scholarly journals HOXBLINC long non-coding RNA activation promotes leukemogenesis in NPM1-mutant acute myeloid leukemia

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ganqian Zhu ◽  
Huacheng Luo ◽  
Yang Feng ◽  
Olga A. Guryanova ◽  
Jianfeng Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Expression Patterns ◽  
Promoter Regions ◽  
Signature Genes ◽  
Non Coding Rna ◽  
Rna Activation ◽  
Long Non Coding Rna ◽  
Acute Myeloid

AbstractNucleophosmin (NPM1) is the most commonly mutated gene in acute myeloid leukemia (AML) resulting in aberrant cytoplasmic translocation of the encoded nucleolar protein (NPM1c+). NPM1c+ maintains a unique leukemic gene expression program, characterized by activation of HOXA/B clusters and MEIS1 oncogene to facilitate leukemogenesis. However, the mechanisms by which NPM1c+ controls such gene expression patterns to promote leukemogenesis remain largely unknown. Here, we show that the activation of HOXBLINC, a HOXB locus-associated long non-coding RNA (lncRNA), is a critical downstream mediator of NPM1c+-associated leukemic transcription program and leukemogenesis. HOXBLINC loss attenuates NPM1c+-driven leukemogenesis by rectifying the signature of NPM1c+ leukemic transcription programs. Furthermore, overexpression of HoxBlinc (HoxBlincTg) in mice enhances HSC self-renewal and expands myelopoiesis, leading to the development of AML-like disease, reminiscent of the phenotypes seen in the Npm1 mutant knock-in (Npm1c/+) mice. HoxBlincTg and Npm1c/+ HSPCs share significantly overlapped transcriptome and chromatin structure. Mechanistically, HoxBlinc binds to the promoter regions of NPM1c+ signature genes to control their activation in HoxBlincTg HSPCs, via MLL1 recruitment and promoter H3K4me3 modification. Our study reveals that HOXBLINC lncRNA activation plays an essential oncogenic role in NPM1c+ leukemia. HOXBLINC and its partner MLL1 are potential therapeutic targets for NPM1c+ AML.

scholarly journals NPM1 mutation mediated HOXBLINC lncRNA activation promotes AML leukemogenesis

2020 ◽  
Author(s):  
Ganqian Zhu ◽  
Huacheng Luo ◽  
Yang Feng ◽  
Olga Guryanova ◽  
Jianfeng Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Promoter Regions ◽  
Npm1 Mutation ◽  
Signature Genes ◽  
Non Coding Rna ◽  
Expression Program ◽  
Long Non Coding Rna ◽  
Transcription Program ◽  
Downstream Mediator

Abstract Nucleophosmin (NPM1) is the most commonly mutated gene in acute myeloid leukemia (AML) resulting in aberrant cytoplasmic translocation of the encoded nucleolar protein (NPM1c+). NPM1c+ maintains a unique leukemic gene expression program, characterized by activation of HOXA/B clusters and MEIS1 oncogene to facilitate leukemogenesis. However, the mechanisms by which NPM1c+ controls such gene expression patterns to promote leukemogenesis remain largely unknown. Here, we show that the activation of HOXBLINC, a HOXB locus-associated long non-coding RNA (lncRNA), is a critical downstream mediator of NPM1c+-associated leukemic transcription program and leukemogenesis. HOXBLINC loss attenuates NPM1c+-driven leukemogenesis by rectifying the signature of NPM1c+ leukemic transcription programs. Furthermore, overexpression of HoxBlinc (HoxBlincTg) in mice enhances HSC self-renewal and expands myelopoiesis, leading to the development of AML-like disease, reminiscent of the phenotypes seen in the Npm1 mutant knock-in (Npm1c/+) mice. HoxBlincTg and Npm1c/+ HSPCs share significantly overlapped transcriptome and chromatin structure. Mechanistically, HoxBlinc binds to the promoter regions of NPM1c+ signature genes to control their activation in HoxBlincTg HSPCs, via MLL1 recruitment and promoter H3K4me3 modification. Our study reveals that HOXBLINC lncRNA activation plays an essential oncogenic role in NPM1c+ leukemia. HOXBLINC and its partner MLL1 are potential therapeutic targets for NPM1c+ AML.

