ArticleTranslational cancer research2022
Novel insight on predicting prognosis of gastric cancer based on inflammation.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Gastric cancer-derived LBP promotes liver metastasis by driving intrahepatic fibrotic pre-metastatic niche formation.Journal of experimental & clinical cancer research : CR · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The tumor microenvironment (TME) and inflammation play vital roles in the development and progression of gastric cancer (GC). However, there are no inflammation-related models that can predict the prognosis and immunotherapy response of GC patients. We aimed to establish a prognostic model based on an inflammation-related gene (IRG) signature that can predict poor clinical outcomes in GC. Methods: We searched IRGs in The Cancer Genome Atlas (TCGA) database and identified genes differentially expressed in GC. The model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis and validated using Gene Expression Omnibus (GEO) database. Receiver operating characteristic (ROC) curve, principal component analysis (PCA), and t-distribution stochastic neighbor embedding (t-SNE) analysis were performed to evaluate model performance. Independent prognostic factor, immune infiltration, cancer stemness, immunotherapy response analysis and gene set enrichment analysis (GSEA) were performed for functional evaluation. Results: An inflammation-related risk model was established based on 8 genes ( Conclusions: We established and validated an inflammation-related prognostic model that predicts immune escape and patient prognosis in GC. Our model is expected to improve clinical outcomes by facilitating clinical decision making and the development of individualized treatments.
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