CaPSSA: visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data
- 주제(기타) Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability
- 설명문(일반) [Jang, Yeongjun; Seo, Jihae; Lee, Sanghyuk] Ewha Womans Univ, Ewha Res Ctr Syst Biol ERCSB, Seoul 03760, South Korea; [Jang, Yeongjun; Kim, Sun] Seoul Natl Univ, Coll Nat Sci, Interdisciplinary Program Bioinformat, Seoul 08826, South Korea; [Jang, Insu; Lee, Byungwook] KRIBB, Korean Bioinformat Ctr KOBIC, Daejeon 34141, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 OXFORD UNIV PRESS
- 발행년도 2019
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000165850
- 본문언어 영어
- Published As http://dx.doi.org/10.1093/bioinformatics/btz516
- PubMed https://pubmed.ncbi.nlm.nih.gov/31228188
초록/요약
Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information.
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