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Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification

BMC Cancer volume 25, Article number: 809 (2025) Cite this article

Abstract

Background

Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most important predictors and unravel their interactions to classify patients, which might guide prioritized treatment decisions.

Methods

A total of 140 patients with histopathological confirmed GC who underwent surgery between 2011 and 2016 were enrolled in the study. The inspired modification of the partial least squares (SIMPLS)-based model was used to identify the most significant predictors and interactions between variables. Predictive partition analysis was employed to establish the decision tree model to prioritize markers for clinical use. ML models have also been developed to predict TNM stage and different subtypes of GC. Latent class analysis (LCA) and principal component analysis (PCA) were carried out to cluster the GC patients and to find a subgroup of survivors who tended to die.

Results

The findings revealed that the SIMPLS method was able to predict the mortality of GC patients with high predictabilities (Q2 = 0.45–0.70). The analysis identified MMP-7, P53, Ki67, and vimentin as the top predictors. Correlation analysis revealed different patterns of prognostic markers in the non-survivor and survivor cohorts and different GC subtypes. The main prediction models were verified via other ML-based analyses, with a high area under the curve (AUC) (0.84–0.99), specificity (0.82–0.99) and sensitivity (0.87–0.99). Patients were classified into three clusters of mortality risk, which highlighted the most significant mortality predictors. Partition analysis prioritizes the most significant predictors P53 ≥ 6, COX-2 > 2, vimentin > 2, Ki67 ≥ 13 in mortality of patients (AUC = 0.85–0.90).

Conclusion

The present study highlights the importance of considering multiple variables and their interactions to predict the prognosis of mortality and stage in GC patients through ML-based techniques. These findings suggest that the incorporation of molecular biomarkers may enhance patient prognosis compared to relying solely on clinical factors. Furthermore, they demonstrate the potential for personalized medicine in GC treatment by identifying high-risk patients for early intervention and optimizing therapeutic strategies. The partition analysis technique offers a practical tool for identifying cutoffs and prioritizing markers for clinical application. Additionally, providing Clinical Decision Support systems with predictive tools can assist clinicians and pathologists in identifying aggressive cases, thereby improving patient outcomes while minimizing unnecessary treatments. Overall, this study contributes to the ongoing efforts to improve patient outcomes by advancing our comprehension of the intricate nature of GC.

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Introduction

Gastric cancer (GC), accounting for 7.7% of total cancer deaths, is the third leading cause of cancer deaths worldwide. According to estimates published by the International Agency for Research on Cancer (IARC), over 750,000 new mortalities of GC worldwide and more than 12,500 new cases of mortality have been reported in Iran [1].

Despite all the advantages in the field of diagnosis and treatment of GC, the prognosis of patients with GC is still very poor, especially the long-term survival of patients with advanced GC, for whom the median survival is less than 12 months [2]. Therefore, accurate staging of GCs in patients may improve the management and outcome of these patients [3]. The TNM staging system provided by the International Union against Cancer/American Joint Committee on Cancer (UICC/AJCC)) has been recognized as the most important staging system [4]. However, the TNM staging system has limited applications, and middle-stage patients present diverse prognostic outcomes, making it difficult to accurately predict the mortality of patients [5]. Advancements in the management of GC and innovative therapeutic approaches offer preoperative treatment options and highlight the importance of identifying high-risk patients and the role of biomarkers in GC management [6, 7]. These factors underscore the need for the development of new and accurate prognostic models [8, 9]. Using various artificial intelligence (AI) algorithms, nonlinear statistical models can be constructed to predict the survival of GC patients and categorize patients into groups with better distinguishing abilities via unsupervised machine learning (ML) algorithms [10,11,12,13].

In the past few years, several models with different capabilities for determining the prognosis of GC have been designed [9, 10, 13,14,15,16,17]. Numbers of these models include only common clinical indicators (TNM stage, age, sex, etc.) to determine prognosis [14,15,16], and few models consider biomarkers in addition to other clinical factors, which increases the accuracy of the models. However, the number of these biomarkers is limited and can be expanded [9, 13, 17]. Additionally, the performed analysis has a limited ability, which can be solved by using AI and ML algorithms. Traditional analysis methods often have limitations in handling complex and multidimensional datasets, identifying nonlinear patterns, processing unstructured data, and performing predictive analysis. AI and ML algorithms can address these limitations and provide more robust and efficient solutions [18]. The study of multiple biomarkers and their interactions plays a crucial role in understanding the complex relationships among molecular pathways, particularly in diseases such as gastric cancer.

