GEPIA performs survival analysis based on gene expression levels (Figure 2D ). This function allows users to select their custom cancer types for overall or disease-free survival analysis. For example, to examine the survival curves of an input gene in lung cancer, a user can select lung squamous cell carcinoma (LUSC) only or choose both LUSC and lung adenocarcinoma for the survival analyses. GEPIA uses log-rank test, sometimes called the Mantel–Cox test, for the hypothesis evaluation. The cox proportional hazard ratio and the 95% confidence interval information can also be included in the survival plot. The thresholds for high/low expression level cohorts can be adjusted.
For survival analysis, GEPIA also provides a gene normalization feature that allows the relative expression of two different genes as input. For example, when investigating gene FOXP3 in cancer survival analysis, users can also input another gene such as CD3G to normalize the expression of FOXP3. In such case, GEPIA will perform the survival analysis based on the FOXP3/CD3G relative expression levels. Furthermore, GEPIA can also present top genes that are most associated with cancer patient survival. The gene list is ranked by P-values of survival analysis based on any input cancer types.
For survival analysis, GEPIA also provides a gene normalization feature that allows the relative expression of two different genes as input. For example, when investigating gene FOXP3 in cancer survival analysis, users can also input another gene such as CD3G to normalize the expression of FOXP3. In such case, GEPIA will perform the survival analysis based on the FOXP3/CD3G relative expression levels. Furthermore, GEPIA can also present top genes that are most associated with cancer patient survival. The gene list is ranked by P-values of survival analysis based on any input cancer types.
