| Literature DB >> 35697807 |
Shaolong Cao1, Jennifer R Wang2, Shuangxi Ji1, Peng Yang1,3, Yaoyi Dai1,4, Shuai Guo1, Matthew D Montierth1,4, John Paul Shen5, Xiao Zhao2, Jingxiao Chen1, Jaewon James Lee6,7,8, Paola A Guerrero6,7, Nicholas Spetsieris9, Nikolai Engedal10, Sinja Taavitsainen11, Kaixian Yu12, Julie Livingstone13,14,15,16, Vinayak Bhandari17, Shawna M Hubert18, Najat C Daw19, P Andrew Futreal20, Eleni Efstathiou9, Bora Lim21, Andrea Viale20, Jianjun Zhang18, Matti Nykter11, Bogdan A Czerniak22, Powel H Brown23, Charles Swanton24, Pavlos Msaouel7,9, Anirban Maitra6,7,22, Scott Kopetz5, Peter Campbell25, Terence P Speed26,27, Paul C Boutros13,14,15,16,17, Hongtu Zhu12, Alfonso Urbanucci10, Jonas Demeulemeester28,29, Peter Van Loo28,30, Wenyi Wang31,32.
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.Entities:
Year: 2022 PMID： 35697807 DOI： 10.1038/s41587-022-01342-x
Source DB: PubMed Journal: Nat Biotechnol ISSN： 1087-0156 Impact factor: 54.908