Evidence map›Paper›PMID 36692601›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2023

Artificial intelligence-guided discovery of gastric cancer continuum.

Daniella Vo, Pradipta Ghosh, Debashis Sahoo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Review
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

3 authors.

Daniella VoDepartment of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0703, Leichtag Building 132, La Jolla, CA, 92093-0703, USA.
Pradipta Ghosh *Moores Cancer Center, University of California San Diego, La Jolla, USA.
Debashis Sahoo *Department of Pediatrics, University of California San Diego, 9500 Gilman Drive, MC 0703, Leichtag Building 132, La Jolla, CA, 92093-0703, USA. dsahoo@ucsd.edu.ORCID 0000-0003-2329-8228

Funding

Spatial Regulation of RGS and G Protein SignalingR01CA100768 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GHOSH, PRADIPTA · 2003 to 2019
$7.4M
Macrophage Polarization in Response to Infections and InflammationR01AI155696 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GHOSH, PRADIPTA, SAHOO, DEBASHIS · 2020 to 2024
$4.9M
Modulation of Macrophage Polarization by Heterotrimeric G proteins: Implications of Gastrointestinal InflammationR01AI141630 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Pradipta Ghosh · 2019 to 2026
$3.2M
Integrators of Metastatic PotentialR01CA238042 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GHOSH, PRADIPTA, LUKER, GARY D · 2019 to 2023
$2.3M
Graduate Training Program in BioinformaticsT32GM139790 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAFNA, VINEET, GAASTERLAND, THERESA · 2021 to 2025
$2.1M
Summer Student Research on the Application of Boolean AnalysisR01GM138385 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SAHOO, DEBASHIS · 2020 to 2023
$2.0M
Modulation of G proteins by Growth FactorsR01CA160911 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GHOSH, PRADIPTA · 2013 to 2017
$1.8M
Network-Based Novel Therapeutics in Colorectal CancersUG3TR002968 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DAS, SOUMITA, GHOSH, PRADIPTA · 2019 to 2021
$946k
NCATS NIH HHS UG3 TR002968NCI NIH HHS R01 CA100768NCI NIH HHS R01 CA160911NCI NIH HHS R01 CA238042NIAID NIH HHS R01 AI141630NIAID NIH HHS R01 AI155696NIGMS NIH HHS R01 GM138385NIGMS NIH HHS T32 GM139790
6 · The paper itself

Abstract

backgroundDetailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.

methodsWe built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.

resultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.

conclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

Indexed as

Gastritis, AtrophicHelicobacter InfectionsIntestinal NeoplasmsPrecancerous ConditionsStomach NeoplasmsAnimalsArtificial IntelligenceGastric MucosaHumansMetaplasiaMiceComputational biologyMachine learningStomach neoplasmsSystems biologyTranscriptome

Identifiers

PMID36692601
PMCPMC9871434

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.