Furthermore, the potential interactions among biomarkers are often overlooked in conventional models, impeding the complex relationships among molecular pathways. Notably, the integration of molecular pathology and AI could lead to novel biomarkers that have diagnostic or prognostic value.

Several biomarkers, such as human epidermal growth factor receptor 2 (HER-2) [19], matrix metalloproteinase 7 (MMP-7) [20, 21], cyclooxygenase 2 (Cox2) [22, 23], vimentin [24, 25], tumor protein 53 (TP53) [26], CD34 [27], and Ki67 [28], have shown potential as markers for angiogenesis and cell proliferation in the prognosis of patients with GC.

In the present study, our objective was to utilize an AI model to identify potential biomarkers and their combinations for prognostic purposes and variant classifications in gastric cancer. An ML-based statistical method was applied to predict the mortality of GC patients and identify the complex interactions between predictors. The clustering method was used to design and classify the patients into different mortality groups (low-, moderate-, and high-risk groups) and to understand the role of each feature in the groups. To achieve this objective, a combination of seven biomarkers and other clinical and histopathological factors was employed in a cohort of GC patients. Identifying potential predictors is helpful for determining the appropriate prognosis and improving the understanding of the pathophysiological behavior of GC.

Methods and materials

Data collection

A retrospective cohort of 140 patients diagnosed with GC was included in the study, which was previously described by Razavirad et al. [29], who traditionally analyzed the pathological impacts of biomarkers. Clinical data and histopathological findings from patients were collected at the Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, I.R. of Iran, from April 2011 to January 2016. All patients who underwent neoadjuvant therapy were excluded from the study. The pathologic stage of patients was classified according to the 8th edition of the American Joint Committee on Cancer (AJCC) classification. Patients with stage I–III disease (n = 140) were analyzed, and the follow-up period was 60 months. A total of 21 variables, including patient demographics, histological findings and 7 biomarkers, were collected from pathologically confirmed patients with GC. Formalin-fixed, paraffin-embedded primary tumor specimens were retrieved from the Cancer Institute archives, sectioned into 3 μm slices, and processed for immunohistochemistry (IHC). Tissue sections were deparaffinized, antigen-retrieved in citrate buffer, blocked with bovine serum albumin, and incubated with specific antibodies against HER2, CD34, p53, Ki67, COX-2, MMP7, and vimentin. Immunoreactivity was assessed by two pathologists, with HER2 scored per CAP guidelines, CD34 evaluated for microvessel density, and p53, Ki67, COX-2, MMP7, and vimentin categorized based on staining intensity [30]. In this study, participants with missing values were excluded to minimize bias and ensure the accuracy of the ML models. Mortality prediction was performed at two time points, 18 and 24 months, as the median and mean survival times for GC patients, which is consistent with previous studies in Iran [31]. However, the current cohort was followed for 5 years, and only 4% of patients survived. There was a significant group imbalance between survivors and non-survivors from 24 months until 5 years. Because we have a relatively small sample size, group imbalance can lead to the development of biased classifiers, where the predictive performance is skewed toward the majority class, potentially resulting in accurate predictions for the minority class.

Continuous and ordinal variables were converted to binary variables via a categorical approach (cutoff point for each variable) using partition analysis (JMP pro, SAS). This study was approved by the ethics committee of Imam Khomeini Hospital Complex- Tehran University of Medical Sciences, Tehran, Iran (No. IR.TUMS.IKHC.REC.1400.001).

Statistical analysis

The statistically inspired modification of partial least squares (SIMPLS), an algorithm of PLS (a linear ML method) [32, 33] analysis, was applied to create a prediction model of 18- and 24-month mortality via patient demographics, histopathological findings and biomarker variables. SIMPLS-based prediction models were obtained through the training and validation sets. The most differentiating variables with variable importance in the projection (VIP) > 1.0 were selected for the building prediction model. The validation set was randomly selected on the basis of 30% of the 140 patients with GC, which was also considered internal validation. Q2 and R2Y were counted as the goodness for predictability and the goodness of variability, respectively, to evaluate the performance of the SIMPLS-based prediction model. The best prediction models were obtained using the most differentiating variables when the Q2 reached the highest value before decreasing and with the highest R2Y. Q2 and R2Y were computed and verified through the training and validation sets, respectively. The performances of the models are evaluated via leave-one-out cross validation (LOOCV) and accuracy.

