MicroRNA‑190b expression predicts a good prognosis and attenuates the malignant progression of pancreatic cancer by targeting MEF2C and TCF4

Li, Yunwei; Wang, Zhe; Zhao, Feng; Zeng, Juan; Yang, Xianghong

doi:10.3892/or.2021.8223

Cited by 4 publications

(2 citation statements)

References 36 publications

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“…46 In this work, MEF2C appeared to be upregulated in glioma, and MEF2C overexpression accelerated glioma cell proliferation and metastasis, prompting an oncogenic role of MEF2C in gliomas. Likewise, previous studies have also validated the oncogenic role of MEF2C in cancers, such as pancreatic cancer, 47 hepatic cancer, 45 and breast cancer. 48 In addition, we detected that MEF2C enhanced the transcription of JAGGED1 in gliomas, contributing to the up-regulation of JAGGED1 expression.…”

Section: Discussionmentioning

confidence: 61%

CircVCAN promotes glioma progression through the miR‐488‐3p/MEF2C‐JAGGED1 axis

Yang,

Gao,

Dong

2024

Environmental Toxicology

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Gliomas are the most prevalent primary malignant brain tumors worldwide. Growing evidences indicate that circular RNAs (circRNAs) play an important role in the regulation of biological behavior of tumors. We aimed to investigate the role and mechanism of circVCAN in glioma. RNase R treatment was utilized to assess the cyclic properties of circVCAN. CircVCAN, miR‐488‐3p, and myocyte enhancer factor 2C (MEF2C) levels in glioma tissues and cells were detected by reverse transcription real‐time polymerase chain reaction (RT‐qPCR), and the localization of them in glioma cells was determined with fluorescence in situ hybridization. Furthermore, a variety of biologically functional assessments were used to validate the role of circVCAN in glioma. The regulatory mechanisms of circVCAN, miR‐488‐3p, and MEF2C were further confirmed by double luciferase reporter gene assay, RNA immunoprecipitation and RNA pull‐down assay, and the binding of MEF2C to JAGGED1 was revealed by chromatin immunoprecipitation. Additionally, a xenograft tumor model was constructed to demonstrate the effect of circVCAN on tumor growth in vivo. Our results indicated that circVCAN was more stable than its linear RNA and was significantly upregulated in gliomas. CircVCAN overexpression stimulated glioma cells to proliferate and metastasize, but circVCAN silencing exerted the opposite effect. Meanwhile, silencing circVCAN inhibited tumor growth in vivo. Moreover, we found that circVCAN interacted with miR‐488‐3p to regulate MEF2C expression, and miR‐488‐3p inhibition or MEF2C overexpression reversed the inhibitory effect on malignant bio‐behaviors mediated by circVCAN knockdown in glioma cells. MEF2C promoted the transcription of JAGGED1, and circVCAN knockdown reduced the binding between MEF2C and JAGGED1. Collectively, circVCAN is a carcinogenic circRNA in glioma, and the circVCAN/miR‐488‐3p/MEF2C‐JAGGED1 axis could serve as a potential target for the management of glioma.

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Section: Discussionmentioning

confidence: 61%

CircVCAN promotes glioma progression through the miR‐488‐3p/MEF2C‐JAGGED1 axis

Yang,

Gao,

Dong

2024

Environmental Toxicology

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“…Inline, Dai et al suggested that hsa-miR-190b-5p is a potential biomarker for breast cancer overall survival [85]. Li et al showed that high expression of hsa-miR-190b-5p is linked to a good prognosis, while low expression has been shown to drive the malignant progression of pancreatic cancer [86].…”

Section: Predictive Sponge Modules Are Regulated By Important Mirna F...mentioning

confidence: 99%

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

Boniolo

Hoffmann

Roggendorf

et al. 2022

Preprint

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Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA-gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. We show how spongEffects can be used for downstream machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects can perform classification tasks and prioritize ceRNA modules as biomarkers, hence offering insights into the miRNA regulatory landscape from gene expression data alone.

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