was established and the calibration plot analysis indicated the good performance and clinical utility of the nomogram. In addition, the methylation and expression of MSX1 and DCAF12L1 were significantly associated with EC survival rate. The joint ROC analysis revealed that the AUC of DCAF12L1-MSX1 was 0.867, which suggested both have a good EC-diagnosing efficiency. We then coped DCAF12L1 and MSX1 with GESA analysis, finding both were mainly associated with the KRAS signaling pathway. CONCLUSION: This bioinformatic study combs the methylated genes involved in EC development for the first time, finding that MSX1 and DCAF12L1 could serve as EC prognostic markers and drug targets. Dove 2021-12-31 /pmc/articles/PMC8725853/ /pubmed/35002309 http://dx.doi.org/10.2147/IJGM.S341771 Text en © 2021 Lu et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Lu, Yiling
Tang, Weichun
Wang, Xiaoyu
Kang, Xinyi
You, Jun
Chen, Liping
Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title_full Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title_fullStr Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title_full_unstemmed Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title_short Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer
title_sort development of potential prognostic biomarkers based on dna methylation-driven genes for patients with endometrial cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725853/
https://www.ncbi.nlm.nih.gov/pubmed/35002309
http://dx.doi.org/10.2147/IJGM.S341771
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