Literature DB >> 36258263

DLC1 deficiency at diagnosis predicts poor prognosis in acute myeloid leukemia.

Xueqian Li1,2, Jiaqian Qi1,2,3, Xiaofei Song1,2, Xiaoyan Xu1,2, Tingting Pan1,2, Hong Wang1,2,3, Jingyi Yang1,2, Yue Han4,5,6,7.   

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.
© 2022. The Author(s).

Entities:  

Keywords:  Acute myeloid leukemia; DLC1; Differential gene expression analysis; Machine learning; Weighted gene co-expression network analysis

Year:  2022        PMID: 36258263      PMCID: PMC9580124          DOI: 10.1186/s40164-022-00335-5

Source DB:  PubMed          Journal:  Exp Hematol Oncol        ISSN: 2162-3619


  11 in total

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5.  Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia.

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6.  Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application.

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7.  Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA).

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Journal:  Cancers (Basel)       Date:  2019-12-21       Impact factor: 6.639

8.  SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.

Authors:  Hector Sanz; Clarissa Valim; Esteban Vegas; Josep M Oller; Ferran Reverter
Journal:  BMC Bioinformatics       Date:  2018-11-19       Impact factor: 3.169

9.  The tumor suppressor activity of DLC1 requires the interaction of its START domain with Phosphatidylserine, PLCD1, and Caveolin-1.

Authors:  Beatriz Sanchez-Solana; Dunrui Wang; Xiaolan Qian; Parthibane Velayoudame; Dhirendra K Simanshu; Jairaj K Acharya; Douglas R Lowy
Journal:  Mol Cancer       Date:  2021-11-02       Impact factor: 27.401

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