ReviewGastroenterology and hepatology from bed to bench2020
Assessment of liver cancer biomarkers.
Review in Gastroenterology and hepatology from bed to bench, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Disulfidptosis-related genes and long non-coding RNAs define a prognostic model for liver cancer.Translational cancer research · 2026Article
- Evaluation of the effects of Theranekron and Sorafenib treatments on alpha-fetoprotein, heat shock protein 90 alpha and indoleamine 2,3 dioxygenase 1 enzyme levels in experimental hepatocellular carcinoma in rats.Molecular biology reports · 2025Article
- Identification of Causal Plasma Proteins in Hepatocellular Carcinoma via Two-Sample Mendelian Randomization and Integrative Transcriptomic‒Proteomic Analysis.Cancer research communications · 2025Article
- Evaluation of glypican‑3 in patients with hepatocellular carcinoma.Molecular and clinical oncology · 2025Article
- Current status and new directions for hepatocellular carcinoma diagnosis.Liver research (Beijing, China) · 2024Review
- Review
- Proliferating cell nuclear antigen clamp associated factor, a potential proto-oncogene with increased expression in malignant gastrointestinal tumors.World journal of gastrointestinal oncology · 2021Review
- Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer.Biomedicines · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver cancer is the third cause of cancer-related deaths in the world. It is primarily divides into two main types, namely hepatocellular carcinoma (HC) and cholangiocarcinoma (IC). Due to the increasing number of patients with liver cancer and the high mortality rate, early diagnosis of the disease can be helpful in treatment, but most patients are diagnosed atlate stages of HC. The aim of this study is to screen and provide an overview on candidate biomarkers related to primary liver cancer to introduce the critical ones. In this study, various biomarkers related to the diagnosis of primary liver cancer have been studied. Accordingly, biomarkers are divided into different groups as blood biomarkers classified as serum and plasma biomarkers, tissue biomarkers, microRNA biomarkers, proteomic biomarkers and altered genes. Previous researches have focused on liver cells and bile ducts, the surround cellular environment, how cells differentiate, and the types of genes expressed in liver cancer. Some even have focused on the origin of tumor cells and how they differentiate and develop. In all these studies, the expression of specific proteins and genes in liver cancer has been considered. Based on available sources, biomarkers can be considered as candidates to diagnose and prognosis of various types of primary liver cancer, from sources such as blood, tissue, mic-RNA, proteome and genes. However, more investigations are required to introduce a biomarker for precise detection of early liver cancer.
Indexed as
Identifiers
33585001PMC7881406W3119328881What OpenQuestion holds
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.