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Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression
Journal article   Open access   Peer reviewed

Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression

Shaolong Cao, Jennifer R. Wang, Shuangxi Ji, Peng Yang, Yaoyi Dai, Shuai Guo, Matthew D. Montierth, John Paul Shen, Xiao Zhao, Jingxiao Chen, …
Nature biotechnology, Vol.40(11), pp.1624-1633
11/01/2022
DOI: 10.1038/s41587-022-01342-x
PMID: 35697807
url
https://doi.org/10.1038/s41587-022-01342-xView
Published (Version of record) Open Access

Abstract

Single-cell RNA sequencing studies have suggested that total mRNA content correlates with tumor phenotypes. Technical and analytical challenges, however, have so far impeded at-scale pan-cancer examination of total mRNA content. Here we present a method to quantify tumor-specific total mRNA expression (TmS) from bulk sequencing data, taking into account tumor transcript proportion, purity and ploidy, which are estimated through transcriptomic/genomic deconvolution. We estimate and validate TmS in 6,590 patient tumors across 15 cancer types, identifying significant inter-tumor variability. Across cancers, high TmS is associated with increased risk of disease progression and death. TmS is influenced by cancer-specific patterns of gene alteration and intra-tumor genetic heterogeneity as well as by pan-cancer trends in metabolic dysregulation. Taken together, our results indicate that measuring cell-type-specific total mRNA expression in tumor cells predicts tumor phenotypes and clinical outcomes. Total mRNA expression in cancer cells is a marker for disease progression and death.
631/114/2397 631/67/2329 Agriculture Article Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology General Life Sciences

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