Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) co-expression 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 onco-genic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data.

Automated summary

This article introduces a tool called SCOPE, which integrates bootstrapped least absolute shrinkage and selection operator and coexpression analysis to obtain stable results that are insensitive to variations in the data. By applying SCOPE to cancer expression datasets, core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response were identified, highlighting the pivotal role of CD63 as an oncogenic driver and potential therapeutic target in kidney cancer.