# A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis
## 2 Materials and Methods  
### Data Source and Preprocessing  
We downloaded RNA sequencing data (HTSeq-Counts) of 407 GC samples (adjacent non-tumor: 32, tumor: 375) and corresponding clinical data from the TCGA database (TCGA-STAD, [[https://xenabrowser.net/datapages](https://xenabrowser.net/datapages)]([https://xenabrowser.net/datapages](https://xenabrowser.net/datapages)) (accessed on 25 April 2022)). To balance the number of tumor and non-tumor tissue samples, we also downloaded gene expression data of 174 normal gastric tissue samples from the GTEx database ([[https://xenabrowser.net/datapages](https://xenabrowser.net/datapages)]([https://xenabrowser.net/datapages](https://xenabrowser.net/datapages)) (accessed on 30 April 2022)) and combined them with the adjacent non-tumor tissue samples [15]. We used this combined dataset of TCGA-GTEx as the training set (all data were generated by Illumina platform). For validation, we used the GSE84426 dataset from the GEO database. On 12 August 2022, we downloaded 564 FRGs from the FerrDB database [16]. We extracted probe information from the annotation file to annotate the genes for both TCGA and GTEx data. Then, we classified the tumor and non-tumor tissues based on clinical information. We merged the tumor, non-tumor, and normal tissues by gene names. We normalized the sequencing depth and removed batch effects. This resulted in a new dataset, with sample name as row name and gene name as column name, including 581 GC samples (normal: 206, tumor: 375) and 57,793 genes. Table 1 shows the clinical pathological features of patients in TCGA-STAD and GSE84426. Some patients had incomplete clinical data, so not all information is shown in Table 1.  
We used R (version 4.2.1) language to filter out genes with low expression in the TCGA-GTEx dataset (quantitative analysis was performed by count-per-million first, and then those whose gene value was less than 1 were filtered out) and conducted a Trimmed Mean of M-values (TMM) normalization of the data. Next, we screened the differentially expressed genes (DEGs) in the dataset using empirical Bayesian estimation and exact test based on the negative binomial model, with \(\mid\)logFC\(\mid\)\(>\) 1 and padj \(<\) 0.05 as screening criteria. We obtained the padj using the Benjamini-Hochberg (BH) method. Finally, we took the genes shared by DEGs and FRGs and noted them as DE-FRGs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Characteristics**} & \multicolumn{1}{c}{**TCGA-STAD (_n = 375_)**} & \multicolumn{1}{c}{**GSE84426 (_n = 76_)**} \\ \hline \multirow{2}{*}{**Age**} & \(\leq\)65 & 164 (43.73\%) & 22 (28.95\%) \\  & \(>\)65 & 208 (55.47\%) & 54 (71.05\%) \\ \hline \multirow{2}{*}{**Gender**} & Male & 239 (63.73\%) & 54 (71.05\%) \\  & Female & 136 (36.27\%) & 22 (28.95\%) \\ \hline \multirow{2}{*}{**Stage**} & I & 36 (9.60\%) & – \\  & II & 69 (18.40\%) & – \\  & III & 95 (25.33\%) & – \\  & IV & 22 (5.87\%) & – \\ \hline \multirow{2}{*}{**T stage**} & T1 & 14 (3.73\%) & 0 \\  & T2 & 51 (13.60\%) & 3 (3.95\%) \\  & T3 & 109 (29.07\%) & 25 (32.90\%) \\  & T4 & 59 (15.73\%) & 48 (63.15\%) \\ \hline \multirow{2}{*}{**N stage**} & N0 & 67 (17.87\%) & 9 (11.84\%) \\  & N1 & 65 (17.33\%) & 33 (43.42\%) \\  & N2 & 50 (13.33\%) & 33 (43.42\%) \\  & N3 & 45 (12.00\%) & 1 (1.32\%) \\ \hline \multirow{2}{*}{**M stage**} & M0 & 213 (56.80\%) & – \\  & M1 & 12 (3.20\%) & – \\ \hline \multirow{2}{*}{**Survival status**} & Alive & 230 (61.33\%) & 41 (53.95\%) \\  & Dead & 145 (38.67\%) & 34 (44.74\%) \\ \hline \end{tabular}  
* Some patients had incomplete clinical data, so the table did not show information for all patients.  
\end{table}
Table 1: Clinical characteristics of the GC patients included in this study.