ArticleFrontiers in genetics2019
Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 58 citations in OpenAlex.
- Comprehensive review of publicly accessible databases for gastrointestinal cancer and future challenges in database construction.Discover oncology · 2026Review
- CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.GigaScience · 2026Article
- GULP1 as a novel diagnostic and predictive biomarker in hepatocellular carcinoma.Clinical and molecular hepatology · 2025Article
- Multi-Omics Integration of Lactylation- and PANoptosis-Based Signatures in Lung Adenocarcinoma: Prognostic Stratification and Immune Response.International journal of molecular sciences · 2025Article
- Predicting hepatocellular carcinoma survival with artificial intelligence.Scientific reports · 2025Article
- Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.PloS one · 2025Article
- Efect of N-acetylcysteine on HepG2 cells which were induced into fatty liver cells.Journal of molecular histology · 2024Article
- Review
- Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning.Frontiers in oncology · 2024Article
- DecipheringEpigenomics · 2024Article
- Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making.Scientific reports · 2023Article
- Components of the JNK-MAPK pathway play distinct roles in hepatocellular carcinoma.Journal of cancer research and clinical oncology · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Review
- In silico transcriptional analysis of asymptomatic and severe COVID-19 patients reveals the susceptibility of severe patients to other comorbidities and non-viral pathological conditions.Human gene (Amsterdam, Netherlands) · 2023Article
- FCN3 inhibits the progression of hepatocellular carcinoma by suppressing SBDS-mediated blockade of the p53 pathway.International journal of biological sciences · 2023Article
- ELL associated factor 2 is a potential diagnostic and prognostic indicator: evidence from the in silico and in vitro experiments.American journal of cancer research · 2023Article
- Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma.BMC bioinformatics · 2022Article
- Hepatocellular Carcinoma Intrinsic Cell Death Regulates Immune Response and Prognosis.Frontiers in oncology · 2022Review
- Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.Translational gastroenterology and hepatology · 2022Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (
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