ArticleHeliyon2023
Prediction of immune infiltration and prognosis for patients with gastric cancer based on the immune-related genes signature.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed.
- NR3C1 Modulates Wnt Signalling to Influence the Invasiveness and Immune Features of Nonfunctioning Invasive Pituitary Adenomas.Journal of cellular and molecular medicine · 2026Article
- Immune microenvironment and prognostic genes of triple-negative breast cancer in the context of transcriptome and single-cell sequencing.Scientific reports · 2025Article
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The immune microenvironment influenced clinical outcomes and treatment response of gastric cancer (GC) patients. Though thousands of immune-related genes (IRGs) have been identified, their effects on GC are not fully understood. The objective of the study is to analyze the correlations between the expression and effect of IRGs and clinical outcomes. Moreover, we evaluate the efficacy and value of utilizing the immune-related genes signature as a prognosis prediction model for GC patients. Methods: We identified the differentially expressed IRGs and systematically analyzed their functions. We constructed a novel GC prognostic signature and a new nomogram, Moreover, we explored the infiltrated immune cell types in the immune microenvironment and discussed the genetic variation in GC IRGs. Results: Eight IRGs, including CCL15, MSR1, GNAI1, NR3C1, ITGAV, NMB, AEN, and TGFBR1 were identified. Based on the prognostic signature, GC patients were distinguished into two subtype groups. As verified in multiple datasets, the prognostic signature exhibited good performance in predicting the prognosis (AUC = 0.803, Conclusions: In summary, we found that IRGs contributed to GC prognosis prediction and constructed an IRGs-based GC prognostic signature, which could serve as an effective prognostic stratification tool.
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Registered trials
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