214P - PESSA: A shiny app for pathway enrichment score-based survival analysis in cancer

Background

The activation levels of biologically significant gene sets are innovative molecular markers for tumours and play an irreplaceable role in the diagnosis, staging, prognosis evaluation and treatment of tumours; however, there is still a scarcity of web-based tools for prognosis analysis that use gene set activation levels as molecular markers for tumours. We have developed a web-based tool for survival analysis using gene set activation levels.

Methods

All data analysis within PESSA is implemented via R. Activation levels of MSigDB gene sets were assessed using the single-sample gene set enrichment analysis (ssGSEA) method based on microarray and high-throughput sequencing data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). PESSA was used to perform median and best cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival-related analysis and visualisation. The web pages were built relying on Shiny.

Results

PESSA is an open-access online tool for visualising prognostic analysis based on the activation levels of biologically significant gene sets as molecular markers of tumour prognosis. A total of 214 datasets from the GEO and TCGA covering 51 different cancer types and 13 different survival status types were included; 13,434 tumour-related gene sets were obtained from the MSigDB for pre-grouping. For survival analyses, the gene set of interest was selected; Kaplan‒Meier analyses and visualisation were conducted based on the median and best dichotomous cut-off, plus continuous variables were subjected to univariate Cox regression analyses.

Conclusions

PESSA (https://smuonco.shinyapps.io/PESSA/) is a large, interactive, easily accessible web-based analysis tool covering a large amount of data; it can creatively use predefined gene set activation levels as molecular markers of tumours and facilitate survival analysis for cancer patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

The Natural Science Foundation of Guangdong Province [2018A030313846, 2021A1515012593]; the Science and Technology Planning Project of Guangdong Province [2019A030317020]; the National Natural Science Foundation of China [81802257, 81871859, 81772457, 82172750 and 82172811]; and the Guangdong Basic and Applied Basic Research Foundation (Guangdong - Guangzhou Joint Funds) [2022A1515111212].

Disclosure

All authors have declared no conflicts of interest.