MYC-regulated pseudogene HMGA1P6 promotes ovarian cancer malignancy via augmenting the oncogenic HMGA1/2

Tian, Xiaoxue; Song, Jianping; Zhang, Xiyu; Yan, Ming-Yao; Wang, Shourong; Wang, Yuqiong; Xu, Limei; Zhao, Ling; Wei, Jian‐Jun; Shao, Changshun; Kong, Beihua; Liu, Zhaojian

doi:10.1038/s41419-020-2356-9

Cited by 34 publications

(27 citation statements)

References 39 publications

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“…Accumulating evidence revealed that pseudogenes manifest functional roles. As we have known, pseudogene-related lncRNAs have been demonstrated to adjust their parent genes and the new discovered mechanism has been noted in various cancer kinds, including OC [12,13]. Some tumor-related pseudogenes can be utilized as predictors of prognosis.…”

Section: Introductionmentioning

confidence: 99%

Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Zhao

Wei

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

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Section: Introductionmentioning

confidence: 99%

Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Zhao

Wei

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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“…(40) The HMGA2 far-upstream Tier-1 SNPs overlap predicted allelespecific sites for TFs associated with bone or cartilage biology (Supplementary Table S6). Of special interest is HMGA1, a chromatin architectural protein similar to HMGA2, which is posttranscriptionally regulated by some of the same miRNAs that control HMGA2 RNA (41) and can upregulate Wnt signaling. (42) Moreover, HMGA1 is critical for forming and maintaining bone and downregulates miR-196A-2, which targets HMGA2 RNA in mouse embryonic fib.…”

Section: Hmga2 Has Three Far-upstream Bmd-riskmentioning

confidence: 99%

Prioritization of Osteoporosis‐Associated Genome‐wide Association Study (GWAS) Single‐Nucleotide Polymorphisms (SNPs) Using Epigenomics and Transcriptomics

et al. 2021

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Genetic risk factors for osteoporosis, a prevalent disease associated with aging, have been examined in many genome‐wide association studies (GWASs). A major challenge is to prioritize transcription‐regulatory GWAS‐derived variants that are likely to be functional. Given the critical role of epigenetics in gene regulation, we have used an unusual epigenetics‐based and transcription‐based approach to identify some of the credible regulatory single‐nucleotide polymorphisms (SNPs) relevant to osteoporosis from 38 reported bone mineral density (BMD) GWASs. Using Roadmap databases, we prioritized SNPs based upon their overlap with strong enhancer or promoter chromatin preferentially in osteoblasts relative to 12 heterologous cell culture types. We also required that these SNPs overlap open chromatin (Deoxyribonuclease I [DNaseI]‐hypersensitive sites) and DNA sequences predicted to bind to osteoblast‐relevant transcription factors in an allele‐specific manner. From >50,000 GWAS‐derived SNPs, we identified 14 novel and credible regulatory SNPs (Tier‐1 SNPs) for osteoporosis risk. Their associated genes, BICC1, LGR4, DAAM2, NPR3, or HMGA2, are involved in osteoblastogenesis or bone homeostasis and regulate cell signaling or enhancer function. Four of these genes are preferentially expressed in osteoblasts. BICC1, LGR4, and DAAM2 play important roles in canonical Wnt signaling, a pathway critical for bone formation and repair. The transcription factors predicted to bind to the Tier‐1 SNP‐containing DNA sequences also have bone‐related functions. We present evidence that some of the Tier‐1 SNPs exert their effects on BMD risk indirectly through little‐studied long noncoding RNA (lncRNA) genes, which may, in turn, control the nearby bone‐related protein‐encoding gene. Our study illustrates a method to identify novel BMD‐related causal regulatory SNPs for future study and to prioritize candidate regulatory GWAS‐derived SNPs, in general. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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“…This study integrated analysis two datasets GSE119056 [56] and GSE135886 [57], which were from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). GSE119056 contains 12 cases of ovarian malignant tumor tissues and 6 cases of ovarian normal tissues, and GSE119056 was divided into two subseries GSE119054 and GSE119055.…”

Section: Data Acquisitionmentioning

confidence: 99%

Identification of LINC01503 as Biomarker Regulated by CTBP1 with Prognostic and Diagnostic Role in Epithelial Ovarian Cancer

Wang

Wei²,

Zhang

et al. 2021

Preprint

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Background: Epithelial ovarian cancer (EOC) is a disease with high morbidity and mortality worldwide, which is seriously harmful to female health. LncRNA has an important relationship with the occurrence and development of tumors. Hence, the investigation of the underlying mechanism between LncRNA and EOC is of great importance.Results: In this study, we found that LINC01503 was highly expressed in EOC with a poor prognosis based on microarray datasets GSE119056 and GSE135886 obtained from Gene Expression Omnibus (GEO) database, and this result was verified by RT-qPCR. The database lncBase Predicted v.2 and starBase v2.0 were used to predict the targeted relationship of lncRNA-miRNA-mRNA, then the ceRNA network was established by Cytoscape software. Following, the expression and overall survival (OS) analysis of key lncRNAs were analyzed by GEPIA and Kaplan-Meier plotter database. Gene Ontology (GO) functional enrichment analysis was performed by DAVID database and enriched two cancer related biological processes (BP) that response to endoplasmic reticulum stress and IRE1-mediated unfolded protein. Moreover, we verified that LINC01503 was an oncogene regulated by C-terminal binding protein 1 (CTBP1) to promote cell proliferation, migration and inhibited cell apoptosis in ovarian cancer. Conclusion: In conclusion, these results identified LINC01503 as a potential gene for EOC diagnosis and prognosis.

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MYC-regulated pseudogene HMGA1P6 promotes ovarian cancer malignancy via augmenting the oncogenic HMGA1/2

Cited by 34 publications

References 39 publications

Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

Prioritization of Osteoporosis‐Associated Genome‐wide Association Study (GWAS) Single‐Nucleotide Polymorphisms (SNPs) Using Epigenomics and Transcriptomics

Identification of LINC01503 as Biomarker Regulated by CTBP1 with Prognostic and Diagnostic Role in Epithelial Ovarian Cancer

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