English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer

MPS-Authors
/persons/resource/persons221753

Wollny,  Damian       
Single Cell Genomics, Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

Safrastyan_Decoding_HumGen_2023.pdf
(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Safrastyan, A., zu Siederdissen, C. H., & Wollny, D. (2023). Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer. Human Genomics, 17(1): 90. doi:10.1186/s40246-023-00537-w.


Cite as: https://hdl.handle.net/21.11116/0000-000D-CEB4-0
Abstract
Background: Liquid biopsy, particularly cell-free RNA (cfRNA), has emerged as a promising non-invasive diagnostic tool for various diseases, including cancer, due to its accessibility and the wealth of information it provides. A key area of interest is the composition and cellular origin of cfRNA in the blood and the alterations in the cfRNA transcriptomic landscape during carcinogenesis. Investigating these changes can offer insights into the manifestations of tissue alterations in the blood, potentially leading to more effective diagnostic strategies. However, the consistency of these findings across different studies and their clinical utility remains to be fully elucidated, highlighting the need for further research in this area. Results: In this study, we analyzed over 350 blood samples from four distinct studies, investigating the cell type contributions to the cfRNA transcriptomic landscape in liver cancer. We found that an increase in hepatocyte proportions in the blood is a consistent feature across most studies and can be effectively utilized for classifying cancer and healthy samples. Moreover, our analysis revealed that in addition to hepatocytes, liver endothelial cell signatures are also prominent in the observed changes. By comparing the classification performance of cellular proportions to established markers, we demonstrated that cellular proportions could distinguish cancer from healthy samples as effectively as existing markers and can even enhance classification when used in combination with these markers. Conclusions: Our comprehensive analysis of liver cell-type composition changes in blood revealed robust effects that help classify cancer from healthy samples. This is especially noteworthy, considering the heterogeneous nature of datasets and the etiological distinctions of samples. Furthermore, the observed differences in results across studies underscore the importance of integrative and comparative approaches in the future research to determine the consistency and robustness of findings. This study contributes to the understanding of cfRNA composition in liver cancer and highlights the potential of cellular deconvolution in liquid biopsy. © 2023, BioMed Central Ltd., part of Springer Nature.