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Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients
BACKGROUND: Myelodysplastic syndrome (MDS) is a group of hematological malignancies that may progress to acute myeloid leukemia (AML). Bioinformatics-based analysis of high-frequency mutation genes in MDS-related patients is still relatively rare, so we conducted our research to explore whether high...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573449/ https://www.ncbi.nlm.nih.gov/pubmed/34805353 http://dx.doi.org/10.21037/atm-21-4094 |
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author | Wu, Kun Nie, Bo Li, Liyin Yang, Xin Yang, Jinrong He, Zhenxin Li, Yanhong Cheng, Shenju Shi, Mingxia Zeng, Yun |
author_facet | Wu, Kun Nie, Bo Li, Liyin Yang, Xin Yang, Jinrong He, Zhenxin Li, Yanhong Cheng, Shenju Shi, Mingxia Zeng, Yun |
author_sort | Wu, Kun |
collection | PubMed |
description | BACKGROUND: Myelodysplastic syndrome (MDS) is a group of hematological malignancies that may progress to acute myeloid leukemia (AML). Bioinformatics-based analysis of high-frequency mutation genes in MDS-related patients is still relatively rare, so we conducted our research to explore whether high-frequency mutation genes in MDS-related patients can play a reference role in clinical guidance and prognosis. METHODS: Next generation sequencing (NGS) technology was used to detect 32 mutations in 64 MDS-related patients. We classified the patients’ genes and analyzed them by Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and then calculated the gene survival curve of high-frequency mutations. RESULTS: We discovered 32 mutant genes such as ASXL1, DNMT3A, KRAS, NRAS, TP53, SF3B1, and SRSF2. The overall survival (OS) of these genes decreased significantly after DNMT3A, ASXL1, RUNX1, and U2AF1 occurred mutation. These genes play a significant role in biological processes, not only in MDS but also in the occurrence and development of other diseases. Through retrospective analysis, genes associated with MDS-related diseases were identified, and their effects on the disease were predicted. CONCLUSIONS: Thirty-two mutant genes were determined in MDS and when mutations occur in DNMT3A, ASXL1, RUNX1, and U2AF1, their survival time decreases significantly. This results providing a theoretical basis for clinical and scientific research and broadening the scope of research on MDS. |
format | Online Article Text |
id | pubmed-8573449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-85734492021-11-18 Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients Wu, Kun Nie, Bo Li, Liyin Yang, Xin Yang, Jinrong He, Zhenxin Li, Yanhong Cheng, Shenju Shi, Mingxia Zeng, Yun Ann Transl Med Original Article BACKGROUND: Myelodysplastic syndrome (MDS) is a group of hematological malignancies that may progress to acute myeloid leukemia (AML). Bioinformatics-based analysis of high-frequency mutation genes in MDS-related patients is still relatively rare, so we conducted our research to explore whether high-frequency mutation genes in MDS-related patients can play a reference role in clinical guidance and prognosis. METHODS: Next generation sequencing (NGS) technology was used to detect 32 mutations in 64 MDS-related patients. We classified the patients’ genes and analyzed them by Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, protein-protein interaction (PPI) analysis, and then calculated the gene survival curve of high-frequency mutations. RESULTS: We discovered 32 mutant genes such as ASXL1, DNMT3A, KRAS, NRAS, TP53, SF3B1, and SRSF2. The overall survival (OS) of these genes decreased significantly after DNMT3A, ASXL1, RUNX1, and U2AF1 occurred mutation. These genes play a significant role in biological processes, not only in MDS but also in the occurrence and development of other diseases. Through retrospective analysis, genes associated with MDS-related diseases were identified, and their effects on the disease were predicted. CONCLUSIONS: Thirty-two mutant genes were determined in MDS and when mutations occur in DNMT3A, ASXL1, RUNX1, and U2AF1, their survival time decreases significantly. This results providing a theoretical basis for clinical and scientific research and broadening the scope of research on MDS. AME Publishing Company 2021-10 /pmc/articles/PMC8573449/ /pubmed/34805353 http://dx.doi.org/10.21037/atm-21-4094 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Wu, Kun Nie, Bo Li, Liyin Yang, Xin Yang, Jinrong He, Zhenxin Li, Yanhong Cheng, Shenju Shi, Mingxia Zeng, Yun Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title | Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title_full | Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title_fullStr | Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title_full_unstemmed | Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title_short | Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
title_sort | bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573449/ https://www.ncbi.nlm.nih.gov/pubmed/34805353 http://dx.doi.org/10.21037/atm-21-4094 |
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