Related Experiment Video
Updated: Apr 6, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of a multi-cancer gene expression biomarker for cancer clinical outcomes using a network-based
Emmanuel Martinez-Ledesma1, Roeland G W Verhaak2, Victor Treviño3
11] Grupo de Enfoque e Investigación en Bioinformática, Departamento de Investigación e Innovación, Escuela Nacional de Medicina, Tecnológico de Monterrey, Monterrey, Nuevo León 64849, México [2] Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
Abstract:
Cancer types are commonly classified by histopathology and more recently through molecular characteristics such as gene expression, mutations, copy number variations, and epigenetic alterations. These molecular characterizations have led to the proposal of prognostic biomarkers for many cancer types. Nevertheless, most of these biomarkers have been proposed for a specific cancer type or even specific subtypes. Although more challenging, it is useful to identify biomarkers that can be applied for multiple types of cancer. Here, we have used a network-based exploration approach to identify a multi-cancer gene expression biomarker highly connected by ESR1, PRKACA, LRP1, JUN and SMAD2 that can be predictive of clinical outcome in 12 types of cancer from The Cancer Genome Atlas (TCGA) repository. The gene signature of this biomarker is highly supported by cancer literature, biological terms, and prognostic power in other cancer types. Additionally, the signature does not seem to be highly associated with specific mutations or copy number alterations. Comparisons with cancer-type specific and other multi-cancer biomarkers in TCGA and other datasets showed that the performance of the proposed multi-cancer biomarker is superior, making the proposed approach and multi-cancer biomarker potentially useful in research and clinical settings.