scholarly journals Iluntasunetik argirantz, genoma ez-kodetzailea kodetuz

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Xabier Agirre ◽  
Naroa Gimenez ◽  
Ane Amundarain
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Non Coding Rna ◽  
Long Non Coding Rna ◽  
Acute Myeloid

 Azken urteetan gertatu den genomen ikerketarako teknologien garapen ikaragarriak zientzialariek genoma, transkriptoma eta proteomaren inguruan zuten ikuspegia errotik aldatzea eragin du. DNAren eta RNAren sekuentziazio masiboei esker, ordura arte “DNA zabor” bezala ezagututako guneetatik transkribatutako proteinarik kodetzen ez duten milaka RNA ez-kodetzaile (ncRNA) detektatu dira, genomen inguruko ikuspegi proteozentrikoa alboratuz eta RNA ez-kodetzaileak, bereziki luzeak (long non-coding RNA, lncRNA), ikertzaileen fokuan jarriz. Era berean, azken ikerketek geneen adierazpenerako ezinbestekoak diren transkripzio- eta itzulpen-prozesuen erregulazioak duen garrantzia ere agerian jarri dute. Erregulazio-mekanismo nagusietako bat DNAn, RNAn eta proteinetan aurkitu diren marka biokimiko itzulgarriek osatzen dute; eta horien ikerketak epigenomika, epitranskriptomika eta epiproteomika izeneko biologiaren atal berrien sorrera ekarri du. Hala ere, ikerketa gehienak epigenoma eta epiproteomaren inguruan egin direnez, marka horien ezarpenak RNAn duen eraginak ezezaguna izaten jarraitzen du. Emaitza hauek tarteko, zalantzarik gabe RNAren biologiaren esparruan galdera asko dago oraindik erantzuteko. Hori dela-eta, berrikuspen honetan aipatutako RNAren biologiaren bi esparru berrietan fokuratu gara, horien inguruan ezaguna dena jasoz eta giza gaixotasunekin, zehazki leuzemia mieloide akutuarekin (Acute myeloid leukemia-AML), duten harremana laburbilduz. Oraindik alor ezezagunak izanik, ikertzaileek tamaina eta konplexutasun handiko erronka dute aurrean RNAren biologiaren mundu ilun hau argitu ahal izateko eta emaitza hauek arlo klinikora bideratzeko. 

scholarly journals Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression

2019 ◽  
Vol 18 ◽  
pp. 117693511983554 ◽  
Author(s):  
Ophir Gal ◽  
Noam Auslander ◽  
Yu Fan ◽  
Daoud Meerzaman
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Acute Myeloid Leukemia ◽  
Complete Remission ◽  
Myeloid Leukemia ◽  
Statistical Significance ◽  
Expression Patterns ◽  
Pathway Enrichment Analysis ◽  
Data Set ◽  
Acute Myeloid

Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of treatment and the samples taken from patients who did not benefit. We tuned classifier hyperparameters to optimize performance and used multiple methods to guide our feature selection as well as our assessment of algorithm performance. To identify the model which performed best within the context of this study, we plotted receiver operating characteristic (ROC) curves. Using the top 75 genes from the k-nearest neighbors algorithm (K-NN) model ( K = 27) yielded the best area-under-the-curve (AUC) score that we obtained: 0.84. When we finally tested the previously unseen test data set, the top 50 genes yielded the best AUC = 0.81. Pathway enrichment analysis for these 50 genes showed that the guanosine diphosphate fucose (GDP-fucose) biosynthesis pathway is the most significant with an adjusted P value = .0092, which may suggest the vital role of N-glycosylation in AML.

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