Utility of inflammatory markers as predictors of recurrence in gastrointestinal stromal tumors: Insights from a nomogram-based approach

Abstract

Gastrointestinal stromal tumors (GISTs), the most prevalent mesenchymal tumors, often have poor outcomes due to high recurrence rates. However, the specific risk factors for GISTs, particularly those concerning the innate immune-inflammatory response, remain poorly understood. This editorial highlights key prognostic factors that impact GIST progression and prognosis, while discussing the findings of a recent study that investigated the prognostic value of systemic inflammatory markers: systemic immune-inflammation index, neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and monocyte/lymphocyte ratio, on recurrence-free survival in GIST patients. This editorial examines strategies to enhance the clinical applicability of the nomogram developed in the study, ensuring its effectiveness for robust implementation. Future directions outlined in the editorial stress the importance of integrating molecular insights, including KIT and PDGFRA mutations, tumor staging, and mitotic rates to refine predictive models. The editorial also underscores the value of multi-center studies to enhance the generalizability and clinical relevance of these approaches. By bridging inflammatory biomarkers with genetic and clinicopathologic factors, a more comprehensive understanding of GIST pathophysiology can be developed, paving the way for improved management strategies and patient outcomes. This perspective serves as a call to action for continued research into the interplay between genetic mutations, inflammatory marker modulation, and GIST progression, aiming to expand the scope of personalized oncology through a deeper understanding of GIST progression.

Key Words: Gastrointestinal stromal tumor; Recurrence prediction; Nomogram predicative modeling; Inflammatory markers; Tumor risk factors

Core Tip: Gastrointestinal stromal tumor (GIST) treatment is complicated due to its high recurrence rate and multifactorial etiology. This editorial explores the findings of a recent study that evaluates the impact of inflammatory markers on GIST tumor progression and prognosis. Platelet/lymphocyte ratio and monocyte/lymphocyte ratio significantly correlated with recurrence-free survival of patients with GISTs. This editorial explores how future integration of genetic mutations, tumor staging, and molecular profiling could bolster these results and future predictive models developed from this analysis.

TO THE EDITOR

Cancer remains a leading cause of death worldwide, with its prevalence increasing alongside global life expectancy[1]. Among gastrointestinal (GI) cancers, GI stromal tumors (GISTs) are the most prevalent mesenchymal tumors, accounting for a significant proportion of GI malignancies[2]. Only about 18% of GISTs are benign, and most tumors are resistant to traditional therapies such as chemotherapy and radiotherapy. With a recurrence rate of approximately 42%, GISTs often lead to poor patient outcomes[2,3]. As such, it is vital to identify and mitigate risk factors associated with GIST disease progression and recurrence.

Although our native immune system is designed to detect and eradicate foreign pathogens, this same inflammatory response can paradoxically accelerate tumorigenesis in malignant neoplasms. Inflammatory processes promote tumorigenesis through stimulation of angiogenesis, epithelial to mesenchymal transition (EMT), and invasion, driving tumor progression and treatment resistance[4]. Inflammatory markers, such as the systemic immune-inflammation index (SII), neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) have been associated with worse prognosis and overall survival in numerous cancer types, including breast, lung, prostate and colorectal cancers[5-8]. However, the specific risk factors impacting metastatic GIST prognosis remain unclear[9].

The study titled “Prognostic value of inflammatory markers in predicting recurrence-free survival in gastrointestinal stromal tumor patients: A nomogram-based approach” seeks to determine the role of inflammatory markers such as SII, NLR, PLR, and MLR in GIST progression and recurrence[10]. The authors develop a novel inflammatory-based nomogram model to assist clinicians in predicting recurrence-free survival (RFS) in postoperative GIST patients.

