Evidence map›Paper›PMID 37013003›Full record

ArticleComputational and structural biotechnology journal2023

Integration of pharmacoproteomic and computational approaches reveals the cellular signal transduction pathways affected by apatinib in gastric cancer cell lines.

Yosui Nojima, Masahiko Aoki, Suyong Re, Hidekazu Hirano, Yuichi Abe, Ryohei Narumi, Satoshi Muraoka, Hirokazu Shoji, Kazufumi Honda, Takeshi Tomonaga and 3 more

Open access · goldAbstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 23% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Yosui NojimaArtificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Masahiko AokiDepartment of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo 104-0045, Japan.
Suyong ReArtificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Hidekazu HiranoDepartment of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo 104-0045, Japan.
Yuichi AbeLaboratory of Proteome Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Ryohei NarumiLaboratory of Proteome Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Satoshi MuraokaLaboratory of Proteome Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Hirokazu ShojiDepartment of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo 104-0045, Japan.
Kazufumi HondaDepartment of Biomarkers for Early Detection of Cancer, National Cancer Center Research Institute, Tokyo 104-0045, Japan.
Takeshi TomonagaLaboratory of Proteome Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Kenji MizuguchiArtificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutes of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
Narikazu BokuDepartment of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo 104-0045, Japan.
Jun AdachiLaboratory of Proteome Research, National Institute of Biomedical Innovation, Health and Nutrition, Osaka 567-0085, Japan.
National Institute of Biomedical Innovation, Health and Nutrition · JPKyoto University Hospital · JPNippon Medical School · JPTokyo Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Apatinib is known to be a highly selective vascular endothelial growth factor receptor 2 (VEGFR2) inhibitor with anti-angiogenic and anti-tumor properties. In a phase III study, the objective response rate to apatinib was low. It remains unclear why the effectivity of apatinib varies among patients and what type of patients are candidates for the treatment. In this study, we investigated the anti-tumor efficacy of apatinib against 13 gastric cancer cell lines and found that it differed depending on the cell line. Using integrated wet and dry approaches, we showed that apatinib was a multi-kinase inhibitor of c-Kit, RAF1, VEGFR1, VEGFR2, and VEGFR3, predominantly inhibiting c-Kit. Notably, KATO-III, which was the most apatinib-sensitive among the gastric cancer cell lines investigated, was the only cell line expressing c-Kit, RAF1, VEGFR1, and VEGFR3 but not VEGFR2. Furthermore, we identified SNW1 as a molecule affected by apatinib that plays an important role in cell survival. Finally, we identified the molecular network related to SNW1 that was affected by treatment with apatinib. These results suggest that the mechanism of action of apatinib in KATO-III cells is independent of VEGFR2 and that the differential efficacy of apatinib was due to differences in expression patterns of receptor tyrosine kinases. Furthermore, our results suggest that the differential efficacy of apatinib in gastric cell lines may be attributed to SNW1 phosphorylation levels at a steady state. These findings contribute to a deeper understanding of the mechanism of action of apatinib in gastric cancer cells.

Indexed as

ApatinibBioinformaticsComputational biologyGastric cancerProteomicsRivoceranib

Identifiers

PMID37013003
PMCPMC10066531
OpenAlexW4324135355

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.