Development and validation of nomogram of peritoneal metastasis in gastric cancer based on simplified clinicopathological features and serum tumor markers

Gastric cancer is a global health problem, with more than 1 million new cases annually. Although the incidence and mortality of gastric cancer have declined globally in recent years, gastric cancer is still the third leading cause of cancer-related deaths [1, 2]. In recent years, the incidence of gastric cancer has shown a downward trend. However, as a highly invasive and heterogeneous malignant tumor, gastric cancer still maintains a high fatality rate of 75% in most parts of the world, is a major contributor to the global burden of disability-adjusted life years, and remains a global health problem [3‐5].

Noninvasive biomarkers can predict the presence of cancer at the early stage and monitor tumor dynamics in real time, as well as predict its prognosis, and have been increasingly used in clinical practice. Serum tumor markers(STMs) have been widely used to diagnose certain populations and monitor the dynamic changes in cancer, for example, CEA, CA19-9 and CA72-4 are three indicators for the diagnosis of gastric cancer, and their elevation is closely related to the occurrence, recurrence and metastasis of gastric cancer [6‐8]. In addition, an increasing number of studies have demonstrated that clinicopathological features, tumor size, venous invasion, nodal status, overall stage, tumor type, and distant metastasis are closely related to the prognosis of gastric cancer [9, 10].

Gastric cancer metastasis is the main cause of death in patients with advanced gastric cancer, and the most common sites of metastasis are the liver, peritoneum, lung and bone. At the same time, different sites of metastasis are associated with different survival times, so it is necessary to determine the prognosis of tumor metastasis sites [11‐13]. The peritoneum is the most common metastatic site after radical gastrectomy for gastric cancer. Once peritoneal metastasis occurred, the median survival time of patients was only 4 months, while the median survival time of gastric cancer patients without peritoneal metastasis was 14 months [14, 15]. However, peritoneal metastasis is clinically difficult to predict. The accuracy of imaging examinations including computed tomography (CT) and endoscopic ultrasonography (EUS) in diagnosing peritoneal metastasis is not satisfactory [16]. Diagnostic/therapeutic laparoscopy is an effective method for the diagnosis of peritoneal metastasis and is crucial in detecting peritoneal metastasis. However, it is invasive for weak patients who suffer surgical trauma and may place a financial burden on the patient. Therefore, a new model is needed to predict peritoneal metastasis of serous infiltration to formulate effective treatment plans.

In this study, we propose a clinical nomogram method for the preoperative prediction of PM in gastric cancer patients, combining clinical risk factors and radiological characteristics, for individualized preoperative prediction of PM in gastric cancer patients.

In our study, we combined clinical risk factors, radiographic characteristics of primary lesions and radiographic characteristics of peripheral blood to establish a prediction model to predict PM in GC patients before surgery. The results of this study suggest that model II has better predictive efficacy in both the training and validation sets, and DCA demonstrates that this nomogram is very useful for predicting PM in GC patients before surgery.

Currrently, the study of PM in GC patients is one of the GC research hotspots and mainly focuses on molecular biology, machine learning and serum markers. However, no standardized and effective PM prediction scheme has been developed because of the individual differences of patients and the particularity of the disease.

Sawaki et al. identified TNNI2 as a candidate for specific overexpression of gastric cancer prone to PM. The high expression of TNNI2 was significantly and specifically correlated with PM [18]. It can be used as an independent risk marker for peritoneal recurrence after radical gastrectomy, One drawback of this study is that the sample size was small, and further validation is needed. Some studies identified corresponding potential targets for PM prediction by comprehensive analysis of the immune spectrum, and clinical and pathological phenotypes based on total exome sequencing (WES) and total transcriptome sequencing (RNAseq) [19]. Nakanishi et al. combined the determination of SYT13 and CEA mRNA levels in peritoneal irrigation fluid to predict peritoneal recurrence of gastric cancer, and the study revealed that peritoneal recurrence risk was highest in patients with positive SYT13 and CEA mRNA levels [19]. Studies also demonstrated that exosomes play a key role in the progression of gastric cancer, through the analysis of malignant abdominal water exosomes in GC patients, exosomes from ascites in GC patients promote EMT signals in GC cells and mouse peritoneal tumor models to promote PM [20]. Kanda et al. conducted a relapse-pattern-specific transcriptomic analysis of 16 patients with stage III gastric cancer, SYT8 was identified as a candidate biomarker for PM specificity, and the high expression level of SYT8 was found to be significantly and specifically correlated with PM [21]. It can be used as an independent prognostic indicator of peritoneal relapse-free survival in patients with stage II/III gastric cancer.

Some studies proposed a prediction model based on collagen characteristics, and demonstrated that high collagen characteristics were significantly correlated with the risk of PM(P < 0.001), which can be conveniently used to independently predict the risk of PM after radical resection of gastric cancer [22]. Jiang et al. trained a deep convolutional neural network to predict occult PM based on preoperative CT images [23]. The results showed that sensitivity and specificity were high in the external validation cohort, and the differential performance of this model was significantly higher than that of conventional clinicopathological factors. Some studies used a random projection algorithm to develop and optimize a machine learning model based on radiomics to predict the advantages of PM in gastric cancer patients. Studies have demonstrated that the precision, sensitivity and specificity of this model are 65.78%, 43.10% and 87.12%, respectively [24]. Zhou et al. used five machine learning methods to establish a PM model, and found that machine learning combining clinical indicators and serum markers could predict PM in gastric cancer [25].

Serum markers also play an important role in predicting PM in gastric cancer. In a retrospective cohort study of patients with advanced gastric cancer, a high preoperative neutrophil/lymphocyte ratio was associated with the presence of PM during staging laparoscopy [26]. Qin et al. found that the serum IgG glycoprotein profile was high in patients with preoperative PM, and IgG glycan was highly correlated with PM, thus,the IgG model may be a reliable prediction scheme [27]. Studies have demontstrated that serum CEA is significantly correlated with poor prognosis of gastric cancer patients, and high serum CA19-9 levels and peritoneal CEA levels are significant predictors of positive peritoneal flushing cytology and peritoneal cancer development, respectively [28]. Similar to this study, our study comprehensively optimized a preoperative PM prediction model for GC patients by combining various serum markers, clinicopathological features and other indicators, to achieve an accurate preoperative prediction for patients without invasive procedures. In summary, it was found that the prediction of PM of GC requires comprehensive prediction and analysis from multiple levels, and there are often some deficiencies in single-level analysis. In our study, we performed a comprehensive analysis of clinically accessible serological indicators and clinicopathological features to achieve a good predictive efficacy for PM Further studies on PM in patients with gastric cancer are needed to improve the estimation of PM.

In our study, the factors we used to establish the prediction nomogram of PM were easily obtained clinically without any invasive procedure or complicated transformation, and with the combined features, the ability of the model to predict PM was improved. Therefore, the model, can be widely used in the clinic. There are limitations of our study. The main limitation is the retrospective nature of the study. Although this study included patients from two different organizations to assess its reproducibility, this research mainly included patients with gastric cancer, and patients in Western countries. therefore, the model's performance in different ethnic groups also needs further evaluation. While the characteristics of conventional clinical and serological markers were analyzed, and through comparison of different models to generate the best model to predict GC patients with PM, the results indicate that further verification is needed.

In summary, PM is occult for clinical detection in GC patients, and the combined nomogram is an improved model for PM prediction.