Zhao et al’s retrospective study involved patients who underwent GIST resection surgery between January 2014 and January 2017[10]. All patients had blood samples collected within 7 days before surgery. Measurements included MLR, NLR, PLR, and SII. Patients were followed every 3 months for the first 2 years and every 6 months for the following 3 years, totaling 5 years. Data collected from patients' medical records included name, sex, age, body mass index (BMI), tumor size and site, tumor rupture status, preoperative levels of neutrophils, platelets, lymphocytes, and monocytes, use of targeted therapy post-surgery, and time to diagnosis of recurrence or metastasis. In total, 124 patients were included.

The authors developed a nomogram based on relationships in key clinicopathological features, including sex, age, BMI, primary tumor site, size of the tumor, targeted therapy, and inflammatory markers. Univariate analysis of these descriptive variables and blood measurements (SII, NLR, MLR, and PLR) were utilized to evaluate the impact on five-year RFS, which was then subjected to multivariate regression analysis. Multivariate analysis revealed that PLR, MLR, and targeted therapy were independent factors affecting patient prognosis, whereas NLR and SII did not demonstrate significant predictive value. Subsequent Kaplan-Meier survival analysis revealed that while tumor size was related to MLR and PLR, preoperative SII, MLR, NLR, and PLR, as well as post-operative targeted therapy were significantly associated with RFS.

This study establishes a foundation for integrating key inflammatory markers to create a novel approach for assessing RFS in patients with GISTs. The findings suggest that preoperative inflammatory indicators could enhance predictive prognosis for GIST patients, providing physicians with an additional tool for evaluating patient outcomes. Additionally, these inflammatory markers are cost-effective and easily accessible, as they are derived from complete blood counts routinely performed during physicals or during the diagnostic process[11]. The study's design is comprehensive, with a follow-up schedule that adhered to a rigorous standard of longitudinal research. Participants were assessed every 3 months during the first 2 years, transitioning to 6-month intervals through 5 years post-surgery. This meticulous approach aligns with current best practices and adopts a more rigorous long-term follow up schedule compared to similar studies, ensuring consistent monitoring, accurate risk stratification, and reliable long-term outcome data[2,12].

The study’s use of Kaplan-Meier survival curves and Cox regression analysis offers a reliable platform for assessing the relationship between inflammatory markers and RFS. The inclusion of both univariate and multivariate regression analyses helps to isolate independent risk factors, adding depth to the prognostic assessment. By adjusting for multiple variables, the study increases the reliability of its findings, providing insights into which markers most significantly impact RFS.

Notably, the observed associations between elevated inflammatory markers (SII, NLR, PLR, and MLR) and RFS in GIST highlights the importance of understanding tumor-related inflammation, specifically including microenvironment contribution and immune escape mechanisms. Chronic inflammation is a critical factor influencing tumor progression and recurrence through its impact on the local tumor microenvironment and modulation of immune response. As shown by increased PLR and SII values, elevated platelet counts facilitate enhanced GIST progression and metastasis via secretion of angiogenic and pro-inflammatory cytokines. The release of vascular endothelial growth factor accelerates angiogenesis, providing increased blood supply for tumor growth and metastasis[13,14]. Additionally, platelet-driven tumor necrosis factor-alpha can induce EMT, facilitating extravasation from pre-metastatic niches and subsequent systemic dissemination[13,15,16]. Finally, formation of platelet-tumor cell aggregates can protect circulating tumor cells (CTCs) from immune-detection and NK-mediated lysis, enhancing metastatic potential and systemic seeding[13].

Increased neutrophil counts, as reflected by increasing SII and NLR values, similarly contribute to tumor invasion, metastasis, and recurrence. Release of reactive oxygen species induces DNA damage and may promote genetic instability in tumor cells, driving mutations that increase invasive and metastatic potential[17]. Additionally, release of matrix metalloproteinases (MMPs), particularly MMP-9, degrade the extracellular matrix, enhancing tumor cell invasion and migration[18]. Neutrophil extracellular traps have been implicated in cancer progression and recurrence by promoting CTC adhesion, migration, and invasion, creation of a favorable tumor microenvironment for growth, and induction of EMT[17,19,20].

