ArticlePeerJ2020
Searching for a signature involving 10 genes to predict the survival of patients with acute myelocytic leukemia through a combined multi-omics analysis.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Recent advances in the study of FAT family genes in lung cancer.Discover oncology · 2025Review
- Gene expression profiling and pathway analysis in acute myeloid leukaemia-normal karyotype patients.PloS one · 2025Article
- MT1E in AML: a gateway to understanding regulatory cell death and immunotherapeutic responses.Journal of leukocyte biology · 2024Article
- CAP superfamily proteins in human: a new target for cancer therapy.Medical oncology (Northwood, London, England) · 2024Review
- Article
- Use of extracellular vesicle microRNA profiles in patients with acute myeloid leukemia for the identification of novel biomarkers.PloS one · 2024Article
- FAT2 mutation is associated with better prognosis and responsiveness to immunotherapy in uterine corpus endometrial carcinoma.Cancer medicine · 2023Article
- High Expression of DNTTIP1 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma.Pharmacogenomics and personalized medicine · 2023Article
- DNTTIP1 is a Prognostic Biomarker Correlated With Immune Infiltrates in Hepatocellular Carcinoma: A Study Based on The Cancer Genome Atlas Data.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrently, acute myelocytic leukemia (AML) still has a poor prognosis. As a result, gene markers for predicting AML prognosis must be identified through systemic analysis of multi-omics data.
methodsFirst of all, the copy number variation (CNV), mutation, RNA-Seq, and single nucleotide polymorphism (SNP) data, as well as those clinical follow-up data, were obtained based on The Cancer Genome Atlas (TCGA) database. Thereafter, all samples (
resultsAltogether 832 genes related to prognosis, 23 related to copy amplification, 774 associated with copy deletion, and 189 with significant genomic variations were acquired in this study. Later, genes with genomic variations and those related to prognosis were integrated to obtain 38 candidate genes; eventually, a shrinkage estimate was adopted to obtain 10 feature genes (including FAT2, CAMK2A, TCERG1, GDF9, PTGIS, DOC2B, DNTTIP1, PREX1, CRISPLD1 and C22orf42). Further, a signature was established using these 10 genes based on Cox regression analysis, and it served as an independent factor to predict AML prognosis. More importantly, it was able to stratify those external verification, test and training set samples with regard to the risk (
conclusionThe signature based on 10 genes had been established in this study, which is promising to be used to be a new marker for predicting AML prognosis.
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