Development and validation of a novel survival model for acute myeloid leukemia based on autophagy-related genes

• Published in: PeerJ (San Francisco, CA) Vol. 9; p. e11968
• Main Authors: Huang, Li, Lin, Lier, Fu, Xiangjun, Meng, Can
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 12.08.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Acute myeloid leukemia; Analysis; Autophagy; Bioinformatics; Bone marrow; Cancer; Care and treatment; CD4 antigen; Cell death; CTLA-4 protein; Gene expression; Genes; Genetic aspects; Genomes; Genomics; GEO; Hematology; Immune checkpoint; Immunosuppressive agents; Immunotherapy; Killer cells; Leukemia; Localization; Lymphocytes T; Mast cells; Medical Genetics; Medical prognosis; Medical research; Medicine, Experimental; Myelocytic leukemia; Myeloid leukemia; Natural killer cells; Nonlymphoid leukemia; Oncology; Patients; Phagocytosis; Proteins; Risk groups; Risk model; Software packages; T cells; TCGA; Tumor cells; Tumorigenesis; Tumors; GEO; Risk model; Acute myeloid leukemia; Autophagy; TCGA
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.11968

Acute myeloid leukemia (AML) is one of the most common blood cancers, and is characterized by impaired hematopoietic function and bone marrow (BM) failure. Under normal circumstances, autophagy may suppress tumorigenesis, however under the stressful conditions of late stage tumor growth autophagy actually protects tumor cells, so inhibiting autophagy in these cases also inhibits tumor growth and promotes tumor cell death. AML gene expression profile data and corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, from which prognostic-related genes were screened to construct a risk score model through LASSO and univariate and multivariate Cox analyses. Then the model was verified in the TCGA cohort and GEO cohorts. In addition, we also analyzed the relationship between autophagy genes and immune infiltrating cells and therapeutic drugs. We built a model containing 10 autophagy-related genes to predict the survival of AML patients by dividing them into high- or low-risk subgroups. The high-risk subgroup was prone to a poorer prognosis in both the training TCGA-LAML cohort and the validation GSE37642 cohort. Univariate and multivariate Cox analysis revealed that the risk score of the autophagy model can be used as an independent prognostic factor. The high-risk subgroup had not only higher fractions of CD4 naïve T cell, NK cell activated, and resting mast cells but also higher expression of immune checkpoint genes and . Last, we screened drug sensitivity between high- and low-risk subgroups. The risk score model based on 10 autophagy-related genes can serve as an effective prognostic predictor for AML patients and may guide for patient stratification for immunotherapies and drugs.