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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastrointest Oncol. May 15, 2026; 18(5): 116411
Published online May 15, 2026. doi: 10.4251/wjgo.v18.i5.116411
Letter to the Editor: Integrating immune-related metrics with personalize first-line immunochemotherapy in human epidermal growth factor receptor 2-negative advanced gastric cancer
Yu-Han Yang
Yu-Han Yang, West China School of Medicine, Sichuan University, Chengdu 6100041, Sichuan Province, China
Author contributions: Yang YH was responsible for conceptualization, writing original draft preparation, writing review and editing, read and approved the final manuscript and agreed to be accountable for all aspects of the work.
AI contribution statement: There have been no use of AI tools for the manuscript.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Yu-Han Yang, Assistant Professor, West China School of Medicine, Sichuan University, No. 17 People’s South Road, Chengdu 6100041, Sichuan Province, China. yyh_1023@163.com
Received: November 11, 2025
Revised: December 22, 2025
Accepted: January 9, 2026
Published online: May 15, 2026
Processing time: 184 Days and 13 Hours
Core Tip

Core Tip: This study developed and validated a novel survival prediction model for advanced human epidermal growth factor receptor 2 negative gastric cancer patients receiving immunotherapy combined with chemotherapy. A retrospective analysis of 200 patients identified programmed death ligand 1 expression, microsatellite instability, tumor-node-metastasis stage, tumor differentiation, neutrophil-to-lymphocyte ratio, and C-reactive protein-albumin-lymphocyte index as independent prognostic factors. Based on these factors, nomogram models for progression-free survival and overall survival were constructed and validated using the concordance index and area under the curve. The results showed good discrimination ability in both the training and validation sets, indicating that the model effectively predicts patient survival outcomes and provides strong support for personalized treatment decisions.

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