Paired proteomics, transcriptomics and miRNomics in non-small cell lung cancers: known and novel signaling cascades
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Christina Backes1,*, Nicole Ludwig2,*, Petra Leidinger2, Hanno Huwer3, Stefan Tenzer4, Tobias Fehlmann1, Andre Franke5, Eckart Meese2, Hans-Peter Lenhof6, Andreas Keller1
1Chair for Clinical Bioinformatics, Saarland University, Germany
2Department of Human Genetics, Saarland University, Germany
3SHG Clinics, Völklingen, Germany
4Institute for Immunology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
5IKMB, Kiel, Germany
6Chair for Bioinformatics, Saarland University, Germany
*These authors have contributed equally to this work
Andreas Keller, email: email@example.com
Keywords: systems biology, transcriptomics, miRNomics, proteomics, lung cancer
Received: March 15, 2016 Accepted: August 01, 2016 Published: August 31, 2016
High-throughput omics analyses are applied to elucidate molecular pathogenic mechanisms in cancer. Given restricted cohort sizes and contrasting large feature sets paired multi-omics analysis supports discovery of true positive deregulated signaling cascades. For lung cancer patients we measured from the same tissue biopsies proteomic- (6,183 proteins), transcriptomic- (34,687 genes) and miRNomic data (2,549 miRNAs). To minimize inter-individual variations case and control lung biopsies have been gathered from the same individuals.
Considering single omics entities, 15 of 2,549 miRNAs (0.6%), 752 of 34,687 genes (2.2%) and 141 of 6,183 proteins (2.3%) were significantly deregulated. Multivariate analysis also revealed that effects in miRNA were smaller compared to genes and proteins indicating that expression changes of miRNAs might also have limited impact of pathogenicity. However, a new algorithm for modeling the complex mutual interactions of miRNAs and their target genes facilitated precise prediction of deregulation in cancer genes (92.3% accuracy, p=0.007). Lastly, deregulation of genes in cancer matched deregulation of proteins coded by the genes in 80% of cases.
The resulting interaction network, which is based on quantitative analysis of the abundance of miRNAs, mRNAs and proteins each taken from the same lung cancer tissue and from the same autologous normal lung tissue confirms molecular pathological changes and further contributes to the discovery of altered signaling cascades in lung cancer.
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