# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
## 1 Introduction  
Gastric cancer (GC) is a prevalent and deadly malignancy worldwide. According to the recent statistics from the GLOBOCAN database, it ranks fifth in incidence and fourth in mortality in the world [1]. Surgery is the primary and most effective treatment for GC patients, which can significantly improve their survival rate [2]. However, the survival rate remains constrained by low early diagnosis rate [3] and poor postoperative recovery [4]. Therefore, there is an urgent need to identify novel biomarkers for GC screening, diagnosis, prognosis, and therapy. Biomarkers are molecules that can facilitatedisease screening, diagnosis, characterization, and monitoring [5], and they are more convenient and economical than the direct measurement of the final clinical endpoint in clinic [6]. Effective biomarkers can assist in finding optimal individualized treatment approaches for GC patients. Ferroptosis is a novel form of programmed cell death that depends on iron and involves free lipid peroxidation and plasma membrane rupture. Unlike apoptosis, cell necrosis, and autophagy, ferroptosis has distinct features and mechanisms [7]. Its main characteristics are shown in two aspects: cell morphology and cellular component. Ferroptosis leads to smaller mitochondria, increased membrane density, and a ridge reduction in cells. Ferroptosis also increases lipid peroxidation and reactive oxygen species (ROS) in cells. The main mechanism of ferroptosis is to catalyze the highly expressed unsaturated fatty acids on the cell membrane under the action of ferric or ester oxygenase. This results in lipid peroxidation, thus inducing cell death. In addition, ferroptosis decreases the expression of the antioxidant system (glutathione GSH and glutathione peroxidase 4-GPX4) [7]. Many studies have revealed that ferroptosis-related genes (FRGs) are potential prognostic biomarkers for cancer. For example, [PERSON] et al. [8] constructed a ferroptosis-related risk score (FRRS) using random forest and principal component analysis on 1328 samples and 36 FRGs. They validated the FRRS in three independent datasets and showed that it could predict the prognosis and immunotherapy response of lung adenocarcinoma patients. Similarly, [PERSON] et al. [9] identified 25 FRGs and methylation genes that correlated with the survival of renal clear cell carcinoma patients using bioinformatics methods. Moreover, [PERSON] et al. [10] reported that a low expression of two FRGs was associated with poor outcomes of adrenocortical carcinoma patients. However, the role of these FRGs in GC prognosis remains unclear. Weighted gene co-expression network analysis (WGCNA) is a widely used and powerful network analysis method for identifying key genes in tumor datasets. It organizes genes with similar functions and regulatory mechanisms into common modules and explores the relationship between these modules and biological processes. WGCNA analysis is based on two assumptions: (1) Genes with similar expression patterns may have co-regulation, functional relevance or be in the same pathway; (2) Gene networks follow a scale-free distribution. Based on these two points, we can divide gene networks into different modules according to expression similarity and then identify hub genes [11]. WGCNA has been widely used in the predicting research of cancer prognosis-related genes. However, the classical WGCNA methods usually measure linear similarity between genes based on the Pearson correlation coefficient (denoted as P-WGCNA). This measure can only capture the local variable trends between two gene expression levels and may lose some necessary information for complex structured datasets. Therefore, [PERSON] et al. [12] proposed a new similarity measure method--EP_dis--that combined Euclidean distance and Pearson distance by a weighted method. EP_dis captures both global spatial correlation and local variation trend between data points.