ArticleThe Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology2023
Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms.
Article in The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Network toxicology and single-cell transcriptomics nominate candidate pyrethroid-associated targets and pathways in clear cell renal cell carcinoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Article
- Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.PloS one · 2025Article
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Abstract
BACKGROUND/
aimsHepatocellular 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
methodsWe 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.
resultsWe 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.
conclusionThe Notch 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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