Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients

Kun Wu; Bo Nie; Liyin Li; Xin Yang; Jinrong Yang; Zhenxin He; Yanhong Li; Shenju Cheng; Mingxia Shi; Yun Zeng

doi:10.21037/atm-21-4094

Bioinformatics analysis of high frequency mutations in myelodysplastic syndrome-related patients

Ann Transl Med. 2021 Oct;9(19):1491. doi: 10.21037/atm-21-4094.

Authors

Kun Wu^#^{1

2

3}, Bo Nie^#⁴, Liyin Li⁴, Xin Yang⁴, Jinrong Yang⁴, Zhenxin He⁴, Yanhong Li^{1

2

3}, Shenju Cheng^{1

2

3}, Mingxia Shi⁴, Yun Zeng⁴

Affiliations

¹ Department of Clinical Laboratory, First Affiliated Hospital of Kunming Medical University, Kunming, China.
² Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.
³ Yunnan Innovation Team of Clinical Laboratory and Diagnosis, Kunming, China.
⁴ Department of Hematology, First Affiliated Hospital of Kunming Medical University, Hematology Research Center of Yunnan Province, Kunming, China.

^# Contributed equally.

Abstract

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.

Keywords: Gene Ontology (GO); Myelodysplastic syndrome (MDS); next generation sequencing (NGS); prognosis; protein-protein interaction (PPI).