ArticleFrontiers in oncology2020
Tumor-Infiltrating Lymphocyte-Based Risk Score for Predicting Prognosis in Gastric Cancer.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Sex-related disparities in outcomes of cholangiocarcinoma patients in treatment trials.Frontiers in oncology · 2022Pooled it
- OX40L and IL-2 combination strategy for gastric cancer immunotherapy.Translational cancer research · 2025Article
- Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2023Article
- Adverse effects of low serum lipoprotein cholesterol on the immune microenvironment in gastric cancer: a case‒control study.Lipids in health and disease · 2022Article
- Prognostic value of nicotinamide adenine dinucleotide (NADJournal of gastrointestinal oncology · 2022Article
- Prognostic and Immunological Value of GNB4 in Gastric Cancer by Analyzing TCGA Database.Disease markers · 2022Article
- Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.Frontiers in immunology · 2022Article
- A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma.PeerJ · 2021Article
- LAMB1 Is Related to the T Stage and Indicates Poor Prognosis in Gastric Cancer.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor-infiltrating lymphocytes (TILs) in gastric cancer are closely related to clinical prognosis; however, little is known regarding the immune microenvironment in this disease. Thus, RNA-sequencing data from gastric cancer patients were downloaded from the Gene Expression Omnibus (GEO). The proportion of immune cells was determined based on a deconvolution algorithm (CIBERSORT), and gene expression profiles were analyzed in the context of clinical outcomes to construct an immune risk score. Data were analyzed using least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression, to identify prognostic markers of gastric cancer survival. The model included four immune cell types: neutrophils, plasma cells, activated CD4
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