TO THE EDITOR
Gastric cancer (GC) is the fifth most prevalent cancer and presents with the fourth leading cause of cancer-related mortality worldwide[1]. Treatment options, including systemic chemotherapy, radiotherapy, surgery, immunotherapy, and targeted therapy, have effectively managed GC. Recent international benchmarking studies revealed that, despite advancements in treatment, the survival rates for GC remain low[2,3]. However, new biomarkers could improve its unfavorable outcomes[4]. In recent decades, there has been a surge in research on how biomarkers can be used in predictive modeling and their potential benefits in evaluating the risk of clinical events as an indicator to predict the therapeutic effects and improve outcomes. This growing body of work highlights the importance of biomarkers in improving healthcare decision-making and patient management.
Hypercoagulation has emerged as a promising prognostic biomarker in GC research. Its potential applications include aiding in the early detection of the disease, facilitating ongoing monitoring of patient conditions, and contributing to developing personalized treatment strategies. This innovative approach highlights the importance of understanding coagulation factors in the management of GC. In light of current developments, we are particularly interested in the recent study by Li et al[5], which reports on their findings that hypercoagulation serves as a valuable prognostic indicator for patients with locally advanced GC (LAGC) who have undergone radical resection following neoadjuvant immunochemotherapy (NICT). While the study addresses an important clinical issue and provides insightful findings, it also has several methodological and analytical limitations and several constructive suggestions for further refinement and depth of the research.
The study's retrospective and single-center design creates inherent limitations on the generalizability of its findings. Although practical for exploratory analyses, this study design introduces biases that may weaken the reliability of the results. For instance, the inclusion and exclusion criteria might have inadvertently excluded patients whose data could provide further insights into the relationship between hypercoagulation and clinical outcomes. Patients with inadequate data or those who died after surgery were excluded, potentially narrowing the findings' scope. Selection bias, particularly the overrepresentation of patients who managed well with treatment, could have impacted the observed outcomes[6]. Prospective multicenter studies are recommended to confirm the findings in a broader context and address these concerns. Future research should emphasize conducting prospective multicenter trials that utilize advanced predictive models, including machine learning algorithms. These models can improve the integration of coagulation markers with various clinical variables, enabling more personalized approaches to risk stratification. Additionally, sensitivity analyses on the excluded cases could improve the dataset’s interpretative depth.
While the study's statistical analysis was rigorous, it did not adequately consider possible confounders, such as preoperative comorbidities, nutritional status, inflammatory markers, and treatment adherence. These could significantly affect survival outcomes and should be incorporated into future models[7]. For instance, the authors failed to address the impact of underlying systemic inflammation or immune status that influences coagulation and cancer prognosis. Although the analysis identified the cut-off values for age, D-dimer, and fibrinogen levels, they lack external validation and a clear rationale. Future research should use multivariate models that adjust for a broader range of confounding factors, such as preoperative comorbidities and systemic inflammation, and confirm the cut-off values with independent datasets to improve statistical robustness[8].
While informative, the results section highlights some limitations that need consideration. For instance, a median follow-up of 21 months was relatively brief for assessing long-term outcomes such as three-year overall survival and disease-free survival. The figures and tables in the manuscript would have improved with more evident labels and annotations to enhance reader understanding. For example, Figures 2 and 3, which depicted survival curves and comparisons, need additional clarity for better interpretation. Moreover, the manuscript lacks comprehensive data on patients who switched to alternative treatment approaches due to ineffective NICT. Extending the follow-up period and conducting detailed subgroup analyses for these patients could offer a better understanding of the study's implications[9]. Finally, the clinical applicability of these findings warrants further elaboration. Furthermore, developing screening protocols for hypercoagulation and incorporating these into standard clinical practice can significantly improve patient management.
The discussion was primarily focused on coagulation markers as prognostic indicators but did not sufficiently address their interaction with other clinical factors, such as immune response and surgical complications. Moreover, the impact of anticoagulation therapy on hypercoagulated patients remains underexplored. Expanding the discussion to include these dimensions would provide a more holistic view of the findings. The broader implications of hypercoagulation, particularly its interplay with immune modulation and the risk of postoperative complications, need greater emphasis. Additionally, the effect of anticoagulation therapy on patient outcomes ought to be explicitly addressed to guide future clinical practices. The authors’ suggestion to use coagulation markers as prognostic indicators is promising. However, integrating advanced predictive modeling techniques like machine learning could offer more reliable and personalized insights. This approach would support the development of predictive tools personalized for individual patient profiles, potentially improving clinical outcomes.