To categorize the continuous and ordinal values, partition analysis was performed via a decision tree to find the best cutoff point of variables on the basis of the relationship between the outcome and predictors. Latent class analysis (LCA) was performed to identify low- and high-risk patients with GC through the clustering of cohorts using the most differentiating variables. Principal component analysis (PCA) was used to identify the different clusters obtained via LCA. PCA was also applied to find the trend and outliers using all the variables. Cox regression and Kaplan–Meier survival analyses were performed on the AJCC stage groups (stages I, II, and III in the favorable category) among the test data and our model to compare the distinguishing ability between the two methods.

Model screening was conducted to check the final prediction model by providing a summary table via other ML methods, such as XGBoost, support vector machine (SVM), boosted tree (BT), bootstrap forest (BF), K-nearest neighbor (KNN), generalized regression lasso (GRL), boosting neural network (BNN), fit stepwise (FS), and naïve Bayes (NB) methods. Model screening also helps in finding efficient workflows as well as comparing and exploring datasets for the best predictive model.

We further applied predictive partition analysis (PPA) or a decision tree, which is ML method [34] that could be used to classify or predict data responses. The ML aspect of the decision tree comes into determining when and where to divide the data between branches. Data partitioning was performed by dividing a dataset into subsets, including training and validation groups.

Results

Patient characteristics

A total of 140 patients with confirmed GC were enrolled in the study, and 62 (44%) and 99 (70%) patients died within 18 and 24 months, respectively. Table 1 shows the demographic characteristics and histopathological and biochemical markers of survivors and non-survivors GC patients at 18 and 24 months after disease onset. The patient information is available in supplementary table E1.

Table 1 Distribution of patient demographics and histological and paraclinical biomarkers among patients with gastric cancer for mortality at 18 months (left) and 24 months (right)
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Predicting mortality in gastric cancer patients via clinical and paraclinical data

An overview of the present study is shown in Fig. 1. The ML-based approach showed that demographics, histopathological and biochemical markers can be used for predicting the mortality outcomes of patients with GC. SIMPLS analysis was carried out via most differentiating variables (VIPs) [35] to establish the prediction model. The prediction model was developed on 95 patients in the training set and 45 patients in the validation set. Two factor-based SIMPLS models had high predictabilities (Q2 = 0.45 and Q2 = 0.70) for predicting mortality at 18 and 24 months, respectively, and included a total of 10 variables that contributed to the prediction models. SIMPLS-based scatter plots demonstrated very good discrimination between survivors and non-survivors for both 18- and 24-month mortality prediction studies (Fig. 2. A & C). Table 2 shows that MMP-7, P53, and Ki67 were the most important variables for 18-month mortality. For 24-month P53, Ki67 and vimentin were considered the top 3 predictors associated with 24-month mortality in patients with GC.

Fig. 1
figure 1

Overview of the study profile for AI application in gastric cancer prognosis. GC: gastric cancer, PPA: predictive partition analysis

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Fig. 2
figure 2

SIMPLS-based scatter and coefficient plots show very good discrimination between A, B: survivors and non-survivors at 18 months and C, D: 24-month mortality prediction studies Survivors, non-survivors

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Table 2 Importance values (VIPs) of the 10 variables with the greatest ability to predict mortality at 18 (left) and 24 months among patients with gastric cancer
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Furthermore, the coefficient plots revealed that pathological tumor scoring and the presence of regional lymph nodes, vimentin, HER-2, COX, MMP-7, Ki67, and P53 were positively correlated with 18- and 24-month mortality. Low CD34 and high CD34 levels were correlated with mortality at 18- and 24-month mortality, respectively, indicating that the role of CD34 may change in mortality over time (Fig. 2. B & D).

PPA verified that the abovementioned most differentiating variables are strong predictors for mortality at 18 and 24 months for the training and validation sets in comparison with TNM staging (Table 3).

Table 3 Characteristics of the results of the partition analyses to predict mortality at 18 and 24 months
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PCA-based correlation analysis showed that the abovementioned differentiating variables were highly correlated with each other for 18- and 24-month mortality prediction (Fig. 3). Age group was less correlated with most of the variables. Additionally, tumor size was positively correlated with regional lymph node variables in both 18- and 24-month mortality studies. PCA correlations among the most important variables are available in supplementary table E2.

Fig. 3
figure 3

A PCA-based correlation analysis heatmap revealed that the most differentiating variables were highly correlated with mortality at 18 months in the A: non-survivor and B: survivor and 24 months in the C: non-survivor and D: survivor groups. Positive correlations are shown in red, whereas negative correlations are shown in blue. The intensity of the color is related to the correlation coefficient

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Model screening revealed high AUCs (> 0.80), high specificities (> 90%), and good sensitivities (> 70%) when the most differentiating predictors were used in other ML methods, such as SVM, KNN, and GRL (Table E3).

Identification of high- and low-risk patients with gastric cancer

Further investigations using LCA showed that patients with GC can be clustered to identify high-risk patients based on the clinical and biomarker data. LCA-based clustering revealed three main clusters among survivors and non-survivors. LCA-based clustering revealed that cluster 2 and cluster 3 had 44% and 70% mortality rates, respectively (Fig. 4). Compared with Clusters 2 and 3, Cluster 1 had the lowest rate of mortality (0%). All 3 clusters were well depicted through a PCA plot that can verify the clustering via two unsupervised methods. Table 4 shows that although variables had different contributions (conditional probabilities) to each cluster, several variables markedly impact clustering. Hence, CD34 ≥ 30, P53 ≥ 6, Ki67 ≥ 13, and vimentin > 2 were highly correlated with cluster 3 and the highest rate of mortality. On the other hand, the biomarkers MMP-7 ≤ 2, HER-2 ≤ 1, pathological tumor < 3 and regional lymph node ≤ N1 were specifically correlated with the lowest rate of mortality (cluster 1). Moreover, HER-2 > 1, regional lymph node > N1, COX-2 > 2 and MMP-7 > 2 showed similar probabilities for clusters 2 and 3. Cox regression analysis and Kaplan‒Meier survival curves revealed that the survival curve of each subgroup had good precision for 18- and 24-month mortality (p < 0.05). For both the clinical TNM staging and LCA clustering methods, the curves showed acceptable discrimination (p < 0.05) (Supplementary Figure E1).

Fig. 4
figure 4

PCA plot illustrating the LCA-based clustering of patients with gastric cancer. Clusters 2 and 3 are associated with a higher rate of mortality. Survivors, non-survivors

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Table 4 LCA-based conditional probabilities of each variable for clusters 1–3. The orange and blue colors represent high and low correlations, respectively, with each cluster
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The partition analysis based on 18-month mortality showed two main branches on two sides. The branches show that patients with P53 ≥ 6, COX-2 > 2 and vimentin > 2 are associated with the prediction of mortality, and patients with P53 < 6 and vimentin ≤ 2 are associated with survival (Supplementary Figure E2). Also, 24-month-based partition analysis showed that P53 ≥ 6 and Ki67 ≥ 13 in patients with GC were associated mainly with mortality. On the other hand, patients with P53 < 6 and regional lymph nodes ≤ N1 were associated with survival outcomes (Supplementary Figure E3).

Predicting TNM stages of gastric cancer

A SIMPLS-based prediction model revealed that vimentin, P53 and HER-2 can predict the TNM stage of GC patients as an outcome with good predictability (Q2 = 0.45) (Fig. 5C). These biomarkers were the most differentiating among demographics and histopathological and biochemical markers when used in dichotomized values. The predictive decision tree demonstrated cut-off points of vimentin (≤ 2 or > 2), P53 (< 6 or ≥ 6) and HER-2 (≤ 1 or > 1) for the diagnosis of TNM-based staging, which was interestingly the same for the prediction of mortality outcome (Supplementary Figure E4). Model screening showed high AUCs (> 0.80), high specificities (> 90%), and good sensitivities (> 70%) when the most differentiating predictors were used in other ML methods, such as SVM, KNN, and GRL (Supplementary Table E4). Decision tree analysis also revealed that vimentin is the most differentiating biomarker, whereas vimentin < 2 and vimentin > 3 were mostly associated with TNM stages ≤ IIb and > IIb, respectively (Supplementary Figure E4).

