# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
## 4 Discussion  
The current incidence and mortality rates of gastric cancer (GC) are on the rise, with a concerning trend of increasing proportions and younger age of onset [18]. Researchers have found that iron metabolism is altered in GC cells, making iron vital for their growth and survival [19]. This finding offers a new perspectives for GC prevention and treatment, as well as opportunities for studying FRGs in GC. However, GC datasets usually have large samples and high dimensions, making it challenging to identify FRGs that directly impact patient outcomes from these datasets. Therefore, the selection of relevant and  
Figure 5: The expression characteristics of 7 P-FRGs in GC. (**A**) _ALB_. (**B**) _BNIP3_. (**C**) _DPEP1_. (**D**) _GLS2_. (**E**) _PDK4_. (**F**) _TF_. (**G**) _TSC22D3_. informative genes becomes crucial. Eliminating irrelevant, redundant, and noisy genes can reduce the computational burden of the classifier and enhance their accuracy. Additionally, this gene selection process facilitates the validation of selected genes in molecular biology experiments [20]. In this study, we used P-WGCNA and EP-WGCNA to select genes that are closely related to phenotype. Then, we further used univariate and multivariate Cox regression analyses and KM curves to calculate the correlation between these genes and GC prognosis. Then, we identified nine P-FRGs (_ALB_, _AR_, _BNIP3_, _CBS_, _CDC25A_, _FABP4_, _MEG3_, _MYB_ and _PDK4_) based on P-WGCNA-Cox. Eight P-FRGs (_ALB_, _BNIP3_, _DPEP1_, _GLS2_, _MEG3_, _PDK4_, _TF_, and _TSC22D3_) were identified based on EP-WGCNA-Cox. Risk scoring models based on nine and eight P-FRGs were constructed, respectively. The KM curve showed that the survival rate of the low-risk group divided based on the median risk score was significantly higher than that of the high-risk group. TimeROC showed that the prognostic model constructed by P-FRGs based on the EP-WGCNA-Cox method was reliable and superior to P-WGCNA-Cox in predicting the 5-year survival of patients, which was also verified in the validation set GSE84426. In addition, we used GO functional enrichment analysis and the HPA database to further study the eight P-FRGs. It was found that eight P-FRGs were significantly enriched in ferric iron binding (GO: 0008199), blood microparticle (GO: 0072562), reactive oxygen species metabolic process (GO: 0072593) and so on. Relevant studies have shown that these pathways are important factors in the occurrence and development of GC and the occurrence of ferroptosis [21; 22; 23]. According to the degree of immunohistochemical staining and the quantity of stained cells staining from HPA, seven P-FRGs (_ALB_, _BNIP3_, _DPEP1_, _GLS2_, _PDK4_, _TF_, and _TSC22D3_) were significantly differentially expressed in the adjacent non-tumor gastric tissues and tumor tissues. In the past, clinical characteristics such as age, TNM stages, and some serum markers were commonly used to predict the prognosis of GC patients. However, due to individual differences, these single factors may not provide sufficient accuracy or effectiveness in improving treatment options [24]. With the development of molecular biology and genomics, researchers have found that cancer is a genetic disease and have proposed therapies targeting specific genes [25]. Ferroptosis has been proven to be closely related to the proliferation, invasion, metastasis, and drug resistance of GC [26], but its connections to the prognosis of GC patients remains unclear. In this context, we constructed a new prognostic model (EP-WGCNA-Cox) based on eight FRGs and found it is more reliable in predicting the survival rate of GC patients than P-WGCNA-Cox. In addition, compared with other studies, the EP-WGCNA-Cox model also has some advantages. For example, [PERSON] et al. [27] and [PERSON] et al. [28] found the 5-year survival rate of GC patients was 0.668 and 0.713, respectively, using LASSO-Cox. [PERSON] et al.