Li MF, Du SN, Bao PT, Li YG. Multidimensional integration: A novel breakthrough in prognostic prediction for immunochemotherapy in human epidermal growth factor receptor-2-negative advanced gastric cancer. World J Gastrointest Oncol 2026; 18(9): 118614 [DOI: 10.4251/wjgo.118614]
Corresponding Author of This Article
Yu-Geng Li, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), No. 4 Renmin Road, Shibei District, Qingdao 266033, Shandong Province, China. qq252656554@163.com
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Oncology
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review-article
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Li MF, Du SN, Bao PT, Li YG. Multidimensional integration: A novel breakthrough in prognostic prediction for immunochemotherapy in human epidermal growth factor receptor-2-negative advanced gastric cancer. World J Gastrointest Oncol 2026; 18(9): 118614 [DOI: 10.4251/wjgo.118614]
World J Gastrointest Oncol. Sep 15, 2026; 18(9): 118614 Published online Sep 15, 2026. doi: 10.4251/wjgo.118614
Multidimensional integration: A novel breakthrough in prognostic prediction for immunochemotherapy in human epidermal growth factor receptor-2-negative advanced gastric cancer
Meng-Fan Li, Song-Nan Du, Peng-Tao Bao, Yu-Geng Li
Meng-Fan Li, Graduate School, Hebei North University, The Eighth Medical Center of People’s Liberation Army General Hospital, Beijing 100019, China
Song-Nan Du, Department of Radiation Oncology, the Fifth Medical Center of Chinese PLA General Hospital, Beijing 100019, China
Peng-Tao Bao, Department of Respiratory and Critical Care Medicine, The Eighth Medical Center of People’s Liberation Army General Hospital, Beijing 100019, China
Yu-Geng Li, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), Qingdao 266033, Shandong Province, China
Co-first authors: Meng-Fan Li and Song-Nan Du.
Author contributions: Li MF and Du SN drafted the initial manuscript, and they contributed equally to this manuscript as co-first authors; Bao PT contributed to clinical data acquisition; Li YG supervised the overall study, reviewed and edited the manuscript, and approved the final version. All authors read and approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yu-Geng Li, Qingdao Hiser Hospital Affiliated of Qingdao University (Qingdao Traditional Chinese Medicine Hospital), No. 4 Renmin Road, Shibei District, Qingdao 266033, Shandong Province, China. qq252656554@163.com
Received: January 7, 2026 Revised: January 31, 2026 Accepted: March 6, 2026 Published online: September 15, 2026 Processing time: 231 Days and 0.2 Hours
Abstract
The combination of programmed cell death-1 inhibitors with chemotherapy has significantly improved clinical outcomes in patients with human epidermal growth factor receptor-2-negative advanced gastric cancer. However, substantial interindividual heterogeneity in treatment response limits the precision of clinical decision-making. Recent efforts have focused on developing prognostic models that integrate multidimensional data, including molecular biomarkers, clinicopathological characteristics, and systemic inflammatory-nutritional indices. Among these, nomogram-based models have shown promise in improving individualized risk stratification. This review summarizes current advances in immunochemotherapy for advanced gastric cancer, highlights the emerging role of multidimensional prognostic modeling, discusses existing controversies and limitations, and provides perspectives on future directions, including the integration of liquid biopsy and artificial intelligence. A more comprehensive and dynamic predictive framework is essential to optimize treatment selection and improve patient outcomes.
Core Tip: Multidimensional prognostic models integrating molecular, clinical, and host-related factors represent a promising approach for predicting outcomes in human epidermal growth factor receptor-2 negative advanced gastric cancer undergoing immunochemotherapy. Despite encouraging progress, challenges remain regarding model generalizability, biomarker standardization, and clinical applicability. Future integration of multi-omics data and artificial intelligence may enable more precise and dynamic prediction systems.
