ArticleBMC gastroenterology2022
Metabolism reprogramming signature associated with stromal cells abundance in tumor microenvironment improve prognostic risk classification for gastric cancer.
Article in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 3 citations in OpenAlex.
- Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC.Discover oncology · 2025Article
- GC‑MSC‑derived circ_0024107 promotes gastric cancer cell lymphatic metastasis via fatty acid oxidation metabolic reprogramming mediated by the miR‑5572/6855‑5p/CPT1A axis.Oncology reports · 2023Article
- Helicobacter pylori promotes gastric intestinal metaplasia through activation of IRF3-mediated kynurenine pathway.Cell communication and signaling : CCS · 2023Article
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3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundStromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).
methodsMetabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.
resultsA risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients' risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.
conclusionOur study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.
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