Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Abstract:
        Abstract
        Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

SEEK ID: https://fairdomhub.org/publications/687

DOI: 10.1038/s41467-023-41132-w

Projects: PoLiMeR - Polymers in the Liver: Metabolism and Regulation

Publication type: Journal

Journal: Nature Communications

Citation: Nat Commun 14(1),5417

Date Published: 1st Dec 2023

Registered Mode: by DOI

Authors: Han Jin, Cheng Zhang, Martin Zwahlen, Kalle von Feilitzen, Max Karlsson, Mengnan Shi, Meng Yuan, Xiya Song, Xiangyu Li, Hong Yang, Hasan Turkez, Linn Fagerberg, Mathias Uhlén, Adil Mardinoglu

help Submitter
Citation
Jin, H., Zhang, C., Zwahlen, M., von Feilitzen, K., Karlsson, M., Shi, M., Yuan, M., Song, X., Li, X., Yang, H., Turkez, H., Fagerberg, L., Uhlén, M., & Mardinoglu, A. (2023). Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation. In Nature Communications (Vol. 14, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41467-023-41132-w
Activity

Views: 475

Created: 2nd Jan 2024 at 10:30

help Tags

This item has not yet been tagged.

help Attributions

None

Powered by
(v.1.16.0-pre)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH