bioRxivGenomicsPreprint
Tumor cell total mRNA expression shapes the molecular and clinical phenotype of cancer
Shaolong CaoiD, Jennifer Rui WangiD, Shuangxi JiiD, Yang PengiD, Matthew D. MontierthiD, Shuai GuoiD, John Paul SheniD, Xiao Ling ZhaoiD, Jingxiao CheniD, Jaewon James LeeiD, Paola A. GuerreroiD, Nicholas SpetsierisiD, Nikolai EngedaliD, Sinja TaavitsaineniD, Kaixian YuiD, Julie M. LivingstoneiD, Vinayak BhandariiD, Shawna Marie HubertiD, Najat C. DawiD, P. Andrew FutrealiD, Eleni EfstathiouiD, Bora LimiD, Andrea VialeiD, Jianjun J. ZhangiD, Matti NykteriD, , Wenyi WangiD
Abstract
Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 5
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Tumor cell specific total mRNA expression informed neural networks predicts cancer progression
Ankita Paul, Jessica C. Lal, … Wenyi Wang·bioRxiv·May 6, 2026
- Precise identification of cancer cells from allelic imbalances in single cell transcriptomes
Mi K. Trinh, Clarissa N. Pacyna, … Matthew D. Young·Communications BiologyPeer-reviewed·Sep 7, 2022
- Estimation of immune cell content in tumor using single-cell RNA-seq reference data
- Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types
Yao Lin, Jingyi Li, … Sanqi An·Genomics Proteomics & BioinformaticsPeer-reviewed·Jul 5, 2024
- Variation in bulk RNA-seq and estimated cell type proportion using deconvolution when comparing pancreatic cancer samples within the same individual
Rick J. Jansen, Sarah A. Munro, … Hugues Sicotte·medRxiv·May 5, 2025
- The relationship of mRNA with protein expression in CD8+ T cells associates with gene class and gene characteristics
B. P. Nicolet, M. C. Wolkers·bioRxiv·Apr 21, 2020