circRNA circ_POLA2 increases microRNA‑31 methylation to promote endometrial cancer cell proliferation

Xia, Fang; Wang, Jinhua; Chen, Lingying; Zhang, Xiaochun

doi:10.3892/ol.2021.13023

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…Cir_POLA2 has been reported as an oncogene of lung cancer. 19 It has been found that overexpression of Cir_POLA2 can promote the proliferation of acute myeloid leukemia cells. 20 Circ_POLA2 may upregulate the G protein subunit beta 1 (Notch pathway-related molecules) by serving as an endogenous competing RNA for miR-326.14.…”

Section: Discussionmentioning

confidence: 99%

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms

Zhou¹,

Cao²,

Xie³

2023

Turk J Gastroenterol

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Background/Aims: Hepatocellular carcinoma, a highly malignant tumor, is difficult to diagnose, treat, and predict the prognosis. Notch signaling pathway can affect hepatocellular carcinoma. We aimed to predict the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms. Materials and Methods: We downloaded hepatocellular carcinoma data from the Cancer Genome Atlas and Gene Expression Omnibus databases and used machine learning methods to screen the hub Notch signal-related genes. Machine learning classification was used to construct a prediction model for the classification and diagnosis of hepatocellular carcinoma cancer. Bioinformatics methods were applied to explore the expression of these hub genes in the hepatocellular carcinoma tumor immune microenvironment. Results: We identified 4 hub genes, namely, LAMA4, POLA2, RAD51, and TYMS, which were used as the final variables, and found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of hepatocellular carcinoma. The area under curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500, and 0.932; respectively. The area under curves were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489, and 0.926. The area under curve in the external validation set was 0.934. Immune cell infiltration was related to the expression of 4 hub genes. Patients in the low-risk group of hepatocellular carcinoma were more likely to have an immune escape. Conclusion: TheNotch signaling pathway was closely related to the occurrence and development of hepatocellular carcinoma. The hepatocellular carcinoma classification and diagnosis model established based on this had a high degree of reliability and stability.

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Section: Discussionmentioning

confidence: 99%

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms

Zhou¹,

Cao²,

Xie³

2023

Turk J Gastroenterol

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“…It has been reported that LAMA4 could affect the level of Notch ligand and its receptor by regulating TGF-β1 [18], thereby inducing the expression of some key proteins related to the occurrence and development of HCC. Cir_POLA2 has been reported as an oncogene of lung cancer [19]. It has been found that overexpression of Cir_POLA2 can promote the proliferation of acute myeloid leukemia (AML) cells [20].…”

Section: Discussionmentioning

confidence: 99%

Research on predicting the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms

Zhou

Cao

Xie

2022

Preprint

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Background:Hepatocellular carcinoma (HCC) is a highly malignant malignant tumor, and is difficult to diagnose, treat and predict the prognosis. Notch signaling pathway can affect HCC. Therefore, our paper aimed to explore the prediction of the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms.Methods:In our presented study, we downloaded HCC data from TCGA and GEO databases. Firstly, we used machine learning methods to screen the hub Notch signal-related genes. Then, machine learning classification was used to construct a prediction model for the classification and diagnosis of HCC cancer. In our presented study, we also used bioinformatics methods to explore the relationship between the expression of these hub genes in the HCC tumor immune microenvironment to further improve the reliablity of the model.Results:After screening, we identified four hub genes of LAMA4, POLA2, RAD51 and TYMS, which were used as the final variables to construct the model. It was found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of HCC. The area under curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500 and 0.932; respectively. The AUC, accuracy, sensitivity, specificity, PPV, NPV and F1 score in the testing set were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489 and 0.926. The AUC in the external validation set was 0.934. In the immune microenvironment, immune cell infiltration played an important role in HCC and was related to the expression of 4 hub genes. Further researches found that patients in the low-risk group of HCC were more likely to have immune escape.Conclusion: Notch signaling pathway were closely related to the occurrence and development of HCC. The HCC classification and diagnosis model established based on this had a high degree of reliability and stability.

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“…Proliferation, invasion and metastasis are the basic features of malignant tumors, involving a series of complex multi-step interactions among tumor cells and the host (70). For example, bladder cancer, gastric cancer (47,48) and EC all have the potential of proliferation, invasion and metastasis (70,71).…”

Section: Circrnas Regulate Cell Proliferation and Motility In Ecmentioning

confidence: 99%

“…At present, several studies have demonstrated that circRNAs adsorb miRNAs through numerous pathways and fulfill a role in EC development (28,29,71). For example, circIFT80 [circBase ID (http://www.circbase.org/), hsa_circ_0067835], circWHSC1, circSLC6A6, circATP2C1 (circBase ID, hsa_circ_0005797) and circPOLA2 participate in the invasion, proliferation and metastasis of EC through the adsorption of miR-324-5p (71), miR-646 (28), miR-497-5p (29), miR-298 (30) and miR-31 (70), respectively. Notably, it has been reported that activation of these pathways depends on exogenous amino acids for energy (72).…”

Section: Circrnas Regulate Cell Proliferation and Motility In Ecmentioning

confidence: 99%

Circular RNAs in endometrial carcinoma (Review)

et al. 2022

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As one of the leading causes of death in women in Western developed countries, endometrial carcinoma (EC) is a common gynecological malignant tumor that seriously threatens women's health. In recent years, a trend has emerged of EC being manifested in younger women, and its overall incidence is gradually rising. Circular RNAs (circRNAs) are novel endogenous transcripts that have limited ability to encode proteins due to their covalent closed-loop structure, which differs from that of other types of RNA. A growing body of evidence has demonstrated that circRNAs fulfill an important role in lung cancer, gastric cancer, breast cancer, EC and other malignant tumor types, and they can affect the occurrence and development of these malignancies through a variety of pathways, further demonstrating the potential of circRNAs as molecular biomarkers for the diagnosis, treatment and prognosis of malignant tumors. The purpose of the present review is to summarize the current understanding of the biogenesis and effects of circRNAs, and to discuss the expression, function and underlying mechanism of circRNAs in EC in order to identify potential novel biomarkers.

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circRNA circ_POLA2 increases microRNA‑31 methylation to promote endometrial cancer cell proliferation

Cited by 6 publications

References 21 publications

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms

Research on predicting the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms

Circular RNAs in endometrial carcinoma (Review)

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