Development and validation of a prognostic model based on disulfidptosis-related ferroptosis genes: DRD4 and SLC2A3 as biomarkers for predicting prognosis in colon cancer.

Abstract

Background: Disulfidptosis and ferroptosis are emerging cell death modalities crucial to cancer progression, yet their prognostic potential in colon cancer (CC) remains underexplored. This study develops and validates a prognostic model based on DRD4 and SLC2A3, two genes involved in key biological processes in CC. DRD4 regulates cell proliferation, migration, and apoptosis, while SLC2A3 enhances glucose uptake via the Warburg effect, promoting tumor growth. High expression of both genes is linked to poor prognosis, advanced stages, and increased aggressiveness, enabling precise stratification of patients and accurate prognostic predictions.

Methods: Transcriptomic and clinical data from 476 CC samples and 41 normal colon samples were obtained from The Cancer Genome Atlas (TCGA) database, with 452 patient samples utilized for survival analysis. A training cohort and a validation cohort were generated through random allocation. Disulfidptosis-related ferroptosis genes (DRFGs) were identified using Pearson correlation analysis, and a prognostic model was built using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. External validation was performed using the Gene Expression Omnibus (GEO) datasets (GSE17538 and GSE38832), and clinical samples were further analyzed through immunohistochemistry. Predictors in the nomogram included age, gender, tumor stage, and risk score. The C-index of the final model was used to assess its prognostic accuracy.

Results: The results were validated using external cohorts from the GEO database and immunohistochemistry experiments. A prognostic model incorporating DRD4 and SLC2A3 effectively stratified CC patients into high- and low-risk groups, revealing distinct differences in survival times, immune landscapes, and biological characteristics. High expression levels of DRD4 and SLC2A3 correlated with advanced clinicopathological stages and poor prognosis, with a C-index of 0.75 indicating strong predictive accuracy. Immunohistochemistry confirmed the upregulation of both genes in CC tissues, further validating the model's clinical relevance.

Conclusions: This DRFG-based prognostic model offers an effective tool for predicting clinical outcomes in CC and can guide personalized treatment strategies. The upregulation of DRD4 and SLC2A3 suggests their potential as therapeutic targets. Future studies should focus on elucidating the underlying mechanisms of these biomarkers to enhance their clinical application.