Novel nomogram to predict the overall survival of postoperative patients with gastric signet.

BACKGROUND: The TNM staging system cannot accurately predict the prognosis of postoperative gastric signet ring cell carcinoma (GSRC) given its unique biological behavior, epidemiological features, and various prognostic factors. Therefore, a reliable postoperative prognostic evaluation system for GSRC is required. This study aimed to establish a nomogram to predict the overall survival (OS) rate of postoperative patients with GSRC and validate it in the real world. METHODS: Clinical data of postoperative patients with GSRC from 2002 to 2014 were collected from the Surveillance, Epidemiology, and End Results database and randomly assigned to training and internal validation sets at a 7:3 ratio. The external validation set used data from 124 postoperative patients with GSRC who were admitted to the Affiliated Tumor Hospital of Harbin Medical University between 2002 and 2014. The independent risk factors affecting OS were screened using univariate and multivariate analyses to construct a nomogram. The performance of the model was evaluated using the C-index, receiver operating characteristic curve (ROC), calibration curve, decision analysis (DCA) curve, and adjuvant chemotherapy decision analysis. RESULTS: Univariate/multivariate analysis indicated that age, stage, T, M, regional nodes optimized (RNE), and lymph node metastasis rate (LNMR) were independent risk factors affecting prognosis. The C-indices of the training, internal validation, and external validation sets are 0.741, 0.741, and 0.786, respectively. The ROC curves for the first, third, and fifth years in three sets had higher areas under the curves, (training set, 0.782, 0.864, 0.883; internal validation set, 0.781, 0.863, 0.877; external validation set, 0.819, 0.863, 0.835). The calibration curve showed high consistency between the nomogram-predicted 1-, 3-, and 5-year OS and the actual OS in the three queues. The DCA curve indicated that applying the nomogram enhanced the net clinical benefits. The nomogram effectively distinguished patients in each subgroup into high- and low-risk groups. Adjuvant chemotherapy can significantly improve OS in high-risk group (P = 0.034), while the presence or absence of adjuvant chemotherapy in low-risk group has no significant impact on OS (P = 0.192). CONCLUSIONS: The nomogram can effectively predict the OS of patients with GSRC and may help doctors make personalized prognostic judgments and clinical treatment decisions.

SMYD Family Members Serve as Potential Prognostic Markers and Correlate with Immune Infiltrates in Gastric Cancer.

Background: The SMYD family comprises a group of genes encoding lysine methyltransferases, which are closely related to tumorigenesis. However, a systematic understanding of their role in gastric cancer (GC) is lacking. Methods: Using databases and tools such as the Cancer Genome Atlas, Human Protein Atlas, Kaplan-Meier Plotter, Gene Expression Profiling Interactive Analysis, and Metascape, we comprehensively analyzed differences in SMYD expression and its prognostic value as well as the association of SMYDs with immune cell infiltration, tumor mutational burden (TMB), and microsatellite instability (MSI). We conducted functional enrichment analysis and explored a competing endogenous RNA mechanism regulating SMYD mRNA and protein levels in patients with GC. Results: In GC, the expression of SMYD2/3/4/5 mRNA was significantly upregulated, as opposed to that of SMYD1 mRNA, which was significantly downregulated. The protein levels of SMYDs were consistent with mRNA levels. SMYD1/2/4/5 was negatively correlated with overall survival; SMYD1/2/3/5 was negatively correlated with progression-free survival. Our SMYD-based signature and nomogram model may be useful for inferring the prognosis of GC. All SMYDs were closely associated with the infiltration of six immune cell types: uncharacterized, CD8+ T, CD4+ T, macrophage, endothelial, and B cells. TMB was significantly negatively correlated with SMYD1 expression, while a significant positive correlation was observed with SMYD2/5. Furthermore, MSI was significantly positively correlated with SMYD2/5 expression. Long non-coding RNAs, such as chr22-38_28785274-29006793.1, XLOC_002309, and CTD-2008N3.1, were suggested to regulate SMYD expression by sponging multiple microRNAs. Conclusion: SMYDs are differentially expressed in GC and are thus potential prognostic markers. SMYD expression is closely related to immune infiltration, TMB, and MSI, all of which are closely related to the response to targeted immune therapy.

Machine Learning-Based Model for the Prognosis of Postoperative Gastric Cancer.

BACKGROUND: The use of machine learning (ML) in predicting disease prognosis has increased, and scientists have adopted different methods for cancer classification to optimize the early screening of cancer to determine its prognosis in advance. In this study, we aimed at improving the prediction accuracy of gastric cancer in postoperation patients by constructing a highly effective prognostic model. METHODS: The study used postoperative gastric cancer patient data from the SEER database. The LASSO regression method was used to construct a clinical prognostic model, and four machine learning methods (Boruta algorithm, neural network, support vector machine, and random forest) were used to screen and recombine the features to construct an ML prognostic model. Clinical information on 955 postoperative gastric cancer patients collected from the Affiliated Tumor Hospital of Harbin Medical University was used for external verification. RESULTS: Experimental results showed that the AUC values of 1, 3 and 5 years in the training set, validation set and external validation set of clinical prognosis model and ML prognosis model directly established by LASSO regression are all around 0.8. CONCLUSION: Both models can accurately evaluate the prognosis of postoperative patients with gastric cancer, which may be helpful for accurate and personalized treatment of postoperative patients with gastric cancer.