Myeloid leukemia factor 1 stabilizes tumor suppressor C/EBPα to prevent Trib1-driven acute myeloid leukemia

Nakamae, Ikuko; Kato, Jun‐ya; Yokoyama, Takashi; Ito, Hidenori; Yoneda-Kato, Noriko

doi:10.1182/bloodadvances.2017007054

Cited by 15 publications

(10 citation statements)

References 37 publications

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“…Similar to the other Dickkopf proteins, DKK4 controls the WNT signaling pathway [31]; its differing expression and role in tumor genesis and tumor progression have already been described in multiple tumors, including endometrial carcinoma. The MLF1 gene codes an oncoprotein that is responsible for the determination of the phenotype of hematopoietic cells [32]. Translocation between MLF1 and the nucleophosmin has been associated with myelodysplastic syndrome and acute myeloid leukemia.…”

Section: Discussionmentioning

confidence: 99%

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

Gargya

Bálint

2021

Cancers

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The tumor grade of endometrioid endometrial cancer is used as an independent marker of prognosis and a key component in clinical decision making. It is reported that between grades 1 and 3, however, the intermediate grade 2 carries limited information; thus, patients with grade 2 tumors are at risk of both under- and overtreatment. We used RNA-sequencing data from the TCGA project and machine learning to develop a model which can correctly classify grade 1 and grade 3 samples. We used the trained model on grade 2 patients to subdivide them into low-risk and high-risk groups. With iterative retraining, we selected the most relevant 12 transcripts to build a simplified model without losing accuracy. Both models had a high AUC of 0.93. In both cases, there was a significant difference in the relapse-free survivals of the newly identified grade 2 subgroups. Both models could identify grade 2 patients that have a higher risk of relapse. Our approach overcomes the subjective components of the histological evaluation. The developed method can be automated to perform a prescreening of the samples before a final decision is made by pathologists. Our translational approach based on machine learning methods could allow for better therapeutic planning for grade 2 endometrial cancer patients.

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Section: Discussionmentioning

confidence: 99%

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

Gargya

Bálint

2021

Cancers

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“…DIAPH3 played an important role in the infiltration and metastasis of cancerous cells, which might be a potential therapeutic target for cancer treatment [44]. Myeloid leukemia factor 1 (MLF1) encoded an oncoprotein which was associated with the phenotypic determination of hemopoietic cells [45]. Previous study had proved that the translocations between MLF1 and nucleophosmin were related to myelodyplastic syndrome and acute myeloid [46].…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Yan¹,

Fu²,

Guo³

et al. 2020

J. Cancer

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Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high-and low-risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

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“…The coexpression of the Trib1 mutant and COP1 failed to reduce ACC1 protein levels, indicating that the ability of Trib1 to bind to COP1 is crucial for the targeting of ACC1. We previously reported that MLF1 stabilized the C/EBPA protein by interfering with the formation of the Trib1-COP1 complex in the nucleus (26). However, the coexpression of MLF1 with Trib1 and COP1 did not affect ACC1 protein levels, suggesting that MLF1 stabilized the nuclear protein (C/ EBPA), but not the cytoplasmic protein (ACC1).…”

Section: All Tribbles Have a Cop1-binding Motif ([D/e](x)xxvp[d/e]mentioning

confidence: 94%

Stabilization of fatty acid synthesis enzyme acetyl-CoA carboxylase 1 suppresses acute myeloid leukemia development

Ito¹,

Nakamae²,

Kato³

et al. 2021

Journal of Clinical Investigation

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Myeloid leukemia factor 1 stabilizes tumor suppressor C/EBPα to prevent Trib1-driven acute myeloid leukemia

Cited by 15 publications

References 37 publications

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

Histological Grade of Endometrioid Endometrial Cancer and Relapse Risk Can Be Predicted with Machine Learning from Gene Expression Data

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Stabilization of fatty acid synthesis enzyme acetyl-CoA carboxylase 1 suppresses acute myeloid leukemia development

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