Elevated MLR values, indicating disproportional monocyte to lymphocyte counts, further shape GIST progression and recurrence. Tumor-associated macrophages, specifically the M2 immunosuppressive phenotype of macrophages, play a significant role in establishing a pro-tumorigenic microenvironment; promoting localized immunosuppression, tumor angiogenesis, and metastatic capacity. The MIF/CXCR4 pathway, which polarizes macrophages towards an M2 phenotype, has been identified as a key driver in GIST, facilitating immune escape and tumor acceleration[21,22].

The chronic inflammatory cascades associated with elevated SII, NLR, PLR, and MLR levels facilitate a pro-tumorigenic microenvironment that favors immune evasion and tumor progression, extravasation, dissemination, and systemic seeding, especially in cancers such as GIST. Modern therapeutics may eliminate metastatic niches, but the probability of recurrence persists. Tracking these markers through a nomogram-based approach may offer clinicians a practical method for capturing and quantifying inflammatory-driven recurrence risk in GIST via a liquid snapshot, translating the plethora of complex interactions into clinically actionable prognostic tools.

Similar studies have identified SII as an independent prognostic factor in GIST, but Zhao et al’s study[10] took a more comprehensive approach by evaluating a broader range of inflammatory markers[23]. This study uniquely integrated multiple inflammatory markers including SII, and other inflammatory markers like NLR, PLR, and MLR into a novel nomogram for predicting RFS in GIST patients. By incorporating these markers alongside traditional clinicopathological factors, the study offers a more multifaceted approach to GIST prognosis.

The findings reveal that PLR, MLR, and targeted therapy are independent prognostic factors, whereas NLR and SII did not demonstrate significant predictive value. This contrasts with prior studies that identified preoperative SII as a strong and independent predictor of overall survival in gastric cancer patients[23]. Overall, this study stands out by integrating inflammatory markers with conventional prognostic factors to develop a refined nomogram specifically for predicting RFS in GIST patients.

Despite its strengths, this study’s conclusions and nomogram could be bolstered by incorporating additional prognostic indicators that have significant impacts on RFS. The clinicopathologic factors included in this study—gender, age, BMI, tumor site, tumor size, and treatment strategy – could be expanded to incorporate variables such as mitotic rate, mitotic count, margin status, and tumor staging (e.g., American Joint Committee on Cancer or Tumor-Node-Metastasis), which have been established in the literature to influence GIST prognosis[9,12,24].

Additionally, while sufficient for exploratory analysis, the small sample size of 124 patients limits statistical power and the detection of nuanced differences across groups. This sample size is particularly restrictive given that other studies have utilized larger cohorts of patient populations upwards of 1000 to 4000 patients to establish risk factors for nomogram development[2,9,24,25]. Conducting a multi-center study with additional prospective data would increase the sample size, enhance patient diversity, and ultimately strengthen the validity of the authors’ findings. A larger and varied cohort would better corroborate the independent prognostic value of MLR and PLR in GIST.

The authors of the study conclude that SII and NLR are not independent risk factors for RFS. However, this may result from the smaller sample size or confounding variables not accounted for in the model. The absence of matched cohort analysis complicates the isolation and control of confounding variables. Notably, the area under the curve (AUC) values reported (0.768, 0.771, 0.677, and 0.730) were relatively modest compared to similar studies, suggesting that a larger cohort could increase the statistical strength of these findings[9,24]. Optimal cutoff points derived from smaller cohorts risk overfitting, which can reduce generalizability to other patient populations. Consequently, the dismissal of the SII and NLR as irrelevant to GIST prognosis may be premature. A larger sample and multi-center analysis might yield different results regarding the significance of SII and NLR as prognostic indicators. Further studies should explore their potential roles in the context of a broader patient cohort and more comprehensive variables.

