Published online Aug 15, 2025. doi: 10.4251/wjgo.v17.i8.105099
Revised: March 11, 2025
Accepted: March 21, 2025
Published online: August 15, 2025
Processing time: 215 Days and 11.4 Hours
Gastric cancer (GC) has remained one of the leading causes of cancer-related deaths globally. The development of noninvasive biomarkers in cancer diagnosis and treatment has gained substantial traction in recent years. Recent evidence highlights hypercoagulation as a promising prognostic biomarker, particularly in locally advanced GC (LAGC) who underwent radical resection after neoadjuvant immunochemotherapy (NICT). A recent study by Li et al showed that hypercoagulation is a valuable prognostic indicator for patients with LAGC who have undergone radical resection following NICT. While the study addresses an important clinical issue and provides insightful findings, the present study offered valuable insights; the applicability of these findings was constrained by the retrospective design, the focus on a single center, and the small sample size of the existing studies. Additionally, vital confounders, such as preoperative comorbidities and systemic inflammation, are inadequately addressed. Future studies should focus on prospective multicenter trials, incorporating advanced predictive models such as machine learning algorithms to integrate coagulation markers with other clinical variables for personalized risk stratification. In addition, we are required to validate findings to examine the biological mecha
Core Tip: Gastric cancer (GC) has a high mortality-to-incidence ratio. Noninvasive biomarkers promise early detection, monitoring, and personalized treatment strategies. A recent study by Li et al examined whether hypercoagulation is an effective prognostic indicator in locally advanced GC patients who underwent radical resection after neoadjuvant immunochemotherapy. Future research should implement a multicenter approach to improve applicability. Additionally, considering important confounders such as systemic inflammation, nutrition, and immune responses would deepen our understanding of the association between hypercoagulation and outcomes. Advanced methods such as machine learning and imaging techniques could improve predictive models, allowing clinicians to treat patients more effectively.