ArticleFrontiers in genetics2020
Identification of Gene Signatures for Diagnosis and Prognosis of Hepatocellular Carcinomas Patients at Early Stage.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 19 citations in OpenAlex.
- Development of a Novel four-gene Model for Monitoring the Progression from Metabolic Dysfunction-associated Steatotic Liver Disease to Hepatocellular Carcinoma in Males.Journal of Cancer · 2025Article
- Rank Aggregation Methods and Tools in Genomic Data Analysis.Current genomics · 2025Review
- Development of a novel diagnostic model to monitor the progression of metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma in females.Discover oncology · 2024Article
- The Potential Role of Circulating Long Miscellaneous RNAs in the Diagnosis and Prognosis of Hepatitis C Related Hepatocellular Carcinoma.Non-coding RNA · 2023Article
- A novel NHEJ gene signature based model for risk stratification and prognosis prediction in hepatocellular carcinoma.Cancer cell international · 2023Article
- Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis.MethodsX · 2023Article
- ATG101-related signature predicts prognosis and therapeutic option in hepatocellular carcinoma.Scientific reports · 2022Article
- Bioinformatic Analysis and In Vitro and In Vivo Experiments Reveal That Fibrillarin Participates in the Promotion of Lung Metastasis in Hepatocellular Carcinoma.Bioengineering (Basel, Switzerland) · 2022Article
- Identification and Validation of a Novel Immune Infiltration-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma by Machine-Learning Strategies.Gastroenterology research and practice · 2022Article
- Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma.Frontiers in cell and developmental biology · 2022Article
- Serine Protease Inhibitor Kazal Type 1, A Potential Biomarker for the Early Detection, Targeting, and Prediction of Response to Immune Checkpoint Blockade Therapies in Hepatocellular Carcinoma.Frontiers in immunology · 2022Article
- Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Hepatocellular Carcinoma.Frontiers in molecular biosciences · 2021Article
- Establishment of a Prognostic Model for Hepatocellular Carcinoma Based on Endoplasmic Reticulum Stress-Related Gene Analysis.Frontiers in oncology · 2021Article
- Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms.Frontiers in genetics · 2021Article
- Analysis and Validation of Hub Genes in Blood Monocytes of Postmenopausal Osteoporosis Patients.Frontiers in endocrinology · 2021Article
- The Expression and Function of Circadian Rhythm Genes in Hepatocellular Carcinoma.Oxidative medicine and cellular longevity · 2021Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The onset of liver cancer is insidious. Currently, there is no effective method for the early detection of hepatocellular carcinoma (HCC). Transcriptomic profiles of 826 tissue samples from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), Genotype tissue expression (GTEx), and International Cancer Genome Consortium (ICGC) databases were utilized to establish models for early detection and surveillance of HCC. The overlapping differentially expressed genes (DEGs) were screened by elastic net and robust rank aggregation (RRA) analyses to construct the diagnostic prediction model for early HCC (DP.eHCC). Prognostic prediction genes were screened by univariate cox regression and lasso cox regression analyses to construct the survival risk prediction model for early HCC (SP.eHCC). The relationship between the variation of transcriptome profile and the oncogenic risk-score of early HCC was analyzed by combining Weighted Correlation Network Analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), and genome networks (GeNets). The results showed that the AUC of DP.eHCC model for the diagnosis of early HCC was 0.956 (95% CI: 0.941-0.972;
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.