Abstract
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.
Highlights
Integrating and understanding complex data from different high-throughput technologies is a central research topic
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)
The Supplementary Material lists the results for miRNAs (Supplementary Table S1), mRNAs (Supplementary Table S2) and proteins (Supplementary Table S3)
Summary
Integrating and understanding complex data from different high-throughput technologies is a central research topic. Different strategies to uncover genotype–phenotype interactions from multi-omics data sets have been explored as reviewed by Ritchie [1]. Integrating different omics data can support discovering true positive events and help to reduce the false discovery rate. To discover and understand altered signaling cascades in lung cancer we chose a study set-up addressing these two challenges. To gain deeper insight into the regulative capacity of miRNAs on mRNA and protein levels, we generated miRNA expression in the same samples, improved the proteomic data and performed a novel integrative systems biology analysis of miRNA-, mRNA- and protein profiles. Our results rely on experimental data from 6,183 proteins (compared to 3,328 proteins in the proof-ofconcept study), gene expression of 34,687 genes from our previous study and additional 2,549 miRNAs
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