Fig. 5
figure 5

Coefficient plots showing the most important variables for subtyping A: intestine B: adenocarcinoma and C: SIMPLS-based scatter plots showing good discrimination between two categories of patients with TNM stage > IIb and ≤ IIb

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Prediction of different types of gastric cancer: tumor location and histology

We applied the SIMPLS method to predict the tumor location (cardiac vs. non-cardiac) and tumor histology (intestine vs. non-intestine, diffuse vs. non-diffuse) as well as the tumor type (adenocarcinoma vs. non-adenocarcinoma) using clinical and biomarker variables. The models for differentiating adenocarcinoma from non-adenocarcinoma, and the intestine from the non-intestine were acceptably predictive (Q2 = 0.33 and Q2 = 0.34, respectively). Tumor histology, histology grade, tumor size, and age mostly contributed to predicting adenocarcinoma. Adenocarcinoma, pathological tumor, histology grade, HER-2 status, and age contributed to the ability to predict the tumor histology of the intestine from that of the non-intestine. The coefficient plot indicates how variables are correlated with the GC intestine and non-intestine subtypes (Fig. 5A-B).

Multivariate correlation analysis showed higher correlation between biomarkers in the cardia and intestine subtypes than between biomarkers in the noncardia and non-intestine subtypes. Age was negatively correlated with other variables among non-cardia subtypes compared with cardia. This phenomenon was also visible in the intestine and non-intestine subtypes (Supplementary Figure E5).

Discussion

Despite all the progress that has been made in the field of biomarker identification and model development in the field of oncology, there are still limitations in classifying patients in terms of prognosis and identifying the roles of various indicators and their relationships. In this study, the findings indicated that the ML-based SIMPLS model can accurately predict the mortality of GC patients via demographic and histopathological data, along with identifying the most important predictors and interactions between variables at two time points. The mean survival time for the patients was 18 months. The study focused on comparing the mortality rates between 18 and 24 months at 6-month intervals. In our study, 9 variables, including 2 clinical and 7 histopathological markers, were identified as potential prognostic predictors (Table 2). Mortality predictor variables were weighed and ordered on the basis of their importance in the prediction model. Hence, for 18-month mortality, MMP-7 > 2, P53 ≥ 6%, and Ki67 > 13 and for 24-month mortality, P53 ≥ 6%, Ki67 > 13, and vimentin > 2 were regarded as the top three predictors with the greatest impact on the model in the presence of other clinical risk factors, such as vascular invasion. This observation highlights the capacity of molecular biomarkers to provide a more accurate prognosis than do clinical factors. Nonetheless, the model demonstrated lower efficacy in the absence of additional predictors, as indicated in Table 1.

Few numbers of studies have been conducted on the development of AI-based models in the field of GC prognosis. Some studies have utilized deep learning algorithms for image analysis [36,37,38], whereas others have used numerical algorithms [9, 10, 12,13,14,15,16,17, 39,40,41], most of them have used SVM and ANN algorithms for model development and reported accuracies ranging from 0.79 to 0.94. Some of the studies included demographic (age, sex) and clinicopathological (tumor size, pathological grade, TNM staging) features and two biomarkers (CEA, CA199) as predictors. However, in the study of Jiang et al. [40], in addition to 3 baseline features (sex, CEA, and lymph node metastasis), 8 IHC markers (CD3invasive margin, CD3center of tumor, CD8IM, CD45ROCT, CD57IM, CD66bIM, CD68CT and CD34) were also considered. The main objective of most studies was to design a model using ML approaches to predict patient mortality with higher accuracy, which is traditionally predicted by TNM staging. TNM staging has been recognized as the most crucial staging system for stratifying GC patients into different risk groups [4]. However, middle-stage patients have a variety of prognostic outcomes, and a more accurate classification of these patients is required [5]. Importantly, the TNM staging system is applicable only after surgery due to the requirement of pathological analysis of tumor specimens and examination of lymph nodes. Nevertheless, some studies have suggested new patient classification methods to improve the TNM staging system [4, 42, 43].

In the study of Razavirad et al. [29], the data were analyzed using traditional statistical analysis methods. The present study employed unsupervised ML techniques, PCA and LCA, to cluster patients. This approach differs from previous studies, such as those of Que SJ et al. [9] and Oh et al. [13], who utilized supervised ML algorithms to categorize patients into mortality groups based on predicted survival probability. In the unsupervised learning approach, unique classes can be identified, which might offer a superior classification method in comparison to previous approaches [44]. The importance of each feature in each cluster was also revealed through this unsupervised approach.

