Format

Send to

Choose Destination
Cancer Med. 2019 Sep 10. doi: 10.1002/cam4.2493. [Epub ahead of print]

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates.

Author information

1
Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.
2
National Institute of Science and Technology for Translational Medicine (INCT-TM), Porto Alegre, RS, Brazil.

Abstract

BACKGROUND:

Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment.

METHODS:

We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning.

RESULTS:

The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival.

CONCLUSION:

Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.

KEYWORDS:

computational drug repositioning; connectivity map; lung cancer; master regulator; transcriptomic

PMID:
31503425
DOI:
10.1002/cam4.2493
Free full text

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center