DLC1 deficiency at diagnosis predicts poor prognosis in acute myeloid leukemia

Abstract

Abstract Acute myeloid leukemia (AML) is a complex, heterogeneous malignant hematologic disease. Although multiple prognostic-related genes gave been explored in previous studies, there are still many genes whose prognostic value remains unclear. In this study, a total of 1532 AML patients from three GEO databases were included, five genes with potential prognostic value (DLC1, NF1B, DENND5B, TANC2 and ELAVL4) were screened by weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE). Based on this, we conducted survival analysis of the above five genes through the TCGA database and found that low level of DLC1 was detrimental to the long-term prognosis of AML patients. We also performed external validation in 48 AML patients from our medical center to analyze the impact of DLC1 level on prognosis. In conclusion, DLC1 may be a potential marker affecting the prognosis of AML, and its deficiency is associated with poor prognosis.

Keywords

• Acute myeloid leukemia
• Weighted gene co-expression network analysis
• Differential gene expression analysis
• DLC1
• Machine learning