A Gold Standard-Derived Modular Barcoding Approach to Cancer Transcriptomics

Authors

Yan Zhu
Mohamad Karim I Koleilat
Jason Roszik
Man Kam Kwong
Zhonglin Wang
Dipen M Maru
Scott Kopetz
Lawrence N Kwong

Publication Date

5-15-2024

Journal

Cancers

Abstract

A challenge with studying cancer transcriptomes is in distilling the wealth of information down into manageable portions of information. In this resource, we develop an approach that creates and assembles cancer type-specific gene expression modules into flexible barcodes, allowing for adaptation to a wide variety of uses. Specifically, we propose that modules derived organically from high-quality gold standards such as The Cancer Genome Atlas (TCGA) can accurately capture and describe functionally related genes that are relevant to specific cancer types. We show that such modules can: (1) uncover novel gene relationships and nominate new functional memberships, (2) improve and speed up analysis of smaller or lower-resolution datasets, (3) re-create and expand known cancer subtyping schemes, (4) act as a "decoder" to bridge seemingly disparate established gene signatures, and (5) efficiently apply single-cell RNA sequencing information to other datasets. Moreover, such modules can be used in conjunction with native spreadsheet program commands to create a powerful and rapid approach to hypothesis generation and testing that is readily accessible to non-bioinformaticians. Finally, we provide tools for users to create and interpret their own modules. Overall, the flexible modular nature of the proposed barcoding provides a user-friendly approach to rapidly decoding transcriptome-wide data for research or, potentially, clinical uses.

Keywords

cancer, modules, barcoding, next-generation sequencing

DOI

\10.3390/cancers16101886

PMID

38791964

PMCID

PMC11120226

PubMedCentral® Posted Date

5-15-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes