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  • Identification of Survival-Related Genes in Acute Myeloid Leukemia: A Study Based on TCGA Data Analysis

  • Melika Asayesh,1,* Mohammad Rafiee,2
    1. Student Research Committee, Hamedan University of Medical Science, Hamadan, Iran.
    2. Department of Medical Laboratory Sciences, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran


  • Introduction: Acute myeloid leukemia (AML) is the most common leukemia in adults, with a 5-year overall survival (OS) rate of approximately 30%. It is characterized by the clonal expansion of myeloid precursor cells driven by genomic alterations that impair normal myeloid differentiation. The substantial molecular heterogeneity characteristic of AML contributes to variability in treatment responses, underscoring the critical importance of understanding the prognostic implications of genetic alterations for guiding therapeutic decisions. Conventional differential expression analyses, however, typically examine genes in isolation and overlook the coordinated, network-level interactions that more accurately reflect underlying biology. Weighted gene co-expression network analysis (WGCNA) addresses this limitation by grouping genes into co-expression modules and identifying hub genes linked to clinical traits, while machine-learning (ML) approaches provide complementary, data-driven tools for feature selection and prognostic model construction. This combined approach may help refine risk stratification and support more personalized treatment decisions for this molecularly complex disease. In this study, we applied systems biology approaches to AML gene expression profiles to identify survival-associated genes, which were subsequently validated by RT-qPCR.
  • Methods: In this study, transcriptomic data from adult AML patients were obtained from The Cancer Genome Atlas (TCGA) database. Following data preprocessing, Weighted Gene Co-expression Network Analysis (WGCNA) was performed to identify gene co-expression modules associated with survival. Genes from the selected module were subsequently analyzed using multiple machine learning algorithms, including LASSO, RSF, ENET, and AdaLASSO, which were fitted within a Cox proportional hazards framework using the survival outcome Surv(OS_time, OS_event). The top 9 genes with the highest selection frequency were retained as the final candidate gene panel for downstream analysis. The prognostic relevance of the ML-derived candidate genes was further validated in an independent AML cohort from the Gene Expression Omnibus (GEO) database (GSE37642, GPL96). Finally, selected candidate genes were validated by RT-qPCR in AML samples.
  • Results: WGCNA highlighted the turquoise module as the most strongly associated with survival (cor = 0.51, p = 1.7 × 10⁻⁶⁰). Based on the mean selection frequency across all four machine learning algorithms, the top nine genes, LRCH4, ACOT7, TXN2, GCDH, GIGYF1, INPP5E, MVD, FIBP, and PDLIM7, were retained as the final candidate gene panel. In the independent AML cohort, ACOT7, TXN2, and PDLIM7 showed significantly higher expression in deceased than in surviving patients, with concordant positive log₂FC values. RT-qPCR validation confirmed significant upregulation of PDLIM7 (p = 0.016) and an increased expression trend of TXN2 (p = 0.710) in AML samples, consistent with bioinformatics predictions, while ACOT7 exhibited significant downregulation (p = 0.038), contrasting with the upregulation observed in the discovery dataset.
  • Conclusion: In conclusion, our study identified three novel potential prognosis-related genes in AML, including PDLIM7, TXN2, and ACOT7. The differential expression levels of these genes in AML samples suggest that they could be possible biomarkers for predicting survival outcomes in AML. However, further investigation into the functions of these genes is required to determine their molecular mechanisms in the progression of AML.
  • Keywords: Acute myeloid leukemia, TCGA, weighted gene co-expression network analysis, machine learning

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