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Circular RNAs (circRNAs) play crucial roles in multiple cancers, including hepatocellular carcinoma (HCC). However, the effects and molecular mechanisms of circ_LRIG3 in HCC remain barely unknown. qRT-PCR assay was employed to detect the levels of circ_LRIG3, LRIG3, miR-449a and ring finger protein 38 (RNF38). RNase R assay and Actinomycin D assay were performed to analyze the characteristics of circ_LRIG3. Colony formation assay and MTT assay were used to evaluate cell proliferation. Flow cytometry analysis and transwell assay were adopted for cell apoptosis and metastasis, respectively. Western blot assay was carried out for the protein levels of Ki67, Snail, E-cadherin, RNF38, Smad2/3 and p-Smad2/3. Murine xenograft model assay was used to explore the role of circ_LRIG3 in vivo. circ_LRIG3 expression was upregulated in HCC tissues and cells. Knockdown of circ_LRIG3 suppressed proliferation, migration and invasion and facilitated cell apoptosis in HCC cells in vitro and blocked tumor growth of HCC in vivo. RNF38 overexpression reversed the effects of circ_LRIG3 knockdown on the malignant behaviors of HCC cells. Moreover, circ_LRIG3 could sponge miR-449a to positively modulate RNF38 expression in HCC cells. circ_LRIG3 knockdown inhibited the progression of HCC cells by sponging miR-449a. In addition, circ_LRIG3 silencing might inhibit the Smad2/3 pathway. circ_LRIG3 facilitated HCC progression by modulation of miR-449a/LRIG3 axis, which might provide a novel method for HCC therapy.

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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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