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PESSA: A web tool for pathway enrichment score-based survival analysis in cancer
Yang, Hong1,2; Shi, Ying1,3; Lin, Anqi1; Qi, Chang4; Liu, Zaoqu5,6; Cheng, Quan7,8; Miao, Kai9,10; Zhang, Jian1; Luo, Peng1
2024-05-08
Source PublicationPLoS Computational Biology
ISSN1553-734X
Volume20Issue:5 MayPages:e1012024
Abstract

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan–Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Other Abstract

Author summary

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan–Meier analyses based on the median and optimal cut-off values and visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. Freely available on the web at https://smuonco.shinyapps.io/PESSA/ OR http://robinllab.com/PESSA. Website implemented in R Shiny, with major browsers supported.

DOI10.1371/journal.pcbi.1012024
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaBiochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Mathematical & Computational Biology
WOS IDWOS:001218607800001
PublisherPUBLIC LIBRARY SCIENCE, 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111
Scopus ID2-s2.0-85192627871
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionFaculty of Health Sciences
Cancer Centre
Institute of Translational Medicine
Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau
Corresponding AuthorCheng, Quan; Miao, Kai; Zhang, Jian
Affiliation1.Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Haizhu District, Guangdong, China
2.The First School of Clinical Medicine, Southern Medical University, Guangzhou, Baiyun District, Guangdong, China
3.The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Baiyun District, Guangdong, China
4.Institute of Logic and Computation, TU Wien, Austria
5.State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
6.State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
7.Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
8.National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
9.Cancer Centre and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, Macao
10.MoE Frontiers Science Center for Precision Oncology, University of Macau, Macau, Macao
Corresponding Author AffilicationCancer Centre;  University of Macau
Recommended Citation
GB/T 7714
Yang, Hong,Shi, Ying,Lin, Anqi,et al. PESSA: A web tool for pathway enrichment score-based survival analysis in cancer[J]. PLoS Computational Biology, 2024, 20(5 May), e1012024.
APA Yang, Hong., Shi, Ying., Lin, Anqi., Qi, Chang., Liu, Zaoqu., Cheng, Quan., Miao, Kai., Zhang, Jian., & Luo, Peng (2024). PESSA: A web tool for pathway enrichment score-based survival analysis in cancer. PLoS Computational Biology, 20(5 May), e1012024.
MLA Yang, Hong,et al."PESSA: A web tool for pathway enrichment score-based survival analysis in cancer".PLoS Computational Biology 20.5 May(2024):e1012024.
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