ArticleAmerican journal of translational research2020
Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma.
Article in American journal of translational research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Rab24 protein levels show dynamic changes in mouse tissues and human cancers.Cell and tissue research · 2026Article
- Review
- HSPB8 counteracts tumor activity of BRAF- and NRAS-mutant melanoma cells by modulation of RAS-prenylation and autophagy.Cell death & disease · 2022Article
- Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.Reviews in cardiovascular medicine · 2022Article
- Identification of autophagy-related risk signatures for the prognosis, diagnosis, and targeted therapy in cervical cancer.Cancer cell international · 2021Article
- Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis.Frontiers in genetics · 2021Article
- Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Immunoautophagy-Related Genes and Tumor Microenvironment.International journal of general medicine · 2021Article
- HighCancer diagnosis & prognosisArticle
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Authors and funding
13 authors.
Funding
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Abstract
objectiveThis study aimed to identify the novel prognostic gene signature based on autophagy-associated genes (ARGs) in hepatocellular carcinoma (HCC).
methodsThe RNA sequencing data and clinical information of HCC and normal tissues were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs and establish the prognostic prediction model. Kaplan-Meier and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the model. GSE14520 dataset (testing cohort) was used to validate the prognostic risk model in TCGA. A clinical nomogram was established to predict the survival rate of HCC patients.
resultsTotally 27 differentially expressed ARGs were identified. Three OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these three ARGs, a prognostic prediction model was constructed. HCC patients with high risk score present poorer prognosis than those with low risk score both in TCGA cohort (P=4.478e-04) and testing cohort (P=1.274e-03). Moreover, the risk score curve shows a well feasibility in predicting the patients' survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict survival rate in HCC patients.
conclusionsThe survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.
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33194067PMC7653605What OpenQuestion holds
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