Discussion
Recent studies have shown that metabolic pathways play an important role in regulating tumor progression [8,9]. The survival and proliferation of cancer cells depends on metabolic reprogramming [10]. Many studies have reported the possibility of metabolic pathways as tumor-targeted treatments. Specific metabolic activities can directly affect the transformation process or proliferation process, which is the biological process of tumor growth [11]. Abdel-Wahab and other reports pointed out that controlling glucose metabolism may be a new way to inhibit cancer progression [12,13]. Recent studies have shown that microbial metabolites, such as secondary bile acids, can promote cancer. The metabolism of intestinal microbes related to cancer and diets rich in fat and meat, and extracellular metabolism can promote cancer progression [14]. However, the basic mechanism of metabolism in COAD has not been fully elucidated, which hinders the targeted therapy of metabolism. Therefore, the discovery of new molecular markers related to the prognosis of COAD is very important. In this study, based on LASSO COX regression analysis, we identified 16 PRMG in the TCGA-COAD and GSE40976 data sets to construct a prognostic model for COAD patients and determine the risk score. The prognostic model is accurate and accurate. Kaplan–Meier analysis proved that the risk score model can predict the overall survival rate of COAD. Univariate and multivariate regression analysis confirmed that risk score is an independent prognostic factor for COAD. The AUC curve of the gene confirms that the risk score has a good prognostic value in predicting overall survival. The C-index of the nomogram was 0.732. DCA shows that the nomogram prediction model has a higher clinical benefit rate than the TNM staging system. Many enrichment analysis pathways are concentrated in metabolic pathways. In addition to metabolic pathways, the high-risk group shows some cancer-related pathways, such as antigen processing and presentation, basal transcription factors, endometrial cancer, glycolysis gluconeogenesis, erbb signal pathway, and glycosylphosphatidylinositol gpi anchor biosynthesis . These results show that these genes are closely related to metabolic pathways and reveal the potential role of metabolic pathways in COAD.
Target genes are important members of metabolic pathways and can serve as therapeutic targets for cancer. Prognosis prediction is very important for selecting clinical treatment options for cancer patients. Several studies have explored prognostic biomarkers and found that gene expression profiles play a crucial role in the prognosis of cancer [15]. Although our screening of these genes related to cancer prognosis is rarely reported, these genes can reflect the status of cancer driver genes related to their upstream and downstream to a certain extent. The genes we screened are rich in a variety of cancer-related pathways. Based on these results, we concluded that the risk score can accurately predict the survival of patients with COAD, perhaps because the score can reflect the multi-level status of COAD. We constructed a nomogram to predict individualized clinical outcomes. The nomogram generates a graphical statistical prediction model that assigns scores to each factor, including age, gender, and clinical stage, covering important factors that affect clinical outcomes. In addition to traditional clinicopathological characteristics (such as age, gender, TNM staging), risk scores based on genetic markers can also be incorporated into the predictive nomogram model to predict clinical outcomes. The nomogram is a stable and reliable quantification of personal risk by combining clinical characteristics and risk scores. Our nomogram includes risk scores and clinicopathological characteristics, which can well predict patients with colon adenocarcinoma at 1, 2, and 3 years survival rate. The calibration curve for predicting OS indicated that the nomogram-predicted survival closely corresponded with actual survival outcomes. We constructed 16 metabolic gene models based on TCGA and GEO to predict the prognosis of COAD patients. The risk score based on 16 genes may be a promising independent prognostic biomarker. However, these are not yet clear. How genes play their roles in the mechanism, therefore, more research is needed to explore the impact of metabolic enzymes on survival. The study has limitations. First of all, this is a retrospective study. Therefore, information including recurrence time, treatment records and detailed pathological staging cannot be obtained. Second, although the model has been validated in all cohorts, it still needs more samples for further confirmation before clinical application.