AUTHOR=Zheng Yang , Wu Rilige , Wang Ximo , Yin Chengliang 

TITLE=Identification of a Four-Gene Metabolic Signature to Evaluate the Prognosis of Colon Adenocarcinoma Patients

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.860381

DOI=10.3389/fpubh.2022.860381

ISSN=2296-2565

ABSTRACT=Background: Colon adenocarcinoma (COAD) is a highly heterogeneous disease, thus making prognostic predictions uniquely challenging. Metabolic reprogramming is emerging as a novel cancer hallmark that may serve as the basis for more effective prognosis strategies. 
Methods: We acquired the mRNA expression profiles and relevant clinical information of COAD patients from public resources. The least absolute shrinkage and selection operator (LASSO) Cox regression model was exploited to establish a prognostic model, which was performed to gain risk scores for multiple genes in the cancer genome atlas (TCGA). COAD patients from the GSE39582 cohort were exploited for validation. A forest plot and nomogram were constructed to visualize the data. The clinical nomogram was calibrated using a calibration curve coupled with decision curve analysis (DCA). The associations between the model genes’ expression and six types of infiltrating immunocytes were evaluated. Apoptosis, cell cycle assays and cell transfection experiments were performed. 
Results: Our univariate Cox regression analysis results indicated that ten differentially expressed genes (DEGs) were related with disease-free survival (DFS) (P< 0.01). A four-gene signature was developed to classify patients into high- and low-risk groups. And compared with the low-risk group, patients with high-risk exhibited obviously lower DFS in the training and validation cohorts (P < 0.05). The risk score was an independent parameter of DFS in the multivariate Cox regression analyses in the training cohort (HR> 1, P< 0.001). The same findings for overall survival (OS) were obtained in the validation cohort. The predictive capacity of the signature was verified by the receiver operating characteristic (ROC) curve analysis. Gene Ontology (GO) enrichment analysis revealed several metabolic pathways with significant DEGs enrichment, G1/S transition of mitotic cell cycle, CD8+ T-cells and B-cells may be significantly associated with COAD in DFS and OS. These findings demonstrate that si-FUT1 inhibited cell migration and facilitated apoptosis in COAD. 
Conclusion: This research reveals that a novel metabolic gene signature could be used to evaluate the prognosis of COAD, and targeting metabolic pathways may serve as a therapeutic alternative.