Go to The Journal of Clinical Investigation
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact
  • Physician-Scientist Development
  • Current issue
  • Past issues
  • By specialty
    • COVID-19
    • Cardiology
    • Immunology
    • Metabolism
    • Nephrology
    • Oncology
    • Pulmonology
    • All ...
  • Videos
  • Collections
    • In-Press Preview
    • Resource and Technical Advances
    • Clinical Research and Public Health
    • Research Letters
    • Editorials
    • Perspectives
    • Physician-Scientist Development
    • Reviews
    • Top read articles

  • Current issue
  • Past issues
  • Specialties
  • In-Press Preview
  • Resource and Technical Advances
  • Clinical Research and Public Health
  • Research Letters
  • Editorials
  • Perspectives
  • Physician-Scientist Development
  • Reviews
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Publication ethics
  • Publication alerts by email
  • Transfers
  • Advertising
  • Job board
  • Contact

Usage Information

Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit
Bailiang Li, Yuming Jiang, Guoxin Li, George A. Fisher Jr., Ruijiang Li
Bailiang Li, Yuming Jiang, Guoxin Li, George A. Fisher Jr., Ruijiang Li
View: Text | PDF
Clinical Research and Public Health Oncology

Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit

  • Text
  • PDF
Abstract

BACKGROUND Specific features of the tumor microenvironment (TME) may provide useful prognostic information. We conducted a systematic investigation of the cellular composition and prognostic landscape of the TME in gastric cancer.METHODS We evaluated the prognostic significance of major stromal and immune cells within the TME. We proposed a composite TME-based risk score and tested it in 6 independent cohorts of 1678 patients with gene expression or IHC measurements. Further, we devised a patient classification system based on TME characteristics.RESULTS We identified NK cells, fibroblasts, and endothelial cells as the most robust prognostic markers. The TME risk score combining these cell types was an independent prognostic factor when adjusted for clinicopathologic variables (gene expression, HR [95% CI], 1.42 [1.22–1.66]; IHC, 1.34 [1.24–1.45], P < 0.0001). Higher TME risk scores consistently associated with worse survival within every pathologic stage (HR range, 2.18–3.11, P < 0.02) and among patients who received surgery only. The TME risk score provided additional prognostic value beyond stage, and combination of the two improved prognostication accuracy (likelihood-ratio test χ2 = 235.4 vs. 187.6, P < 0.0001; net reclassification index, 23%). The TME risk score can predict the survival benefit of adjuvant chemotherapy in nonmetastatic patients (stage I–III) (interaction test, P < 0.02). Patients were divided into 4 TME subtypes that demonstrated distinct genetic and molecular patterns and complemented established genomic and molecular subtypes.CONCLUSION We developed and validated a TME-based risk score as an independent prognostic and predictive factor, which has the potential to guide personalized management of gastric cancer.FUNDING This project is partially supported by NIH grant 1R01 CA222512.

Authors

Bailiang Li, Yuming Jiang, Guoxin Li, George A. Fisher Jr., Ruijiang Li

×

Usage data is cumulative from December 2024 through December 2025.

Usage JCI PMC
Text version 532 89
PDF 88 35
Figure 293 8
Table 117 0
Supplemental data 100 4
Citation downloads 95 0
Totals 1,225 136
Total Views 1,361
(Click and drag on plot area to zoom in. Click legend items above to toggle)

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

Advertisement

Copyright © 2025 American Society for Clinical Investigation
ISSN 2379-3708

Sign up for email alerts