Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery

Simonetti, Giorgia; Padella, Antonella; Valle, Ítalo Faria do; Fontana, Maria Chiara; Fonzi, Eugenio; Bruno, Samantha; Baldazzi, Carmen; Guadagnuolo, Viviana; Manfrini, Marco; Ferrari, Anna; Paolini, Stefania; Papayannidis, Cristina; Marconi, Giovanni; Franchini, Eugenia; Zuffa, Elisa; Laginestra, Maria Antonella; Zanotti, Federica; Astolfi, Annalisa; Iacobucci, Ilaria; Bernardi, Simona; Sazzini, Marco; Ficarra, Elisa; Hernández, Jesús M.; Vandenberghe, Peter; Cools, Jan; Bullinger, Lars; Ottaviani, Emanuela; Testoni, Nicoletta; Cavo, Michèle; Haferlach, Torsten; Castellani, Gastone; Remondini, Daniel; Martinelli, Giovanni

doi:10.1002/cncr.31837

Cited by 50 publications

(49 citation statements)

References 52 publications

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“…The suppression of CDC20 could mediate the tumor suppressing function of p53 and that CDC20 could be negatively regulated by p53. These findings are consistent with the p53 inactivation observed in various cancer tissues, including acute myeloid leukemia and lung cancer (Hu & Chen, 2015;Simonetti et al, 2019); this effect may be ascribed to CDC20 up-regulation. The anaphase-promoting complex CDC20 can suppress apoptosis by targeting Bim for destruction and ubiquitination.…”

Section: Discussionsupporting

confidence: 89%

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Guo

Wang

Sun

et al. 2020

PeerJ

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Purpose Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL. Methods The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein–protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan–Meier method and were compared using the log-rank test. Results Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05). Conclusions The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

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Section: Discussionsupporting

confidence: 89%

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Guo

Wang

Sun

et al. 2020

PeerJ

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“…CBA was performed as previously described [54]. Karyotypes were examined after GAW or GAG banding technique and described according to International System for Human Cytogenomic Nomenclature (ISCN 2016) [55].…”

Section: Chromosome Banding Analysis (Cba)mentioning

confidence: 99%

“…We analysed gene expression and copy number data from a previously obtained internal cohort [54]. Gene expression raw data were processed by Expression Console software with Signal Space Transformation Robust Multi-Array average (sst-RMA) normalization.…”

Section: Gene Expression Profiling (Gep) and Snp-arraymentioning

confidence: 99%

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia

Padella

Simonetti

Paciello

et al. 2019

Cancers

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Approximately 18% of acute myeloid leukemia (AML) cases express a fusion transcript. However, few fusions are recurrent across AML and the identification of these rare chimeras is of interest to characterize AML patients. Here, we studied the transcriptome of 8 adult AML patients with poorly described chromosomal translocation(s), with the aim of identifying novel and rare fusion transcripts. We integrated RNA-sequencing data with multiple approaches including computational analysis, Sanger sequencing, fluorescence in situ hybridization and in vitro studies to assess the oncogenic potential of the ZEB2-BCL11B chimera. We detected 7 different fusions with partner genes involving transcription factors (OAZ-MAFK, ZEB2-BCL11B), tumor suppressors (SAV1-GYPB, PUF60-TYW1, CNOT2-WT1) and rearrangements associated with the loss of NF1 (CPD-PXT1, UTP6-CRLF3). Notably, ZEB2-BCL11B rearrangements co-occurred with FLT3 mutations and were associated with a poorly differentiated or mixed phenotype leukemia. Although the fusion alone did not transform murine c-Kit+ bone marrow cells, 45.4% of 14q32 non-rearranged AML cases were also BCL11B-positive, suggesting a more general and complex mechanism of leukemogenesis associated with BCL11B expression. Overall, by combining different approaches, we described rare fusion events contributing to the complexity of AML and we linked the expression of some chimeras to genomic alterations hitting known genes in AML.

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“…This result provided the rationale for targeting PLK1 in the treatment of AML, the clinical trials had been conducted [60,61]. The association of PLK1 over-expression and silencing of TP53 was reported in aneuploid AML [36]. Kinesin superfamily proteins (KIFs) function mainly as molecular motors binding to and moving across the microtube network [62,63].…”

Section: Discussionmentioning

confidence: 98%

“…Other preclinical studies were performed to explore the anti-tumor mechanism of APC/C inhibitors, in cervical cancer, osteosarcoma, colorectal and lung cancer [34,35]. The association of CDC20 upregulation with TP53 null mutation or functional silencing was uncovered in aneuploid AML [36], which suggested CDC20 as a potential target and biomarker in TP53 mutant AML. The regulatory subunit of cyclin B (encoded by CCNB1) and CDK1 (encoded by CDK1) constitutes the mitosis promoting factor (MPF), which controls the entry or exit of mitosis [37].…”

Section: Discussionmentioning

confidence: 99%

The Landscape of Gene Co-Expression Modules Correlating with Prognostic Genetic Abnormalities in AML

Guo

Gao

et al. 2020

Preprint

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Background: The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.Methods: We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and ‘WGCNA’ package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by ‘glmnet’ package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. Results: A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the ‘lightyellow’ module for NPM1 mutation, the ‘saddlebrown’ module for RUNX1 mutation, the ‘lightgreen’ module for TP53 mutation). ORA revealed that the ‘lightyellow’ module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The ‘saddlebrown’ module was enriched in immune response process. And the ‘lightgreen’ module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79).Conclusions: We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

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Aneuploid acute myeloid leukemia exhibits a signature of genomic alterations in the cell cycle and protein degradation machinery

Cited by 50 publications

References 52 publications

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Novel and Rare Fusion Transcripts Involving Transcription Factors and Tumor Suppressor Genes in Acute Myeloid Leukemia

The Landscape of Gene Co-Expression Modules Correlating with Prognostic Genetic Abnormalities in AML

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