ArticleLiver international : official journal of the International Association for the Study of the Liver2018
A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings.
Article in Liver international : official journal of the International Association for the Study of the Liver, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.BMC biology · 2026Article
- Applications of gene pair methods in clinical research: advancing precision medicine.Molecular biomedicine · 2025Review
- A pyroptosis-related lncRNA-based prognostic index for hepatocellular carcinoma by relative expression orderings.Translational cancer research · 2024Article
- Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Scientific reports · 2024Article
- Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma.Biochemistry and biophysics reports · 2024Article
- Clinical data mining: challenges, opportunities, and recommendations for translational applications.Journal of translational medicine · 2024Review
- Prognosis and Personalized Treatment Prediction in Different Mutation-Signature Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2023Article
- Metabolism-Related Gene Pairs to Predict the Clinical Outcome and Molecular Characteristics of Early Hepatocellular Carcinoma.Cancers · 2022Article
- A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma.Cancer cell international · 2022Article
- lncRNA pair as candidate diagnostic signature for colorectal cancer based on the within-sample relative expression levels.Frontiers in oncology · 2022Article
- Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning.iScience · 2021Article
- A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy.The Prostate · 2020Article
- A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions.Frontiers in genetics · 2020Article
- Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.Frontiers in cell and developmental biology · 2020Article
- Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method.Frontiers in bioengineering and biotechnology · 2020Article
- Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer.Frontiers in molecular biosciences · 2020Article
- A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer.BMC genomics · 2019Article
- A qualitative transcriptional signature for the early diagnosis of colorectal cancer.Cancer science · 2019Article
- Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma.EBioMedicine · 2019Article
- A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer.Journal of translational medicine · 2019Article
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11 authors.
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Abstract
BACKGROUND &
aimsCurrently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate.
methodsBased on the within-sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non-tumour tissues from cirrhosis tissues of non-hepatocellular carcinoma patients.
resultsA signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non-hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non-hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long-lasting cirrhosis could have already gained hepatocellular carcinoma characteristics.
conclusionsThe signature can distinguish both hepatocellular carcinoma tissues and tumour-adjacent tissues from cirrhosis tissues of non-hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early diagnosis of hepatocellular carcinoma.
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