Construction and validation of a nomogram model for predicting peritoneal metastasis in gastric cancer based on ferroptosis-relate genes and clinicopathological features

Abstract

Background: Gastric cancer peritoneal metastasis (GCPM) is a lethal condition. Current diagnostic methods for GCPM, such as imaging and serum tumor markers, lack specificity and sensitivity. Research suggests that utilizing gene signatures to predict GCPM shows significant predictive ability. Nonetheless, the predictability of GCPM using ferroptosis-related genes (FRGs) remains unknown. We aim to construct a nomogram based on FRGs for early diagnosis of GCPM.

Methods: RNA sequencing and clinical data of patients with gastric cancer (GC) were downloaded from Gene Expression Omnibus (GEO) databases. GCPM was diagnosed through imaging, biopsy and cytology. A GCPM prediction model was developed based on six distinctively expressed FRGs, and the efficiency of the model was assessed through receiver operating characteristic (ROC) curves in both experimental and validation cohorts. Subsequently, 115 clinical samples were examined by immunohistochemistry (IHC) to validate the prediction model's accuracy.

Results: Our analysis included 282 patients, among whom 54 had GCPM while 228 did not. Patients were randomly distributed into experimental and validation groups at a 3:2 ratio. Least absolute shrinkage and selection operator (LASSO) regression identified the coefficients of six FRGs, with a risk score calculated for every patient. Univariate and multivariate logistic analyses revealed that both risk score and pathological stage were significantly associated with GCPM. The area under the curve (AUC) values for the training and validating sets implied good predictability for GCPM (0.827 and 0.767, respectively). Combining the risk score with the tumor node metastasis (TNM) stage substantially improved predictability (AUCs were 0.916 and 0.848 respectively). Lastly, a nomogram incorporating the risk score and TNM stage was constructed, which shows good clinical utility through decision curve analysis (DCA). The IHC results from 115 clinical samples were consistent with these findings.

Conclusions: A nomogram model based on FRGs and clinicopathological features was constructed, demonstrating impressive predictive value for GCPM. This enables timely and personalized therapeutic interventions, thereby benefiting gastric cancer patients.

Keywords: Gastric cancer (GC); RRM2; ferroptosis; peritoneal metastasis; predictive signature.

Figure 1

Flowchart showing the process of constructing the FRGs based signature to predict the possibility of peritoneal metastasis in gastric cancer. DEGs, differently expressed genes; FRGs, ferroptosis-related genes; OS, overall survival; LASSO, least absolute shrinkage and selection operator.

Figure 2

Construction of a FRGs signature. (A) PCA of the transcriptome profiles of two groups. (B) GSE62254 datasets after standardization. (C) The Venn plot shows the differently expressed FRGs associated with GCPM. (D) The volcano plot shows the differently expressed genes between PM and NPM. (E) LASSO regression of 10 FRGs genes. (F) Cross validation of parameter selection in LASSO regression. PCA, principal component analysis; FRGs, ferroptosis-related genes; GCPM, gastric cancer peritoneal metastasis; PM, peritoneal metastasis; NPM, no peritoneal metastasis; LASSO, least absolute shrinkage and selection operator; DE-FRGs, differently expressed ferroptosis-related genes.

Figure 3

Univariate Cox analysis of the 10 FRGs with OS. HR, hazard ratio; CI, confidence interval; FRGs, ferroptosis-related genes; OS, overall survival.

Figure 4

Validation of the 6 FRGs-based signature in the training set and the validating set. (A,B) The heatmap of the 6 FRGs expression profiles. (C,D) KM analysis for OS of GC patients based on the risk stratification. (E,F) KM analysis for DFS of GC patients based on the risk stratification. (G,H) ROC analysis for the risk of PM including the risk score, TNM stage and combination of the two. PM, peritoneal metastasis; NPM, no peritoneal metastasis; OS, overall survival; DFS, disease-free survival; ROC, receiver operating characteristic curve; FRGs, ferroptosis-related genes; AUC, areas under the curve; TNM, tumor node metastasis; KM, Kaplan-Meier; GC, gastric cancer.

Figure 5

Univariate logistics analysis of the 6 FRGs-based signature with peritoneal metastasis. HR, hazard ratio; CI, confidence interval; pStage, pathological stage; FRGs, ferroptosis-related genes.

Figure 6

Multivariate logistics analysis of the 6 FRGs-based signature with peritoneal metastasis. HR, hazard ratio; CI, confidence interval; pStage, pathological stage; FRGs, ferroptosis-related genes.

Figure 7

Nomogram to predict the risk of GCPM. (A) Nomogram to predict the risk of GCPM. (B) DCA of the nomogram in training set. (C) DCA of the nomogram in validation set. pStage, pathological stage; PM, peritoneal metastasis; GCPM, gastric cancer peritoneal metastasis; DCA, decision curve analysis.

Figure 8

Pathway enrichment analysis of the related DEGs. (A) Dotplot of GO enrichment analysis. (B) Dotplot of KEGG enrichment analysis. (C,D) Pathways enriched by Metascape. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix; IL, interleukin; BP, biological process; CC, cellular component; MF, molecular function; DEGs, differently expressed genes.

Figure 9

Validation of the 6 FRGs in clinical samples. (A-F) IHC staining and expression of the 6 FRGs in primary GC lesions. Left-side images: 100× magnification; right-side images: 200× magnification. GC, gastric cancer; GCPM, gastric cancer peritoneal metastasis; NPM, no peritoneal metastasis; PM, peritoneal metastasis; FRGs, ferroptosis-related genes; IHC, immunohistochemistry.