Building on the above, this study does not fully address confounding factors, notably the presence or absence of mutations in the KIT or PDGFRA genes, which independently influence recurrence in GIST patients beyond inflammatory markers[26,27]. These genetic factors play a crucial role in disease progression and merit consideration in predictive modelling. With advancements in understanding genetic influence on GIST progression, treatment has shifted towards targeted therapies aimed at specific genetic abnormalities, which has led to substantial improvements in survival[26]. Given the significant genetic impact on GIST outcomes, future research could adopt a more holistic approach by integrating mitotic rates, cellular characteristics, tumor staging, and key genetic mutations (KIT and PDGFRA) into recurrence prediction models. Incorporating these variables would make the study's findings and nomogram more comprehensive and representative of the critical prognostic factors driving GIST progression.

Overall, nomograms offer clinicians a user-friendly and effective tool for predicting patient prognosis in cancer care using readily available clinical metrics. By integrating these predictive models, physicians can refine treatment strategies and tailor follow-up plans based on anticipated disease progression. Strong nomograms accurately stratify patients into different risk groups based on clinical factors and prognosis predictions and anticipate the course of the patient’s disease progression[28]. The clinical utility of nomograms lies in their predictive accuracy, often assessed by the AUC. Additionally, as non-invasive screening tools, they contribute to improved patient quality of life by enabling early risk stratification and personalized management. Specifically, through the nomogram created in this study, physicians are equipped with a prognostic model based on inflammatory markers that are cost-effective and easily accessible. Many of these markers are often included in routine complete blood count tests for cancer patients, making this nomogram highly practical, as it leverages already available clinical data to enhance risk assessment and guide treatment decisions.

While this study presents a valuable and practical prognostic tool, certain limitations must be addressed to enhance its reliability and clinical applicability. The nomogram developed in this study was not externally tested or validated like has been done in other studies that developed nomograms for GIST[24]. Nomograms are a predictive modeling tool used to graphically depict multivariate logistic regressions. A key feature of multivariate logistic regressions is that they are trained on an isolated dataset and tested on a separate dataset to evaluate the model’s performance on unseen data. Both internal and external validation are crucial for establishing the nomogram's clinical utility and generalizability[28]. While it is understandable that the authors were unable to externally test their model given their very modest sample size, the clinical value of the designed nomogram would be much higher if the model was validated through external testing methodology as the bootstrapping technique, as internal validation alone can bias the results of the nomogram[24,28]. Incorporating a larger, more diverse patient population through a multi-center approach would strengthen the study’s conclusion, the nomogram's accuracy and statistical significance, ensuring it’s generalizability.

Zhao et al’s study makes a valuable contribution to understanding and predicting recurrence in GIST through an easy-to-use, readily available tool, providing the potential for better patient outcomes[10]. Compared to previous work, this study incorporated a comprehensive range of inflammatory markers to evaluate their impact on GIST prognosis and their value in the design of a novel inflammation-based nomogram for clinical practice. By identifying and validating key inflammatory markers, PLR and MLR, alongside the impact of targeted therapies, the authors deliver crucial insights that can inform personalized and more effective treatment strategies for GIST patients. The strengths of this study lie in its comprehensive analysis of readily accessible inflammatory markers, robust statistical methodology, and development of a novel nomogram-based approach that can guide post-operative follow-up through an easy-to-use, practical tool. However, the limitations, including its retrospective design, small sample size, lack of additional, well-established prognostic indicators, and absence of external nomogram validation limit its immediate clinical applicability.

Future studies should aim to address these limitations by incorporating a more comprehensive analysis of independent risk factors that play a role in tumor progression, including genetic mutations (KIT and PDGFRA), tumor staging, and molecular profiling. This would enhance predictive accuracy and clinical utility. Expanding the sample size through multi-center studies would allow for both internal and external validation of the nomogram, further enhancing its clinical value. Finally, investigating the interplay between genetic mutations and inflammatory marker modulation could provide a deeper understanding of GIST pathophysiology. By doing so, future research can expand on this exploratory analysis, offering a more comprehensive understanding of GIST progression and presenting a more clinically accurate, nomogram-based approach to enhance early recurrence detection and improve patient outcomes.