ArticleAging2023
Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment.
Article in Aging, 2023. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- Machine Learning and Weighted Gene Coexpression Network-Based Identification of Biomarkers Predicting Immune Profiling and Drug Resistance in Lung Adenocarcinoma.International journal of genomics · 2025Article
- Identification immune-related hub genes in diagnosing atherosclerosis with ischemic stroke through comprehensive bioinformatics analysis and machine learning.Frontiers in neurology · 2025Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is a malignancy with a very high mortality rate. Because of its high heterogeneity, there is an urgent need to find biomarkers that accurately predict prognosis. Epithelial-mesenchymal transition (EMT) is closely associated with frequent recurrence and high mortality of HCC. Therefore, it is necessary to comprehensively analyze the prognostic value and immunological properties of EMT gene in HCC. In our study, we performed bioinformatics analysis of the TCGA and ICGC liver cancer cohorts and identified the module genes of immune-associated EMTs (iEMT) by Weighted Gene Co-Expression Network Analysis (WGCNA). Further we used machine learning (support vector machines-recursive feature elimination and Lasso) to identify three central iEMT genes (ARMC9, ADAM15 and STC2) and construct iEMT_score. Subsequently, in the training and validation cohorts, it was demonstrated that the overall survival (OS) of patients in the high iEMT_score group was worse than that of patients in the low iEMT_score group. Based on this, we have constructed a nomogram that is easy for clinicians to use. In addition, our study explored differences in pathway enrichment, immunological properties, and sensitivity to common chemotherapy and targeted drugs in different subgroups of iEMT_score. Finally, we showed through
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Registered trials
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