ABSTRACT
Chromosomal translocations and gene mutations are characteristics of the genomic profile of acute myeloid leukemia (AML). We aim to identify a gene signature associated with poor prognosis in AML patients with FLT3-ITD compared to AML patients with NPM1/CEBPA mutations. RNA-sequencing (RNA-Seq) count data were downloaded from the UCSC Xena browser. Samples were grouped by their mutation status into high and low-risk groups. Differential gene expression (DGE), machine learning (ML) and survival analyses were performed. A total of 471 differentially expressed genes (DEGs) were identified, of which 16 DEGs were used as features for the prediction of mutation status. An accuracy of 92% was obtained from the ML model. FHL1, SPNS3, and MPZL2 were found to be associated with overall survival in FLT3-ITD samples. FLT3-ITD mutation confers an indicative gene expression profile different from NPM1/CEBPA mutation, and the expression of FHL1, SPSN3, and MPZL2 can serve as prognostic indicators of unfavorable disease.
Subject(s)
Leukemia, Myeloid, Acute , Nuclear Proteins , Child , Humans , Cell Adhesion Molecules/genetics , fms-Like Tyrosine Kinase 3/genetics , Intracellular Signaling Peptides and Proteins , Leukemia, Myeloid, Acute/diagnosis , Leukemia, Myeloid, Acute/genetics , LIM Domain Proteins/genetics , Muscle Proteins/genetics , Mutation , Nuclear Proteins/genetics , Nucleophosmin , Prognosis , Up-RegulationABSTRACT
Background: Neuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan-Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.
Lay abstract Neuroblastoma is the most common extracranial solid tumor in childhood. Elevated levels of the MYCN protein in neuroblastoma is a predictor of poor prognosis. It is the most relevant prognostic factor in neuroblastoma and predicting MYCN gene amplification (which leads to increased gene expression and more protein) from epigenetic data rather than genetic testing might be useful in the oncology clinic. This study was designed to identify a DNA methylation (epigenetic) signature that can be used to diagnose MYCN amplification without actually testing for the gene. The authors also aimed to correlate this DNA methylation signature with patient survival and poorer prognosis. Based on statistical and computational methods applied to DNA methylation data for neuroblastoma, signatures that are predictive of MYCN amplification and poor prognosis were found, which clinicians can use for early patient diagnosis and selection of the best therapies for patients at high risk.
Subject(s)
Biomarkers, Tumor/genetics , DNA Methylation , Epigenesis, Genetic , N-Myc Proto-Oncogene Protein/genetics , Neuroblastoma/mortality , Child , CpG Islands/genetics , Datasets as Topic , Gene Amplification , Gene Expression Regulation, Neoplastic , Humans , Kaplan-Meier Estimate , Machine Learning , Neuroblastoma/genetics , Prognosis , Progression-Free Survival , Risk Assessment/methodsABSTRACT
Neuroblastoma is the most common extracranial solid tumor in childhood. Patients in high-risk group often have poor outcomes with low survival rates despite several treatment options. This study aimed to identify a genetic signature from gene expression profiles that can serve as prognostic indicators of survival time in patients of high-risk neuroblastoma, and that could be potential therapeutic targets. RNA-seq count data was downloaded from UCSC Xena browser and samples grouped into Short Survival (SS) and Long Survival (LS) groups. Differential gene expression (DGE) analysis, enrichment analyses, regulatory network analysis and machine learning (ML) prediction of survival group were performed. Forty differentially expressed genes (DEGs) were identified including genes involved in molecular function activities essential for tumor proliferation. DEGs used as features for prediction of survival groups included EVX2, NHLH2, PRSS12, POU6F2, HOXD10, MAPK15, RTL1, LGR5, CYP17A1, OR10AB1P, MYH14, LRRTM3, GRIN3A, HS3ST5, CRYAB and NXPH3. An accuracy score of 82% was obtained by the ML classification models. SMIM28 was revealed to possibly have a role in tumor proliferation and aggressiveness. Our results indicate that these DEGs can serve as prognostic indicators of survival in high-risk neuroblastoma patients and will assist clinicians in making better therapeutic and patient management decisions.
