ArticleSaudi medical journal2025
Construction and validation of a colorectal cancer diagnostic model based on ferroptosis-related genes.
Article in Saudi medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo establish a ferroptosis-related colorectal cancer (CRC) diagnostic model by integrating machine learning and gene expression analysis. Tumorigenesis is strongly linked to ferroptosis, an iron-mediated kind of cell death. The dismal prognosis of CRC, a severely malignant gastrointestinal cancer, accentuates the demand for effective diagnostic biomarkers.
methodsThe study was performed between January 2024 and 2025. The GEO database provided 2 openly searchable gene expression profiles (GSE9348 and GSE21510) from CRC as well as non-tumor tissues. Genes that were expressed differently in tumor and healthy tissues were found using the GSE9348 dataset. Distinct genetic biological functions were identified through functional enrichment analysis. SVM-RFE and LASSO regression models helped identify potential genetic markers associated with CRC.
resultsGSE9348 dataset analysis helped identify 27 differentially expressed ferroptosis-related genes. KEGG analysis suggested that these genes are primarily related to inflammatory responses, NF-κB signaling, and regulation of the interleukin family. Based on this model, CHMP2A, CYCS, HMGB1, IL18, IL1B, and GZMA were selected as potential diagnostic markers, and a novel diagnostic model was constructed. Its predictive value was examined using receiver operating characteristic analysis. We validated the expression changes of model genes using PCR assays along with a validation set (comprising TCGA and GSE21510 datasets).
conclusionThese outcomes provide an efficient ferroptosis-related gene-based diagnostic model for CRC. Nevertheless, before its use in real-time settings, more clinical studies are required to confirm its diagnostic value.
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