biostudies-literatureZhuang HNatural Science Foundation of Zhejiang ProvinceFoundation of Zhejiang province Chinese medicine science and technology planesKey project of the 2017 school research fund of Zhejiang Chinese Medical UniversityZhejiang Provincial Medical and Health Science and Technology Projecte9437https://www.ebi.ac.uk/biostudies/studies/S-EPMC7321666biostudies-literatureUnknown8<h4>Background</h4>Currently, 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.<h4>Methods</h4>First 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 (<i>n</i> = 229) were randomized as test set and training set, respectively. Of them, the training set was used to screen for genes related to prognosis, and genes with mutation, SNP or CNV. Then, shrinkage estimate was used for feature selection of all the as-screened genes, to select those stable biomarkers. Eventually, a prognPeerJSearching for a signature involving 10 genes to predict the survival of patients with acute myelocytic leukemia through a combined multi-omics analysis.PMC73216662017ZZ0220182773102020KY1962020ZA0442017ZB030LY19H290003LQ20H280002Hong LSheng XGao RZhuang HZhuang XChen YfalseSearching for a signature involving 10 genes to predict the survival of patients with acute myelocytic leukemia through a combined multi-omics analysis.<h4>Background</h4>Currently, 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.<h4>Methods</h4>First 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 (<i>n</i> = 229) were randomized as test set and training set, respectively. Of them, the training set was used to screen for genes related to prognosis, and genes with mutation, SNP or CNV. Then, shrinkage estimate was used for feature selection of all the as-screened genes, to select those stable biomarkers. Eventually, a progn2020-01-01T00:00:00Z20202026-04-30T11:24:03.758Z2020-07-05T07:05:58ZS-EPMC73216663261719510.7717/peerj.9437