Future direction
In the context of future research, we want to share a few points for consideration. Future studies should involve multicenter trials designed with a prospective framework, which could help validate the study's findings across a broader range of patient populations, effectively minimizing bias and increasing the general applicability of the results. By incorporating diverse healthcare settings, researchers can analyze how different demographics and genetic backgrounds influence treatment outcomes[10]. There is a critical need for research that explores the biases arising from excluding specific patient populations from studies-particularly those who did not survive the postoperative period or had incomplete medical records. By systematically quantifying the impact of such exclusions on survival outcomes, researchers can determine how these limitations may skew results and ensure a more accurate interpretation of overall findings[11].
Future research should emphasize the biological mechanisms that connect hypercoagulation to tumor progression, focusing on how coagulation markers interact with systemic factors like inflammation and angiogenesis. Understanding these interactions may uncover new therapeutic targets and enhance our comprehension of cancer progression[12,13]. These studies could focus on how coagulation markers interact with various systemic factors, such as inflammation and angiogenesis, which could potentially reveal new targets for therapeutic intervention and how these dynamics influence cancer advancement[14]. In addition, it is important to explore how lifestyle factors that can be modified, such as dietary choices, levels of physical activity, and smoking status, could affect coagulation profiles and treatment outcomes in GC patients receiving neoadjuvant immunotherapy[15]. Understanding these relationships could lead to developing tailored lifestyle interventions that support treatment efficacy and improve patient well-being.
Future research should expand its focus to include various biomarkers that may interact with hypercoagulation. This contains inflammatory cytokines, immune checkpoint markers, and metabolic parameters, revealing a richer and more nuanced understanding of the tumor microenvironment[16]. In addition, the innovation and validation of advanced machine learning-based predictive models that use coagulation markers in conjunction with other clinical variables hold great promise[17]. Such models could improve personalized risk stratification and treatment planning, allowing clinicians to tailor interventions based on individual patient profiles. Implementing imaging modalities, including PET scans and dynamic contrast-enhanced MRI, could offer insights into how changes in coagulation markers relate to the dynamics of the tumor microenvironment and patient responses to treatment. These imaging techniques could provide a comprehensive view of tumor behavior and treatment efficacy[18]. Finally, real-world data is important for assessing the applicability of research findings in routine clinical practice[19]. By assessing how these findings translate into routine clinical settings, especially among heterogeneous patient populations, researchers can determine their practical impact and enable improvements in patient care.
CONCLUSION
In conclusion, hypercoagulation shows promise as a prognostic biomarker in LAGC following NICT. However, several methodological limitations must be addressed to strengthen its clinical applicability. Noninvasive biomarkers can revolutionize clinical decision-making and improve patient care outcomes. These biomarkers are important in early detection, disease monitoring, and tailoring treatment strategies for various cancer types. The retrospective, single-center design of the study by Li et al[5]. limits generalizability, underscoring the need for prospective, multicenter trials. Additionally, the study's failure to account for key confounders, such as preoperative comorbidities and systemic inflammation, weakens its conclusions. Future research should incorporate multivariate models to adjust for these factors and externally validate cutoff values for coagulation markers to ensure reliability. Nevertheless, further studies are required to address the challenges related to methodological limitations, extend follow-up durations, and incorporate more biomarkers. Using predictive models and investigating the impact of anticoagulation therapy would support the clinical relevance of these findings.
The relatively short follow-up period also limits the assessment of long-term outcomes, highlighting the need for extended follow-up and subgroup analyses. Also, examining lifestyle factors, using advanced imaging methods, and promoting collaborative research will deepen our comprehension of hypercoagulation’s influence on cancer prognosis. Furthermore, integrating advanced predictive modeling techniques, such as machine learning, could enhance the predictive accuracy of coagulation markers by combining them with other clinical variables, enabling personalized risk stratification. These recommendations aim to address current challenges, helping future studies improve predictive models. By addressing these areas, we can approach the goal of improving outcomes for patients with GC. Continuous research and rigorous validation are necessary to establish the reliability of these biomarkers as reliable tools in clinical settings, which could ultimately help develop more effective cancer management strategies.
