Utilizing Shared Big Data to Identify Liver Cancer Dedifferentiation Markers

Kirill Borziak, Joseph Finkelstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Big data reanalysis has the potential to generate novel comparative analyses which aim to generate novel hypotheses and knowledge. However, this approach is underutilized in the realm of cancer research, particularly for cancer stem cells (CSCs). CSCs are a rare subset of tumor cells, which dedifferentiate from healthy adult cells, and have the potential for self-renewal and treatment resistance. This analysis utilizes two publically available single-cell RNA-seq datasets of liver cancer and adult liver cell types to demonstrate how reanalysis of big data can lead to valuable new discoveries. We identify 519 differentially expressed genes between liver CSCs and healthy liver cell types. Here we report the potential novel liver CSC dedifferentiation factor, Msh Homeobox 2, which was significantly upregulated in liver CSCs by 1.36 fold (p-value < 1E-10). These findings have the potential to further advance our knowledge of genes governing the formation of CSCs.

Original languageEnglish
Title of host publicationInformatics and Technology in Clinical Care and Public Health
EditorsJohn Mantas, Arie Hasman, Mowafa S. Househ, Parisis Gallos, Emmanouil Zoulias, Joseph Liasko
PublisherIOS Press BV
Number of pages4
ISBN (Electronic)9781643682501
StatePublished - 2022

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


  • Liver
  • cancer stem cells
  • dedifferentiation
  • single cell transcriptomics


Dive into the research topics of 'Utilizing Shared Big Data to Identify Liver Cancer Dedifferentiation Markers'. Together they form a unique fingerprint.

Cite this