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Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understanding of multi-omics seems to provide a resource for integration in interdisciplinary biology since they altogether can draw the comprehensive picture of an organism's developmental and disease biology in cancers. Such large scale multi-omics data can be obtained from public consortium like The Cancer Genome Atlas (TCGA) and several other platforms. Integrating these multi-omics data from varied platforms is still challenging due to high noise and sensitivity of the platforms used. Currently, a robust integrative predictive model to estimate gene expression from these genetic and epigenetic data is lacking. In this study, we have developed a deep learning-based predictive model using Deep Denoising Auto-encoder (DDAE) and Multi-layer Perceptron (MLP) that can quantitatively capture how genetic and epigenetic alterations correlate with directionality of gene expression for liver hepatocellular carcinoma (LIHC). The DDAE used in the study has been trained to extract significant features from the input omics data to estimate the gene expression. These features have then been used for back-propagation learning by the multilayer perceptron for the task of regression and classification. We have benchmarked the proposed model against state-of-the-art regression models. Finally, the deep learning-based integration model has been evaluated for its disease classification capability, where an accuracy of 95.1% has been obtained.

The present study used untargeted metabolomics to investigate the short-term metabolic changes induced in wheat seedlings by the specialized metabolite umbelliferone, an allelochemical. We used 10 day-old wheat seedlings treated with 104 μM umbelliferone over a time course experiment covering 6 time points (0 h, 6 h, 12 h, 24 h, 48 h, and 96 h), and compared the metabolomic changes to control (mock-treated) plants. Using gas chromatography mass spectrometry (GCMS)-based metabolomics, we obtained quantitative data on 177 metabolites that were derivatized (either derivatized singly or multiple times) or not, representing 139 non-redundant (unique) metabolites. Of these 139 metabolites, 118 were associated with a unique Human Metabolome Database (HMDB) identifier, while 113 were associated with a Kyoto Encyclopedia of Genes and Genomes (KEGG) identifier. Relative quantification of these metabolites across the time-course of umbelliferone treatment revealed 22 compounds (sugars, fatty acids, secondary metabolites, organic acids, and amino acids) that changed significantly (repeated measures ANOVA, P-value < 0.05) over time. Using multivariate partial least squares discriminant analysis (PLS-DA), we showed the grouping of samples based on time-course across the control and umbelliferone-treated plants, whereas the metabolite-metabolite Pearson correlations revealed tightly formed clusters of umbelliferone-derived metabolites, fatty acids, amino acids, and carbohydrates. Also, the time-course umbelliferone treatment revealed that phospho-l-serine, maltose, and dehydroquinic acid were the top three metabolites showing highest importance in discrimination among the time-points. Overall, the biochemical changes converge towards a mechanistic explanation of the plant metabolic responses induced by umbelliferone. In particular, the perturbation of metabolites involved in tryptophan metabolism, as well as the imbalance of the shikimate pathways, which are strictly interconnected, were significantly altered by the treatment, suggesting a possible mechanism of action of this natural compound.

RNA sequencing (RNAseq) has become the method of choice for transcriptome analysis, yet no consensus exists as to the most appropriate pipeline for its analysis, with current benchmarks suffering important limitations. Here, we address these challenges through a rich benchmarking resource harnessing (i) two RNAseq datasets including ERCC ExFold spike-ins; (ii) Nanostring measurements of a panel of 150 genes on the same samples; (iii) a set of internal, genetically-determined controls; (iv) a reanalysis of the SEQC dataset; and (v) a focus on relative quantification (i.e. across-samples). We use this resource to compare different approaches to each step of RNAseq analysis, from alignment to differential expression testing. We show that methods providing the best absolute quantification do not necessarily provide good relative quantification across samples, that count-based methods are superior for gene-level relative quantification, and that the new generation of pseudo-alignment-based software performs as well as established methods, at a fraction of the computing time. We also assess the impact of library type and size on quantification and differential expression analysis. Finally, we have created a R package and a web platform to enable the simple and streamlined application of this resource to the benchmarking of future methods.

High grade serous ovarian cancer (HGSOC) is a major unmet need in oncology. The persistent uncertainty on its originating tissue has contributed to hamper the discovery of oncogenic pathways and effective therapies. Here we define the DNA methylation print that distinguishes the human fimbrial (FI) and ovarian surface epithelia (OSE) and develop a robust epigenetic cell-of-origin tracer that stratifies HGSOC in FI-and OSE-originated tumors across all available cohorts. We translate this origin-based stratification into a clinically actionable transcriptomic signature, demonstrating its prognostic impact on patients’ survival and identifying novel network level dysregulations specific for the two disease subtypes.