Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer

Huey-Miin Chen; Justin A MacDonald

doi:10.1016/j.xpro.2022.101168

Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer

STAR Protoc. 2022 Feb 7;3(1):101168. doi: 10.1016/j.xpro.2022.101168. eCollection 2022 Mar 18.

Authors

Huey-Miin Chen¹, Justin A MacDonald¹

Affiliation

¹ Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4Z6, Canada.

Abstract

Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from human cancers, complete integrative GO and network analyses with focus on clinical and survival data, and identify differential correlation of trait-associated biomarkers. For complete details on the use and execution of this protocol, please refer to Chen and MacDonald (2021).

Keywords: Bioinformatics; Cancer; Gene Expression; Genomics; RNAseq; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers, Tumor / genetics
Gene Expression
High-Throughput Nucleotide Sequencing / methods
Humans
Neoplasms* / diagnosis
RNA-Seq

Substances

Biomarkers, Tumor

Grants and funding

MOP 97931/CIHR/Canada