Super enhancer inhibitors suppress MYC driven transcriptional amplification and tumor progression in osteosarcoma

Chen, Demeng; Zhao, Zhiqiang; Huang, Zixin; Chen, Du-Chu; Zhu, Xinxing; Wang, Yi–Ze; Yan, Ya-Wei; Tang, Shao-Jun; Madhavan, Subha; Ni, Weiyi; Huang, Zhan-Peng; Li, Wen; Ji, Weidong; Shen, Huangxuan; Lin, Shuibin; Jiang, Yi‐Zhou

doi:10.1038/s41413-018-0009-8

Cited by 115 publications

(110 citation statements)

References 35 publications

(52 reference statements)

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“…In recent years, developments in molecular biology have provided new insights into potential diagnostic and therapeutic biomarkers for osteosarcoma. Previous study demonstrated that prometastasis genes such as MYC and RAS facilitate osteosarcoma metastasis and metastasis‐resistant genes including nm23 , p16 and KiSS‐1 metastasis‐suppressor inhibited the metastasis process in osteosarcoma. Furthermore, microarray technology has been widely used for screening a series of metastasis‐related genes in osteosarcoma .…”

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confidence: 99%

“…In recent years, developments in molecular biology have provided new insights into potential diagnostic and therapeutic biomarkers for osteosarcoma. Previous study demonstrated that prometastasis genes such as MYC [7,8] and RAS [9] facilitate osteosarcoma metastasis and metastasis-resistant genes including nm23 [10], p16 [11] and KiSS-1 metastasis-suppressor [12] inhibited the metastasis process in osteosarcoma.Abbreviations AUC, area under receiver operating characteristic curve; GEO, Gene Expression Omnibus; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.…”

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A risk score model for the prediction of osteosarcoma metastasis

et al. 2019

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Osteosarcoma is the most common primary solid malignancy of the bone, and its high mortality usually correlates with early metastasis. In this study, we developed a risk score model to help predict metastasis at the time of diagnosis. We downloaded and mined four expression profile datasets associated with osteosarcoma metastasis from the Gene Expression Omnibus. After data normalization, we performed LASSO logistic regression analysis together with 10‐fold cross validation using the GSE21257 dataset. A combination of eight genes ( RAB 1 , CLEC 3B , FCGBP , RNASE 3 , MDL 1 , ALOX 5 AP , VMO 1 and ALPK 3 ) were identified as being associated with osteosarcoma metastasis. These genes were put into a gene risk score model, and the prediction efficiency of the model was then validated using three independent datasets ( GSE33383 , GSE66673 , and GSE49003 ) by plotting receiver operating characteristic curves. The expression levels of the eight genes in all datasets were shown as heatmaps, and gene ontology gene annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed. These eight genes play a role in cancer‐related biological processes, such as apoptosis and biosynthetic processes. Our results may aid in elucidating the possible mechanisms of osteosarcoma metastasis, and may help to facilitate the individual management of patients with osteosarcoma after treatment.

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confidence: 99%

“…In recent years, developments in molecular biology have provided new insights into potential diagnostic and therapeutic biomarkers for osteosarcoma. Previous study demonstrated that prometastasis genes such as MYC [7,8] and RAS [9] facilitate osteosarcoma metastasis and metastasis-resistant genes including nm23 [10], p16 [11] and KiSS-1 metastasis-suppressor [12] inhibited the metastasis process in osteosarcoma.Abbreviations AUC, area under receiver operating characteristic curve; GEO, Gene Expression Omnibus; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.…”

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confidence: 99%

A risk score model for the prediction of osteosarcoma metastasis

et al. 2019

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“…Univariate Cox regression and Lasso Cox regression analysis showed that a gene signature composed of 9 autophagy-related genes (BNIP3, MYC, BAG1, CALCOCO2, ATF4, AMBRA1, EGFR, MAPK1, and PEX3) was closely related to the prognosis of OS. Studies have shown that the abnormal expression of MYC [52], ATF4 [53], and MAPK1 [54] is related to the proliferation, migration, and invasion of OS cells. As a transcription target of MYC, BAG1 cooperates with HSP70 molecular chaperone to selectively mediate MYC overexpression and affect the survival of OS cells [55].…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

Zhang

Deng

et al. 2020

Preprint

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Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.

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“…Therefore, identification of new specific metastasis‐associated molecules as therapeutic targets or independent predictors of OS metastasis is needed. Recently, many potential prognostic biomarkers and therapeutic targets have been discovered, including MYC and RAS were reported to facilitate OS metastasis. Previous studies found that some genes, such as p16 and nm23 could also inhibit the metastatic process in OS.…”

Section: Introductionmentioning

confidence: 99%

A risk signature‐based on metastasis‐associated genes to predict survival of patients with osteosarcoma

Shi

Zhuang

et al. 2020

J of Cellular Biochemistry

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Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis-associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis-associated genes were identified by limma package. Further, a protein-protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three-gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low-and highrisk groups were significantly different in the training set and validation set.Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin-6 production. Therefore, the three-gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets.differentially expressed genes, metastasis, osteosarcoma, risk signature, survival analysis 1 | INTRODUCTION Osteosarcoma (OS), characterized by malignant mesenchymal cells producing an osteoid matrix and fibrillary stroma, is the most common osseous aggressive cancer in children and juveniles and commonly arises at the terminus of the long bones, including distal femurs, proximal tibias, and proximal humor. 1 According to the National Cancer Institute Surveillance, Epidemiology, and End Results program, the frequency of OS has increased by 0.3% per year over the last decade. 2 With the intensification of chemotherapeutic regimens, the 5-year survival rate of OS patients without metastasis has been improved to 60%-70%. 3 By contrast, the survival rate is only 0%-30% in patients with OS with metastasis, 4 indicating that OS metastasis is associated poor longterm prognosis. However, metastasis-associated molecules Yi Shi and Ronghan He contributed equally to this work.

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Super enhancer inhibitors suppress MYC driven transcriptional amplification and tumor progression in osteosarcoma

Cited by 115 publications

References 35 publications

A risk score model for the prediction of osteosarcoma metastasis

A risk score model for the prediction of osteosarcoma metastasis

Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

A risk signature‐based on metastasis‐associated genes to predict survival of patients with osteosarcoma

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