ReviewGene expression2020
Inside-Out or Outside-In: Choosing the Right Model of Hepatocellular Cancer.
Review in Gene expression, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed, 11 citations in OpenAlex.
- Precision models in hepatocellular carcinoma.Nature reviews. Gastroenterology & hepatology · 2025Review
- Preclinical Models of Hepatocellular Carcinoma: Current Utility, Limitations, and Challenges.Biomedicines · 2024Review
- MYC and MET cooperatively drive hepatocellular carcinoma with distinct molecular traits and vulnerabilities.Cell death & disease · 2022Article
- Liver-specific deletion of miR-181ab1 reduces liver tumour progression via upregulation of CBX7.Cellular and molecular life sciences : CMLS · 2022Article
- Scaffolding Protein IQGAP1 Is Dispensable, but Its Overexpression Promotes Hepatocellular Carcinoma via YAP1 Signaling.Molecular and cellular biology · 2021Article
- Exploring the Impact of the β-Catenin Mutations in Hepatocellular Carcinoma: An In-Depth Review.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The incidence of hepatocellular cancer (HCC) is gradually rising. HCC occurs as a sequela to various chronic liver diseases and ensuing cirrhosis. There have been many therapies approved for unresectable HCC in the last 5 years, including immune checkpoint inhibitors, and the overall response rates have improved. However, there are many cases that do not respond, and personalized medicine is lacking, making HCC an unmet clinical need. Generation of appropriate animal models have been key to our understanding of HCC. Based on the overall concept of hepatocarcinogenesis, two major categories of animal models are discussed herein that can be useful to address specific questions. One category is described as the outside-in model of HCC and is based on the premise that it takes decades of hepatocyte injury, death, wound healing, and regeneration to eventually lead to DNA damage and mutations in a hepatocyte, which initiates tumorigenesis. Several animal models have been generated, which attempt to recapitulate this complex tissue damage and cellular interplay through genetics, diets, and toxins. The second category is the inside-out model of HCC, where clinically relevant genes can be coexpressed in a small subset of hepatocytes to yield a tumor, which matches HCC subsets in gene expression. This model has been made possible in part by the widely available molecular characterization of HCC, and in part by modalities like sleeping beauty transposon/transposase, Crispr/Cas9, and hydrodynamic tail vein injection. These two categories of HCC have distinct pros and cons, which are discussed in this Thinking Out Loud article.
Indexed as
Identifiers
What 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.