Title:Systematic Analysis of Tumor Stem Cell-related Gene Characteristics
to Predict the PD-L1 Immunotherapy and Prognosis of Gastric
Cancer
Volume: 31
Issue: 17
关键词:
胃癌，干性，预后，PD-L1免疫治疗，单细胞RNA测序，肿瘤。
摘要:
Aims: We aimed to develop a prognostic model with stemness-correlated
genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).
Background: Tumor 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.
Objective: We aimed to develop a prognostic model with stemness-correlated genes to
evaluate prognosis and immunotherapy responsiveness in GC.
Methods: We 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.
Results: Early 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.
Conclusion: We 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.