By considering the incorporation of all the parameters in model development and a thorough discussion of the statistical process, this study has shed light on the importance of individual factors and their interplay with other variables in clinical and statistical aspects that were not addressed in previous studies. Given the complexity of AI analysis, it is beneficial to consider the associations between features and the formation of distinct clusters to enhance medical comprehensibility. Furthermore, identifying the interaction of parameters can offer insights into GC molecular pathways [45]. The correlations and interactions between different variables are presented in Fig. 3, revealing significant links among biomarkers such as vimentin/CD34, Ki67/CD34, and p53/Ki67 in non-survival cases. In the survival groups, notable correlations were observed between vimentin/COX2 and MMP-7/p53, suggesting the possibility that similar molecular pathways contribute to these relationships. Additionally, alterations in correlations were detected during different periods for certain biomarkers, including HER-2/vimentin, HER-2/CD34, vimentin/Ki67, and Ki67/CD34.

Several studies have shown significant relationships between these markers, such as the coexistence of positive HER2 status and vimentin expression, indicating more aggressive proliferation of carcinoma cells [46]. In the study of Ludovini et al. [47], the significant relationship between HER2 overexpression and CD34 has been identified as an important indicator reflecting microvessel density (MVD). A combination of p53/MMP-7 expression was found to be a prognostic indicator for Stage II/III GC [48]. The relationship between p53 expression and Ki-67 was not statistically significant, but the combination of p53 and COX-2 status was associated with significantly higher mean Ki-67 values in p53+/COX-2 + tumors than in p53-/COX-2- tumors [49]. Elevated cytoplasmic expression of TSPAN1 was positively correlated with increased nuclear expression of Ki-67, accompanied by upregulation of MVD, as indicated by the stromal level of CD34 [50]. Vimentin immunolabeling in the tumor-associated stroma was positively correlated with Ki-67, and COX-2 was found to promote the production of MMP-2, MMP-9, VEGF, and vimentin while inhibiting E-cadherin production [51]. The relationship between Ki67 and p53 and the histological grade of GC indicates their importance in cell differentiation [52, 53], whereas the correlation between p53 and CD34 biomarkers and perineural invasion indicates the role of biomarkers in cancer invasion pathways [54,55,56,57]. It has also been shown that Ki67, HER2, vimentin, MMP-7, p53 and CD34 are correlated with gastric cancer mortality, supporting previous works [25, 58,59,60,61,62].

In contrast to previous studies, the patient prognosis in the current study was assessed at 2 time points, 18 and 24 months. The order of most of the parameters was similar; however, some features, such as MMP-7, differed in importance, which could indicate changes in the pathophysiological role of MMP-7 for 6 months. Differences in the pattern correlations between factors related to 18- and 24-month mortality are also notable for the variability in tumor behavior during this 6-month interval, which requires further investigation to obtain a better understanding of tumor behavior. The role of MMP-7 and other MMPs in tumor growth, invasion and spread can cause this effect [63, 64].

In the clinical setting, employing all biomarkers and features to predict patient mortality may be impractical. Consequently, prioritizing the significance of individual biomarkers to select a subset for prognostic purposes can offer a practical approach for accurate prediction. This approach facilitates the early identification of aggressive GC cases and may guide the implementation of different treatment strategies, such as more aggressive chemotherapy or immunotherapy. Also, by classifying patients into distinct risk categories, the study supports a move toward personalized medicine in the treatment of GC. Patients identified within high-risk clusters may benefit from closer monitoring and early intervention, while those in low-risk categories could potentially avoid unnecessary aggressive treatments.

Partition analysis revealed that specific biomarker thresholds, p53, COX-2, and pathological tumors for 18-month mortality, and p53, CD34, and regional lymph nodes for 24-month mortality, are indicative of accurate mortality prediction. The results of the partition analysis agreed with those of the SIMPLS prediction model, indicating the importance of the P53 biomarker in the prediction of mortality at different times. Decision tree analysis revealed some differences in the importance of some biomarkers in the prediction of mortality with respect to the VIP scores of biomarkers, which may be related to the difference in the algorithms used for the two methods. Nonetheless, we believe that SIMPLS can be the primary method for identifying the most differentiating variables, and a decision tree could be a secondary method with more applications in clinical settings. Clinical Decision Support (CDS) systems have previously played a significant role in health care informatics and could be further enhanced through the incorporation of decision trees and partition analysis. These methodologies provide clinically practical cutoffs for biomarker expression, making AI-driven prognostic models more interpretable for healthcare providers. Such models can be integrated into clinical decision-support systems, assisting in staging, treatment selection, and the prediction of survival outcomes. Furthermore, these predictive models can be embedded in pathological workflows to assist pathologists in identifying high-risk GC patients.

