Mechanism underlying circRNA dysregulation in the TME of digestive system cancer

Wu, Zeyu; Yu, Xiao; Zhang, Shuijun; He, Yuting; Guo, Wenzhi

doi:10.3389/fimmu.2022.951561

Cited by 8 publications

(6 citation statements)

References 294 publications

(380 reference statements)

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“…CircRNAs regulate intercellular communication by modulating the release of signaling molecules from the ECM. 14 By acting on the miR-488-3p/CD39 axis, exosomal circTMEM181, secreted by HCC cells, enhances the role of CD39 in macrophages. Notably, CD73 was revealed to be upregulated in HCC cells.…”

Section: Ecm Reorganizationmentioning

confidence: 99%

Crosstalk Between circRNA and Tumor Microenvironment of Hepatocellular Carcinoma: Mechanism, Function and Applications

Xie,

Hao,

Yuan

et al. 2024

OTT

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Hepatocellular carcinoma (HCC) is one of the most common aggressive tumors in the world. Despite the availability of various treatments, its prognosis remains poor due to the lack of specific diagnostic indicators and the high heterogeneity of HCC cases. CircRNAs are noncoding RNAs with stable and highly specific expression. Extensive research evidence suggests that circRNAs mediate the pathogenesis and progression of HCC through acting as miRNA sponges, protein modulators, and translation templates. Tumor microenvironment (TME) has become a hotspot of immune-related research in recent years due to its effects on metabolism, secretion and immunity of HCC. Accordingly, understanding the role played by circRNAs in TME is important for the study of HCC. This review will discuss the crosstalk between circRNAs and TME in HCC. In addition, we will discuss the current deficiencies and controversies in research on circRNAs and predict future research directions.

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Section: Ecm Reorganizationmentioning

confidence: 99%

Crosstalk Between circRNA and Tumor Microenvironment of Hepatocellular Carcinoma: Mechanism, Function and Applications

Xie,

Hao,

Yuan

et al. 2024

OTT

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“…CircRNAs are a class of circular ncRNAs that are more stable than linear RNAs and are rich in miRNA binding sites 94 . CircRNAs, therefore, act mainly as miRNA sponges, and in addition, circRNAs can regulate the translation and post-translational modifications of many proteins 95 , 96 .…”

Section: Ncrnas To Methylationmentioning

confidence: 99%

The bidirectional interplay between ncRNAs and methylation modifications in gastrointestinal tumors

Kong,

Yu,

Guo

et al. 2023

Int. J. Biol. Sci.

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The aberrant expression of methylation and ncRNAs, two crucial regulators of epigenetic modifications, has been widely demonstrated in cancer. The complex interplay between them is essential in promoting malignant phenotype, poor prognosis, and drug resistance in GI tumors (including esophageal, gastric, colorectal, liver, and pancreatic cancers). Therefore, we summarize the interrelation process between ncRNAs and methylation modifications in GI tumors, including the detailed mechanism of methylation enzyme regulation of ncRNAs, the molecular mechanism of ncRNAs regulation of methylation modifications, and the correlation between the interactions between ncRNAs and methylation modifications and clinical features of tumors. Finally, we discuss the potential value of ncRNAs and methylation modifications in clinical diagnosis and therapy.

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“…Circular RNAs (circRNAs) are characterized by covalent closure [1]. They are strongly associated with biological processes [2] and diseases [3], [4] especially cancers [5].…”

Section: Introductionmentioning

confidence: 99%

An Encoding-Decoding Framework Based on CNN for circRNA-RBP Binding Sites Prediction

Guo,

Lei,

Pan

2024

Chinese J. Elect.

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Predicting RNA binding protein (RBP) binding sites on circular RNAs (circRNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network (CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network (TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

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Mechanism underlying circRNA dysregulation in the TME of digestive system cancer

Cited by 8 publications

References 294 publications

Crosstalk Between circRNA and Tumor Microenvironment of Hepatocellular Carcinoma: Mechanism, Function and Applications

Crosstalk Between circRNA and Tumor Microenvironment of Hepatocellular Carcinoma: Mechanism, Function and Applications

The bidirectional interplay between ncRNAs and methylation modifications in gastrointestinal tumors

An Encoding-Decoding Framework Based on CNN for circRNA-RBP Binding Sites Prediction

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