ArticleCurrent medicinal chemistry2024
Systematic Analysis of Tumor Stem Cell-related Gene Characteristics to Predict the PD-L1 Immunotherapy and Prognosis of Gastric Cancer.
Article in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis of tumor marker application in gastric cancer diagnosis from 2019 to 2024.Frontiers in medicine · 2025Pooled it
- An integrated bulk and single-cell transcriptomic analysis reveals stemness-driven immune regulation and therapeutic vulnerability in colorectal cancer.Journal of Cancer · 2026Article
- Mitochondrial Ribosomal Proteins and Cancer.Medicina (Kaunas, Lithuania) · 2025Review
- VPS35-Retromer: Multifunctional Roles in Various Biological Processes - A Focus on Neurodegenerative Diseases and Cancer.Journal of inflammation research · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
aimsWe aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).
backgroundTumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.
objectiveWe aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.
methodsWe downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram.
resultsEarly GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy.
conclusionWe developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.
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Registered trials
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