ArticleCancer biomarkers : section A of Disease markers2022
Machine learning algorithm and deep neural networks identified a novel subtype in hepatocellular carcinoma.
Article in Cancer biomarkers : section A of Disease markers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning.Pediatric rheumatology online journal · 2024Article
- Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.Biology · 2023Article
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Authors and funding
4 authors.
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Abstract
backgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors. Due to the lack of specific characteristics in the early stage of the disease, patients are usually diagnosed in the advanced stage of disease progression.
objectiveThis study used machine learning algorithms to identify key genes in the progression of hepatocellular carcinoma and constructed a prediction model to predict the survival risk of HCC patients.
methodsThe transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differential expression analysis and COX proportional-hazards model participated in the identification of survival-related genes. K-Means, Random forests, and LASSO regression are involved in identifying novel subtypes of HCC and screening key genes. The prediction model was constructed by deep neural networks (DNN), and Gene Set Enrichment Analysis (GSEA) reveals the metabolic pathways where key genes are located.
resultsTwo subtypes were identified with significantly different survival rates (p< 0.0001, AUC = 0.720) and 17 key genes associated with the subtypes. The accuracy rate of the deep neural network prediction model is greater than 93.3%. The GSEA analysis found that the survival-related genes were significantly enriched in hallmark gene sets in the MSigDB database.
conclusionsIn this study, we used machine learning algorithms to screen out 17 genes related to the survival risk of HCC patients, and trained a DNN model based on them to predict the survival risk of HCC patients. The genes that make up the model are all key genes that affect the formation and development of cancer.
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