ReviewCancer letters2024
Colorectal cancer murine models: Initiation to metastasis.
Review in Cancer letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Colorectal Cancer: Epidemiology, Risk Factors, Signaling Pathways, Clinical Features, Screening, Diagnosis, and Management.MedComm · 2026Review
- ZSGSD inhibits colorectal cancer via PI3K/AKT/mTOR pathway: pharmacological and experimental evidence.Hereditas · 2026Article
- Engineered Nanozymes for Colorectal Cancer Therapy: Catalytic Reprogramming of the Tumor Microenvironment.International journal of nanomedicine · 2026Review
- Aquatic-derived antimicrobial peptides and their strategically modified analogues as prospective anticancer therapeutics: a comprehensive systematic review of enhancement methodologies and mechanistic insights.Frontiers in chemistry · 2026Review
- Review
- Mucin 5AC Promotes Breast Cancer Brain Metastasis through cMET/CD44v6.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Exploring the cytotoxic effects of bioactive compounds from Alcea rosea against stem cell driven colon carcinogenesis.Scientific reports · 2025Article
- Recent advances in the development and application of colorectal cancer mouse models.Frontiers in pharmacology · 2025Review
- Organoid models: applications and research advances in colorectal cancer.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Despite significant advancements in prevention and treatment, colorectal cancer (CRC) remains the third leading cause of cancer-related deaths. Animal models, including xenografts, syngeneic, and genetically engineered, have emerged as indispensable tools in cancer research. These models offer a valuable platform to address critical questions regarding molecular pathogenesis and test therapeutic interventions before moving on to clinical trials. Advancements in CRC animal models have also facilitated the advent of personalized and precision medicine. Patient-derived xenografts and genetically engineered mice that mirror features of human tumors allow for tailoring treatments to specific CRC subtypes, improving treatment outcomes and quality of life. To overcome the limitations of individual model systems, recent studies have employed a multi-modal approach, combining different animal models, 3D organoids, and in vitro studies. This integrative approach provides a comprehensive understanding of CRC biology, including the tumor microenvironment and therapeutic responses, driving the development of more effective and personalized therapeutic interventions. This review discusses the animal models used for CRC research, including recent advancements and limitations of these animal models.
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.