Dataset Information

N6-methyladenosine-induced ERR? triggers chemoresistance of cancer cells through upregulation of ABCB1 and metabolic reprogramming.

ABSTRACT: Background: Drug resistance severely reduces treatment efficiency of chemotherapy and leads to poor prognosis. However, regulatory factors of chemoresistant cancer cells are largely unknown. Methods: The expression of estrogen receptor related receptors (ERRs) in chemoresistant cancer cells are checked. The roles of ERR? in chemoresistance are confirmed by in vitro and in vivo studies. The mechanisms responsible for ERR?-regulated expression of ABCB1 and CPT1B are investigated. Results: The expression of ERR? is upregulated in chemoresistant cancer cells. Targeted inhibition of ERR? restores the chemosensitivity. ERR? can directly bind to the promoter of ABCB1 to increase its transcription. An elevated interaction between ERR? and p65 in chemoresistant cells further strengthens transcription of ABCB1. Further, ERR? can increase the fatty acid oxidation (FAO) in chemoresistant cells via regulation of CPT1B, the rate-limiting enzyme of FAO. The upregulated ERR? in chemoresistant cancer cells might be due to increased levels of N6-methyladenosine (m6A) can trigger the splicing of precursor ESRRG mRNA. Conclusions: m6A induced ERR? confers chemoresistance of cancer cells through upregulation of ABCB1 and CPT1B.

SUBMITTER: Chen Z

PROVIDER: S-EPMC7069076 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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N6-methyladenosine-induced ERRγ triggers chemoresistance of cancer cells through upregulation of ABCB1 and metabolic reprogramming.

Chen Zhuojia Z Wu Long L Zhou Jiawang J Lin Xinyao X Peng Yanxi Y Ge Lichen L Chiang Cheng-Ming CM Huang Hui H Wang Hongsheng H He Weiling W

Theranostics 20200210 8

Background: Drug resistance severely reduces treatment efficiency of chemotherapy and leads to poor prognosis. However, regulatory factors of chemoresistant cancer cells are largely unknown. Methods: The expression of estrogen receptor related receptors (ERRs) in chemoresistant cancer cells are checked. The roles of ERRγ in chemoresistance are confirmed by in vitro and in vivo studies. The mechanisms responsible for ERRγ-regulated expression of ABCB1 and CPT ...[more]

PMID: 32206097

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Project description:N6-methyladenosine (m6A) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA m6A transcriptome-wide at base resolution via experimental approaches, which are generally time-consuming and expensive. Developing computational methods is a good strategy for accurate in silico detection of m6A modification sites from the large amount of RNA sequence data. Unfortunately, the existing computational models are usually only for m6A site prediction in a single species, without considering the tissue level of species, while most of them are constructed based on low-confidence level data generated by an m6A antibody immunoprecipitation (IP)-based sequencing method, thereby restricting reliability and generalizability of proposed models. Here, we review recent advances in computational prediction of m6A sites and construct a new computational approach named im6APred using ensemble deep learning to accurately identify m6A sites based on high-confidence level data in multiple tissues of mammals. Our model im6APred builds upon a comprehensive evaluation of multiple classification methods, including four traditional classification algorithms and three deep learning methods and their ensembles. The optimal base-classifier combinations are then chosen by five-fold cross-validation test to achieve an effective stacked model. Our model im6APred can produce the area under the receiver operating characteristic curve (AUROC) in the range of 0.82-0.91 on independent tests, indicating that our model has the ability to learn general methylation rules on RNA bases and generalize to m6A transcriptome-wide identification. Moreover, AUROCs in the range of 0.77-0.96 were achieved using cross-species/tissues validation on the benchmark dataset, demonstrating differences in predictive performance at the tissue level and the need for constructing tissue-specific models for m6A site prediction.

Dataset Information

N6-methyladenosine-induced ERR? triggers chemoresistance of cancer cells through upregulation of ABCB1 and metabolic reprogramming.

Publications

<i>N6</i>-methyladenosine-induced ERRγ triggers chemoresistance of cancer cells through upregulation of ABCB1 and metabolic reprogramming.

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