Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma

URN to cite this document:

urn:nbn:de:bvb:355-epub-318902 Copy

DOI to cite this document:

10.5283/epub.31890 Copy

Engelmann, Julia C. ; Amann, Thomas ; Ott-Rötzer, Brigitta ; Nützel, Magit ; Reinders, Yvonne ; Reinders, Jörg ; Thasler, Wolfgang E. ; Kristl, Theresa ; Teufel, Andreas ; Huber, Christian G. ; Oefner, Peter J. ; Spang, Rainer ; Hellerbrand, Claus

Preview

PDF (2MB)

Date of publication of this fulltext: 29 May 2015 09:54

---

Alternative links to fulltext:DOIPublisher

---

Details

Item type:	Article
Journal or Publication Title:	PLoS Computational Biology
Publisher:	Public Library of Science
Volume:	11
Number of Issue or Book Chapter:	5
Page Range:	pp. 1-22
Date:	28 March 2015
Institutions:	Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner) Medicine > Lehrstuhl für Innere Medizin I Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Projects:	Open Access Publizieren (DFG)
Identification Number:	Value Type 10.1371/journal.pcbi.1004293 DOI Article ID: e1004293 Other
Dewey Decimal Classification:	600 Technology > 610 Medical sciences Medicine
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	31890

Preview

Export bibliographical data

ASCII CitationAtomBibTeXData Cite XMLDataCite XML OAIDublin CoreEndNoteHTML CitationJSON LDMETSMODSOpenAPCRDF+N-TriplesRDF+N3RDF+XMLReferReference ManagerSimple MetadataXMLxMetaDissPlus

---

Abstract

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated ...

Abstract

Inter-cellular communication with stromal cells is vital for cancer cells. Molecules involved in the communication are potential drug targets. To identify them systematically, we applied a systems level analysis that combined reverse network engineering with causal effect estimation. Using only observational transcriptome profiles we searched for paracrine factors sending messages from activated hepatic stellate cells (HSC) to hepatocellular carcinoma (HCC) cells. We condensed these messages to predict ten proteins that, acting in concert, cause the majority of the gene expression changes observed in HCC cells. Among the 10 paracrine factors were both known and unknown cancer promoting stromal factors, the former including Placental Growth Factor (PGF) and Periostin (POSTN), while Pregnancy-Associated Plasma Protein A (PAPPA) was among the latter. Further support for the predicted effect of PAPPA on HCC cells came from both in vitro studies that showed PAPPA to contribute to the activation of NFκB signaling, and clinical data, which linked higher expression levels of PAPPA to advanced stage HCC. In summary, this study demonstrates the potential of causal modeling in combination with a condensation step borrowed from gene set analysis [Model-based Gene Set Analysis (MGSA)] in the identification of stromal signaling molecules influencing the cancer phenotype.

---

---

Metadata last modified: 25 Nov 2020 23:56

Owner only: item control page

Download Statistics

Download Statistics

Download Statistics

Download Statistics

Downloads

Downloads per month over past year

Article Level Metrics (PLOS)

Article Level Metrics (PLOS)

Article Level Metrics (PLOS)

Article Level Metrics (PLOS)

Altmetric

Altmetric

Altmetric

Alternative Statistics (altmetrics)

More literature (via CORE)

More literature (via CORE)

More literature (via CORE)

More literature (via CORE)