A similar analysis was performed, in which the TNM system was used to identify the most important factors depicted in supplementary figure E3, and interestingly, patients were classified into advanced and early groups according to the conventional definition of advanced and early GC [65].

While TNM staging relies on tumor size (T), lymph node involvement (N), and metastasis (M), our model integrates multiple biomarkers (P53, Ki67, vimentin, MMP-7, COX-2, HER-2, CD34) alongside clinical factors, achieving higher predictive accuracy (AUC: 0.84–0.99) and better risk stratification. Unlike TNM staging, which is only applicable post-surgery, our model can provide preoperative prognostic information, guiding treatment decisions earlier. Additionally, decision tree analysis identified biomarker cutoff values (e.g., P53 ≥ 6, Ki67 ≥ 13) that can be used in clinical settings. However, while TNM staging is globally accepted and easy to interpret, our model requires external validation and AI explainability techniques before it can be more broadly adopted in clinical settings. Integrating ML-based approaches into clinical workflows could significantly enhance patient prognosis and personalized treatment strategies.

Previous investigations discovered that HER-2 status was significantly associated with various clinical parameters, such as tumor depth or pathological tumor (pT) status, recurrence, distant metastasis, and positive lymph node (pN) status, which was supported by prior research [66]. Moderate to poorly differentiated carcinoma frequently shows P53-positive staining, which is often accompanied by lympho-vascular invasion. Moreover, an analysis based on the pT and pN stages revealed a significant rise in p53-positive cases [67]. The expression of vimentin was notably elevated in patients with advanced stages of GC, particularly those exhibiting poorly differentiated types of gastric carcinoma [46]. These findings suggest a potential role for these biomarkers in predicting clinical outcomes and guiding therapeutic decisions for patients with GC.

Given the diverse environmental risk factors associated with GC subtypes and the recent development of molecular classifications such as The Cancer Genome Atlas (TCGA) [68] and Asian Cancer Research Group (ACRG) [69], it is imperative to comprehend the role of biomarkers in each anatomical (gastroesophageal junction/cardia and non-cardia) [70] and histological (Laurén classification) [71] subtype to gain insights into the pathophysiology of GC. This work offers an advantage over previous studies by investigating the differences in biomarker expression between GC subtypes. In intestinal and non-intestinal GC, variation in the expression of HER-2 has been revealed, as has greater expression in the diffuse subtype, which diverges from earlier findings [72]. In addition, as presented in supplementary figure E5, gastroesophageal junction/cardia GC shows different correlation patterns than non-cardia GC does, suggesting that these seven biomarkers contribute to a molecular pathway similar to that of non-cardia cancer.

The present study is subject to certain constraints. First, the sample size is relatively small compared with that of prior investigations. Nevertheless, the SIMPLS model is considered to be a suitable algorithm for cohorts of limited size [33, 73]. On the other hand, recent research on various ML techniques has indicated that the effectiveness of ML-based methods is linked to the quality of the data, even with sample size as small as 40–50 [74].

Additionally, the major limitation of the current study is the lack of an external validation set, which makes the generalizability and robustness of our findings uncertain, as the results have only been assessed within the confines of the current dataset. Additionally, since all patients were from a single institution, potential selection bias may have occurred. Incorporating a large independent validation cohort would have strengthened the study by allowing us to confirm the reproducibility and applicability of our conclusions across different patient populations, thereby enhancing the overall reliability of our findings. The prediction model was developed by dividing the data into training and validation sets and internally validate using the LOOCV method. Further studies are required to repeat the process of data partitioning several times to obtain an average performance. Finally, owing to the low number of patients, it was not possible to stratify them into more than three categories for better patient classification. Despite the 5-year follow-up period, the survival rate of patients at the end of 60 months was only 4%, leading to a significant group imbalance between survivors and non-survivors from 24 months until 5 years. Since we have a relatively small sample size, group imbalance may give rise to the development of biased classifiers, where the predictive performance is skewed toward the majority class, potentially resulting in accurate predictions for the minority class [75]; therefore, two specific time points, 18 and 24 months as the median and mean survival times, were chosen for conducting a mortality analysis.

