In this panel, you can compare gene expression-survival relationship between patients with and without specific drug treatment. Some gene signature lists are provided.

GEPIA performs Overall Survival (OS) or Progression-free Interval (PFI) analysis based on gene expression. GEPIA uses Log-rank test, a.k.a the Mantel-Cox test, for hypothesis test. Cohorts thresholds can be adjusted. The cox proportional hazard ratio and the 95% confidence interval information can also be included in the survival plot.

Parameters:

• Gene: Input a gene/isoform or gene signature A of interest.
• Methods: Select the OS or PFS survival response.
• Axis Units: Select Month or Day unit for plotting.
• Datasets Selection: Select one or multiple cancer types of interest in the "Dataset Selection" field and click "add" to build dataset list in the "Datasets" field.
• Colors: Choose color to plot KM curves
• Group Cutoff: Select a suitable expression threshold for splitting the high-expression and low-expression cohorts.
• Cutoff-High(%): Samples with expression level higher than this threshold are considered as the high-expression cohort.
• Cutoff-Low(%): Cutoff-Low(%): Samples with expression level lower than this threshold are considered the low-expression cohort.

Results:

• The result table shows the results of Cox regression analysis for gene expression levels and patient survival between the drug and non-drug groups. Click the button beside drug names to calculate the survival differences between the four patient groups: high expression with the drug, low expression with drug, high expression without drug, and low expression without drug. Kaplan-Meier (KM) survival curves will be generated for each pairwise comparison.

Warnings:

• Both drug generic name and trade name are provided. See drug name standarization rules.
• Only patients with drug treatment information are included. Thus the group without using selected drug refers to patients using other drugs.
• Since special treatment of blood tumor are not included (e.g. stem cell transplantation in DLBC), be careful to combine blood tumor and solid tumor into a uniformed dataset.
• Notice that there may not be sufficient cases for survial anaysis in single or limited cancer types.