Risk Assessment and Preventive Treatment for Peritoneal Recurrence Following Radical Resection for Gastric Cancer

Vagus nerve signal has an inhibitory influence on the development of peritoneal metastasis in murine gastric cancer

The vagus nerve is the only pathway for transmitting parasympathetic signals between the brain and thoracoabdominal organs, thereby exhibiting anti-inflammatory functions through the cholinergic anti-inflammatory pathway. Despite often being resected during lymph node dissection in upper gastrointestinal cancer surgery, the impact of vagotomy on postoperative outcomes in gastric cancer patients remains unclear. Sub-diaphragmatic vagotomy was performed on C57BL/6 mice. Three weeks later, syngeneic murine gastric cancer cell line YTN16P was injected into the peritoneal cavity, and the number of peritoneal metastases (PM) on the mesentery and omentum compared with control mice. The phenotypes of immune cells in peritoneal lavage and omental milky spots one day after tumor inoculation were analyzed using flow cytometry and immunohistochemistry. Intraperitoneal transfer of 3 × 105 YTN16P significantly increased the number of metastatic nodules on the mesentery in the vagotomy group compared to the control group. The omental metastasis grade was also significantly higher in the vagotomy group. Phenotypic analysis of immune cells in peritoneal lavage did not reveal significant differences after vagotomy. However, vagotomized mice exhibited a notable increase in milky spot area, with a higher presence of cytokeratin(+) tumor cells, F4/80(+) macrophages, and CD3(+) T cells. Vagus nerve signaling appears to regulate the immune response dynamics within milky spots against disseminated tumor cells and inhibits the development of PM. Preserving the vagus nerve may offer advantages in advanced gastric cancer surgery to reduce peritoneal recurrence.

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis

Background

Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment.

Methods

Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models.

Results

In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection.

Conclusion

The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Platelet-Derived Growth Factors Affect Clinical Features and Prognosis of Gastric Cancer

Purpose. To investigate the association of platelet-derived growth factors (PDGFs), clinicopathological features, and prognosis in gastric cancer patients. Methods. Tumor specimens of 180 individuals with gastric cancer treated between 2016 and 2020 were collected. Immunohistochemical staining and Western blot (WB) were used to detect the expression of PDGF-B and PDGF-D. The relationship between the expression of PDGF-B and PDGF-D and relapse-free subsistence (RFS) time was assessed using Kaplan–Meier curves. Univariate and multivariate Cox proportional hazards regression models were used to evaluate the relationship between the expression of PDGF-B and PDGF-D and the prognosis and clinicopathological features in gastric cancer patients. Results. High expression of PDGF-B and PDGF-D was detected in 108 (60%) and 137 (76%) tumor specimens, respectively. The expressions of PDGF-B and PDGF-D were independent predictive indicators in multivariate analysis when compared to tumor depth, tumor stage, lymph node metastasis, and RFS ( $P<0.01$ ). Conclusion. The high expression of PDGF-B and PDGF-D in gastric cancer tissues is associated with poor prognosis and poor survival rate of the patients. The expression of PDGF-B and PDGF-D can be used as important indicators to evaluate the biological behavior and prognosis of gastric cancer.