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An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia
- Source :
- Journal of Oncology, Journal of Oncology, Vol 2021 (2021)
- Publication Year :
- 2021
- Publisher :
- Hindawi Limited, 2021.
-
Abstract
- Objective. The aim of this research was to create a new genetic signature of immune checkpoint-associated genes as a prognostic method for pediatric acute myeloid leukemia (AML). Methods. Transcriptome profiles and clinical follow-up details were obtained in Therapeutically Applicable Research to Generate Effective Treatments (TARGET), a database of pediatric tumors. Secondary data was collected from the Gene Expression Omnibus (GEO) to test the observations. In univariate Cox regression and multivariate Cox regression studies, the expression of immune checkpoint-related genes was studied. A three-mRNA signature was developed for predicting pediatric AML patient survival. Furthermore, the GEO cohort was used to confirm the reliability. A bioinformatics method was utilized to identify the diagnostic and prognostic value. Results. A three-gene (STAT1, BATF, EML4) signature was developed to identify patients into two danger categories depending on their OS. A multivariate regression study showed that the immune checkpoint-related signature (STAT1, BATF, EML4) was an independent indicator of pediatric AML. By immune cell subtypes analyses, the signature was correlated with multiple subtypes of immune cells. Conclusion. In summary, our three-gene signature can be a useful tool to predict the OS in AML patients.
- Subjects :
- 0301 basic medicine
Oncology
medicine.medical_specialty
Multivariate statistics
Article Subject
business.industry
Proportional hazards model
Univariate
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Immune checkpoint
03 medical and health sciences
030104 developmental biology
0302 clinical medicine
Immune system
030220 oncology & carcinogenesis
Internal medicine
BATF
Cohort
Medicine
business
Gene
RC254-282
Research Article
Subjects
Details
- ISSN :
- 16878469 and 16878450
- Volume :
- 2021
- Database :
- OpenAIRE
- Journal :
- Journal of Oncology
- Accession number :
- edsair.doi.dedup.....f25431945374e0ddf24c0437c17a350d