In conclusion, the present study provides valuable insights into the use of ML-based techniques for prognostic biomarker identification in GC patients. Furthermore, the study classified patients into three categories and identified the major factors associated with mortality and staging in each group. These results indicate that molecular biomarkers could offer a more accurate prognosis compared to relying solely on clinical factors. Specifically, MMP-7, P53, Ki67, and vimentin were identified as the top predictors. The analysis of the correlation between various factors and their interactions revealed that the biomarker relationship pattern could differ among different anatomical and histopathological subtypes of GC, highlighting the possibility of distinct molecular pathways in GC pathogenesis. The partition analysis technique employed in this study offers a practical tool for identifying cutoffs and prioritizing markers to simplify models for clinical use. However, further research with large independent validation cohort is necessary to validate these findings and explore the potential of clinical and histopathological markers for enhancing prognostic accuracy in patients with GC.

Future research should focus on validating the identified biomarkers in larger, independent cohorts to assess their reliability and clinical utility. Given that some of these biomarkers are associated with early tumor progression, their validation could contribute to the development of early screening programs, enabling the detection of GC at an earlier, more treatable stage. Additionally, the potential application of these biomarkers in blood-based tests should be explored for non-invasive monitoring of GC progression and recurrence through liquid biopsy approaches. Such advancements could enhance surveillance strategies and improve patient outcomes. Further investigation into the molecular pathways linked to these biomarkers may provide insights into GC pathogenesis and facilitate the development of targeted therapies. Moreover, conducting subgroup analyses based on other factors than histological type, such as age, sex in larger cohorts would enhance disease classification, improve understanding of GC pathophysiology, and refine risk stratification models for personalized treatment approaches.

Overall, this study contributes to the ongoing efforts to improve patient outcomes by advancing our comprehension of the intricate nature of cancer.

Data availability

The data are available upon request.

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Acknowledgements

We would like to express our sincere gratitude to Dr. Kazem Zendehdel for his invaluable guidance and support throughout the preparation of this study. His expertise in the field of cancer epidemiology has been instrumental in shaping the direction of this work.

Funding

A part of the analysis costs of this study was provided by a grant from the Cancer Biology Research Center, the Cancer Research Center, Tehran University of Medical Sciences (Grant No. 52012).

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Author notes

  1. Haniyeh Rafiepoor and Mohammad M. Banoei contributed equally to this work and co-first authors.

Authors and Affiliations

  1. Cancer Biology Research Center, Cancer Institute, Tehran University of Medical Sciences, Keshavarz Blvd, Building, Tehran, Iran

    Haniyeh Rafiepoor, Alireza Ghorbankhanloo, Ahad Muhammadnejad, Amirhossein Razavirad & Saeid Amanpour

  2. Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada

    Mohammad M. Banoei

  3. Department of Biological Science, University of Calgary, Calgary, AB, Canada

    Mohammad M. Banoei

  4. Cancer Research Center, Cancer Institute, Tehran University of Medical Sciences, Tehran, Iran

    Amirhossein Razavirad

  5. Department of Internal Medicine, Department of Digital Health, Yale School of Medicine, New Haven, CT, USA

    Saeed Soleymanjahi

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Contributions

H.R. contributed to the study concept, data cleaning, statistical analysis interpretation and drafting of the manuscript. M.M.B. contributed to the statistical analysis interpretation, and drafting of the manuscript. A.G. contributed to the interpretation and drafting of the manuscript. A.M. contributed to the study design, data collection, patients follow up and preparation. A.R. contributed to the study concept, collection, patients follow up and data cleaning. S.S contributed to the data collection. S.A. contributed to interpretation and drafting of the manuscript, and overall supervision of the study. All authors read and approved the final manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Saeid Amanpour.

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This study was approved by the ethics committee of Imam Khomeini Hospital Complex- Tehran University of Medical Sciences, Tehran, Iran (No. IR.TUMS.IKHC.REC.1400.001). Written informed consent obtained from all the participants in the manuscript. All experiments were performed in accordance with the Guidelines of Iran National Committee for Ethics in Biomedical Research.

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Rafiepoor, H., Banoei, M.M., Ghorbankhanloo, A. et al. Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification. BMC Cancer 25, 809 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14204-x

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BMC Cancer

ISSN: 1471-2407

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