What is the KM plotter?

The Kaplan Meier plotter is capable to assess the effect of 54k genes (mRNA, miRNA, protein) on survival in 21 cancer types including breast (n=7,830), ovarian (n=2,190), lung (n=3,452), and gastric (n=1,440) cancer. Sources for the databases include GEO, EGA, and TCGA. Primary purpose of the tool is a meta-analysis based discovery and validation of survival biomarkers.

mRNA gene chip

mRNA RNA-seq








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For a general citation of the KM-plotter, please use: Győrffy B. Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer, Computational and Structural Biotechnology Journal, 2021;19:4101-4109, https://doi.org/10.1016/j.csbj.2021.07.014.


What can the KM plotter do?


How does it work?

The background database is manually curated. Gene expression data and relapse free and overall survival information are downloaded from GEO, EGA and TCGA. The database is handled by a PostgreSQL server, which integrates gene expression and clinical data simultaneously. To analyze the prognostic value of a particular gene, the patient samples are split into two groups according to various quantile expressions of the proposed biomarker. The two patient cohorts are compared by a Kaplan-Meier survival plot, and the hazard ratio with 95% confidence intervals and logrank P value are calculated. Databases and clinical data are supervised and extended regularly.

Where is the "Show probe expression in normal tissue" option?

We developed a new analysis portal for the comparison of normal and tumor samples utilizing 63000+ samples. Access here the TNM plotter.

Which gene ID can I use?

KM-plot recognizes 70,632 gene symbols (including HUGO Gene Nomenclature Committee approved official gene symbols, previous symbols and aliases - all these are listed in the results page). As the different names can overlap, we recommend to cross-check the identity of the selected gene.

I have selected gene XXX but the results are for gene YYY - why is this?!

The problem is that the genes symbols are not unambiguous and the HUGO database we use for the selection of the probe sets also includes overlapping gene symbols. Please read following example to understand the phenomena: let's assume we want to measure EPHA3. Once we start typing EPHA3, the system suggests a probe set: 206070_s_at. Now, in case the all probe sets per gene is enabled, the system looks up all gene symbols for 206070_s_at. These are EPHA3, ETK1, HEK4, TYRO4, ETK, and HEK. As all probe sets should be included, the system looks up all 21 probe sets linked to any of these symbols. Finally, at the end, all gene symbols are listed on the results page.

I have several candidates. How can I select the reliable ones?

You need to correct for multiple testing. For this, use our multiple testing calculator.

Can I have a better image?

There are four options: 1) utilize the scalable PDF provided at the results; 2) adjust "Settings" to generate a hi-res TIFF file; 3) use our powerpoint template to change font and the text size; 4) still not satisfied?! -> adjust "Settings" to export plot data as text and format it in any other software.

Can I use multiple genes?

Yes. Click on the button "Use multiple genes" and enter multiple genes. You can run the analysis on all these biomarkers simultaneously (default setting), or using the mean expression of the genes. For this, tick the "Use mean expression of the selected probes" radio button. Maximum 65 genes are allowed.

Can I check response to chemotherapy?

To validate predictive biomarkers, we suggest the ROC plotter, available at the ROCplot website.

Are microarrays and RNA-seq datasets combined?

No way! In one analysis, one platform is included only, because this enables to measure the same gene with the same sensitivity, specificity and dynamic range.

Can I use mutation or copy number alterations?

To utilize mutation or CNV data, try our online platform Genotype 2 Outcome, available at the G-2-O website.

Want to predict survival for a single patient?

Try Recurrence Online, a tool capable to predict response to hormonal treatment, to targeted therapy and survival (recurrence score) for breast cancer patients using gene expression data obtained by Affymetrix gene chips.

The KM-plotter has been utilized among others in studies published in:

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