Cancer omics: from regulatory networks to clinical outcomes

Uncovering the Pathogenesis of Orofacial Clefts Using Bioinformatics Analysis

OBJECTIVE

Many genes have been found to be associated with the occurrence of the orofacial clefts (OFC). The links between these pathogenic genes are rarely studied. In this study, bioinformatics analysis were performed in order to find associations between OFC-related genes and provide new ideas for etiology study of OFCs.

METHODS

Orofacial clefts-related genes were searched and identified from the Online Mendelian Inheritance of Man (OMIM.org). These genes were then analyzed by bioinformatics methods, including protein-protein interaction network, functional enrichment analysis, module analysis, and hub genes analysis.

RESULTS

After searching the database of OMIM.org and removing duplicate results, 279 genes were finally obtained. These genes were involved to 369 pathways in biological process, 56 in cell component, 64 in molecular function, and 45 in the Kyoto Encyclopedia of Genes and Genomes. Most identified genes were significantly enriched in embryonic appendage morphogenesis (29.17%), embryonic limb morphogenesis (6.06%), and limb development (4.33%) for biological process (Fig. 5A); ciliary tip (42.86%), MKS complex (28.57%), ciliary basal body (14.29%), and ciliary membrane (14.29%) for cell component. The top 10 hub genes were identified, including SHH, GLI2, PTCH1, SMAD4, FGFR1, BMP4, SOX9, SOX2, RUNX2, and CDH1.

CONCLUSIONS

Bioinformatics methods were used to analyze OFC-related genes in this study, including hub gene identifying and analysis, protein-protein interaction network construction, and functional enrichment analysis. Several potential mechanisms related to occurrence of OFCs were also discussed. These results may be helpful for further studies of the etiology of OFC.

Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research

Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention.

SHORT ABSTRACT

Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.

Cancer Omics in Africa: Present and Prospects

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an "African Cancer Genomics Consortium" to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.

Integrated bioinformatics analysis of chromatin regulator EZH2 in regulating mRNA and lncRNA expression by ChIP sequencing and RNA sequencing

Enhancer of zeste homolog 2 (EZH2), a dynamic chromatin regulator in cancer, represents a potential therapeutic target showing early signs of promise in clinical trials. EZH2 ChIP sequencing data in 19 cell lines and RNA sequencing data in ten cancer types were downloaded from GEO and TCGA, respectively. Integrated ChIP sequencing analysis and co-expressing analysis were conducted and both mRNA and long noncoding RNA (lncRNA) targets were detected. We detected a median of 4,672 mRNA targets and 4,024 lncRNA targets regulated by EZH2 in 19 cell lines. 20 mRNA targets and 27 lncRNA targets were found in all 19 cell lines. These mRNA targets were enriched in pathways in cancer, Hippo, Wnt, MAPK and PI3K-Akt pathways. Co-expression analysis confirmed numerous targets, mRNA genes (RRAS, TGFBR2, NUF2 and PRC1) and lncRNA genes (lncRNA LINC00261, DIO3OS, RP11-307C12.11 and RP11-98D18.9) were potential targets and were significantly correlated with EZH2. We predicted genome-wide potential targets and the role of EZH2 in regulating as a transcriptional suppressor or activator which could pave the way for mechanism studies and the targeted therapy of EZH2 in cancer.

Actionable clinical decisions based on comprehensive genomic evaluation in asymptomatic adults

Whole-exome sequencing (WES) arises as a new approach in diagnosing individuals affected by multigenic and complex phenotypes. Herein, we aim to examine whether WES is useful in screening asymptomatic individuals for actionable interventions, which has not yet been established. Twenty-five healthy adults underwent WES, bioinformatics, and manual curation of their exomes. Six participants (24%) harbored significant, management-changing variants in cancer predisposition genes, American College of Medical Genetics, and genomics reportable cardiac diseases and pharmacogenomic biomarkers that have led to clinical recommendations and interventions. Furthermore, more than 80% of the participants (21) carried 1–3 genetic variants with an associated clinical guideline for an altered drug dosing or administration based on the FDA’s table of pharmacogenomics. These results support WES potential not only to answer specific diagnostic questions presented by the relevant personal and/or family history but also to uncover clinically important genetic findings unrelated to the primary indication for sequencing.

Mathematical model of a telomerase transcriptional regulatory network developed by cell-based screening: analysis of inhibitor effects and telomerase expression mechanisms

Cancer cells depend on transcription of telomerase reverse transcriptase (TERT). Many transcription factors affect TERT, though regulation occurs in context of a broader network. Network effects on telomerase regulation have not been investigated, though deeper understanding of TERT transcription requires a systems view. However, control over individual interactions in complex networks is not easily achievable. Mathematical modelling provides an attractive approach for analysis of complex systems and some models may prove useful in systems pharmacology approaches to drug discovery. In this report, we used transfection screening to test interactions among 14 TERT regulatory transcription factors and their respective promoters in ovarian cancer cells. The results were used to generate a network model of TERT transcription and to implement a dynamic Boolean model whose steady states were analysed. Modelled effects of signal transduction inhibitors successfully predicted TERT repression by Src-family inhibitor SU6656 and lack of repression by ERK inhibitor FR180204, results confirmed by RT-QPCR analysis of endogenous TERT expression in treated cells. Modelled effects of GSK3 inhibitor 6-bromoindirubin-3′-oxime (BIO) predicted unstable TERT repression dependent on noise and expression of JUN, corresponding with observations from a previous study. MYC expression is critical in TERT activation in the model, consistent with its well known function in endogenous TERT regulation. Loss of MYC caused complete TERT suppression in our model, substantially rescued only by co-suppression of AR. Interestingly expression was easily rescued under modelled Ets-factor gain of function, as occurs in TERT promoter mutation. RNAi targeting AR, JUN, MXD1, SP3, or TP53, showed that AR suppression does rescue endogenous TERT expression following MYC knockdown in these cells and SP3 or TP53 siRNA also cause partial recovery. The model therefore successfully predicted several aspects of TERT regulation including previously unknown mechanisms. An extrapolation suggests that a dominant stimulatory system may programme TERT for transcriptional stability.

Tumour cells acquire the ability to divide and multiply indefinitely whereas normal cells can undergo only a limited number of divisions. The switch to immortalisation of the tumour cell is dependent on maintaining the integrity of telomere DNA which forms chromosome ends and is achieved through activation of the telomerase enzyme by turning on synthesis of the TERT gene, which is usually silenced in normal cells. Suppressing telomerase is toxic to cancer cells and it is widely believed that understanding TERT regulation could lead to potential cancer therapies. Previous studies have identified many of the factors which individually contribute to activate or repress TERT levels in cancer cells. However, transcription factors do not behave in isolation in cells, but rather as a complex co-operative network displaying inter-regulation. Therefore, full understanding of TERT regulation will require a broader view of the transcriptional network. In this paper we take a computational modelling approach to study TERT regulation at the network level. We tested interactions between 14 TERT-regulatory factors in an ovarian cancer cell line using a screening approach and developed a model to analyse which network interventions were able to silence TERT.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.