ArticleBioMed research international2020
Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients.Frontiers in bioengineering and biotechnology · 2020Pooled it
- A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma.BMC gastroenterology · 2022Article
- Landscape of B lymphocytes and plasma cells in digestive tract carcinomas.Annals of gastroenterologyReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric cancer (GC) has been divided into four molecular subtypes, of which the mesenchymal subtype has the poorest survival. Our goal is to develop a prognostic signature by integrating the immune system and molecular modalities involved in the mesenchymal subtype.
methodsThe gene expression profiles collected from 6 public datasets were applied to this study, including 1,221 samples totally. Network analysis was applied to integrate the mesenchymal modalities and immune signature to establish an immune-based prognostic signature for GC (IPSGC).
resultsWe identified six immune genes as key factors of the mesenchymal subtype and established the IPSGC. The IPSGC can significantly divide patients into high- and low-risk groups in terms of overall survival (OS) and relapse-free survival (RFS) in discovery (OS:
conclusionsWe propose an immune-based signature identified by network analysis, which is a promising prognostic biomarker and help for the selection of GC patients who might benefit from more rigorous therapies. Further prospective studies are warranted to test and validate its efficiency for clinical application.
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Registered trials
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