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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.

Multi-omics, variously called integrated omics, pan-omics, and trans-omics, aims to combine two or more omics data sets to aid in data analysis, visualization and interpretation to determine the mechanism of a biological process. Multi-omics efforts have taken center stage in biomedical research leading to the development of new insights into biological events and processes. However, the mushrooming of a myriad of tools, datasets, and approaches tends to inundate the literature and overwhelm researchers new to the field. The aims of this review are to provide an overview of the current state of the field, inform on available reliable resources, discuss the application of statistics and machine/deep learning in multi-omics analyses, discuss findable, accessible, interoperable, reusable (FAIR) research, and point to best practices in benchmarking. Thus, we provide guidance to interested users of the domain by addressing challenges of the underlying biology, giving an overview of the available toolset, addressing common pitfalls, and acknowledging current methods’ limitations. We conclude with practical advice and recommendations on software engineering and reproducibility practices to share a comprehensive awareness with new researchers in multi-omics for end-to-end workflow.

Background High-grade serous ovarian cancer (HGSOC) is a major unmet need in oncology. The remaining uncertainty on its originating tissue has hampered the discovery of molecular oncogenic pathways and the development of effective therapies. Methods We used an approach based on the retention in tumors of a DNA methylation trace (OriPrint) that distinguishes the two putative tissues of origin of HGSOC, the fimbrial (FI) and ovarian surface epithelia (OSE), to stratify HGSOC by several clustering methods, both linear and non-linear. The identified tumor subtypes (FI-like and OSE-like HGSOC) were investigated at the RNAseq level to stratify an in-house cohort of macrodissected HGSOC FFPE samples to derive overall and disease-free survival and identify specific transcriptional alterations of the two tumor subtypes, both by classical differential expression and weighted correlation network analysis. We translated our strategy to published datasets and verified the co-occurrence of previously described molecular classification of HGSOC. We performed cytokine analysis coupled to immune phenotyping to verify alterations in the immune compartment associated with HGSOC. We identified genes that are both differentially expressed and methylated in the two tumor subtypes, concentrating on PAX8 as a bona fide marker of FI-like HGSOC. Results We show that: - OriPrint is a robust DNA methylation tracer that exposes the tissue of origin of HGSOC. - The tissue of origin of HGSOC is the main determinant of DNA methylation variance in HGSOC. - The tissue of origin is a prognostic factor for HGSOC patients. - FI-like and OSE-like HGSOC are endowed with specific transcriptional alterations that impact patients’ prognosis. - OSE-like tumors present a more invasive and immunomodulatory phenotype, compatible with its worse prognostic impact. - Among genes that are differentially expressed and regulated in FI-like and OSE-like HGSOC, PAX8 is a bona fide marker of FI-like tumors. Conclusions Through an integrated approach, our work demonstrates that both FI and OSE are possible origins for human HGSOC, whose derived subtypes are both molecularly and clinically distinct. These results will help define a new roadmap towards rational, subtype-specific therapeutic inroads and improved patients’ care.

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.