Document Type
Article
Publication Date
12-21-2022
Abstract
Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data.
Recommended Citation
Kossinna, Pathum; Cai, Weijia; Lu, Xuewen; Shemanko, Carrie S; and Zhang, Qingrun, "Stabilized COre Gene and Pathway Election Uncovers Pan-Cancer Shared Pathways and a Cancer-Specific Driver" (2022). Department of Cancer Biology Faculty Papers. Paper 197.
https://jdc.jefferson.edu/cbfp/197
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
36542714
Language
English
Comments
This article is the author’s final published version in Science advances, Volume 8, Issue 51, December 2022, Pages eabo2846.
The published version is available at https://doi.org/10.1126/sciadv.abo2846. Copyright © Kossinna et al.