ArticleCancer cell international2025
Establishment and characterization of a new mouse gastric carcinoma cell line, MCC.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Animal models and pathogenesis of gastric cancer: from premalignant conditions-to-metastasis.Cancer biology & medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundThe aim of this study was to establish a primary mouse gastric carcinoma cell line.
methodsGastric adenocarcinoma in the body region was induced in immunocompetent BALB/c mice using N-Methyl-N-nitrosourea and a 2% NaCl solution. Fresh gastric cancer tissue samples were cultured in 1640 medium supplemented with 10% fetal bovine serum for primary culture and subculture. Cellular morphology was assessed via light microscopy, and a cell growth curve was established. Genomic and proteomic analyses were conducted to characterize the molecular features of the cell lines. This cell line demonstrated a 100% success rate in forming subcutaneous tumors in BALB/c mice. By integrating proteomic profiles from clinical gastric cancer patients and the murine subcutaneous tumor model, several molecular targets suitable for preclinical investigation were identified. Trametinib, a MEK inhibitor, was employed as a model compound in our preclinical study.
resultsA novel gastric carcinoma cell line, designated MCC, was established from BALB/c mice. This cell line exhibited a doubling time of approximately 33 h. Genomic and proteomic analyses identified mutations frequently observed in clinical gastric cancer patients, such as Kras, Egfr, and Ccnd3. Additionally, MCC overexpresses proteins, including SLC1A5, MCM6, and ITGA2, which are significantly upregulated in gastric cancer tissues compared to adjacent non-cancerous tissues. The MCC cell line demonstrated stable tumorigenicity in immunocompetent BALB/c mice, forming subcutaneous tumors that closely resemble the proteomic profile of clinical gastric cancer samples. This high concordance facilitated the identification of several potential therapeutic targets for gastric cancer. Preclinical studies with trametinib revealed that treatment effectively inhibited gastric cancer growth, likely mediated through the activation of immune cells, particularly neutrophils and T cells.
conclusionsThe MCC cell line serves as an indispensable model for gastric cancer research, offering a robust platform for investigating tumor development and progression. Its exceptional tumorigenic capacity and strong concordance with clinical proteomic profiles underscore its significance in translational research, facilitating the discovery of novel therapeutic targets and elucidation of molecular pathways critical for developing effective treatment strategies.
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.