Copy number variations in endometrial cancer: from biological significance to clinical utility.

The molecular basis of endometrial cancer, which is the most common malignancy of the female reproductive organs, relies not only on onset of mutations but also on copy number variations, the latter consisting of gene gains or losses. In this review, we introduce copy number variations and discuss their involvement in endometrial cancer to determine the perspectives of clinical applicability. We performed a literature analysis on PubMed of publications over the past 30 years and annotated clinical information, including histological and molecular subtypes, adopted molecular techniques for identification of copy number variations, their locations, and the genes involved. We highlight correlations between the presence of some specific copy number variations and myometrial invasion, lymph node metastasis, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, high grade, drug response, and cancer progression. In particular, type I endometrial cancer cells have few copy number variations and are mainly located in 8q and 1q, while type II, high grade, and advanced FIGO stage endometrial cancer cells are aneuploid and have a greater number of copy number variations. As expected, the higher the number of copy number variations the worse the prognosis, especially if they amplify CCNE1, ERBB2, KRAS, MYC, and PIK3CA oncogenes. Great variability in copy number and location among patients with the same endometrial cancer histological or molecular subtype emerged, making them interesting candidates to be explored for the improvement of patient stratification. Copy number variations have a role in endometrial cancer progression, and therefore their detection may be useful for more accurate prediction of prognosis. Unfortunately, only a few studies have been carried out on the role of copy number variations according to the molecular classification of endometrial cancer, and even fewer have explored the correlation with drugs. For these reasons, further studies, also using single cell RNA sequencing, are needed before reaching a clinical application.

Three-dimensional nuclear architecture distinguishes thyroid cancer histotypes.

Molecular markers can serve as diagnostic tools to support pathological analysis in thyroid neoplasms. However, because the same markers can be observed in some benign thyroid lesions, additional approaches are necessary to differentiate thyroid tumor subtypes, prevent overtreatment and tailor specific clinical management. This applies particularly to the recently described variant of thyroid cancer referred to as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). This variant has an estimated prevalence of 4.4% to 9.1% of all papillary thyroid carcinomas worldwide. We studied 60 thyroid lesions: 20 classical papillary thyroid carcinoma (CPTC), 20 follicular variant of PTC (FVPTC) and 20 NIFTP. We examined morphological and molecular features to identify parameters that can differentiate NIFTP from the other PTC subtypes. When blindly investigating the nuclear architecture of thyroid neoplasms, we observed that NIFTP has significantly longer telomeres than CPTC and FVPTC. Super-resolved 3D-structured illumination microscopy demonstrated that NIFTP is heterogeneous and that its nuclei contain more densely packed DNA and smaller interchromatin spaces than CPTC and FVPTC, a pattern that resembles normal thyroid tissue. These data are consistent with the observed indolent biological behavior and favorable prognosis associated with NIFTP, which lacks BRAFV600E mutations. Of note, next-generation thyroid oncopanel sequencing was unable to distinguish the thyroid cancer histotypes in our study cohort. In summary, our data suggest that 3D nuclear architecture can be a powerful analytical tool to diagnose and guide clinical management of NIFTP.

LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario.

SUMMARY: LC/MS-based analysis techniques combined with specialized lipid tool allow for the qualitative and quantitative determination of thousands of lipid molecules. Some recent bioinformatics tools have been developed to study changes in the lipid profile in case-control experiments and correlate these changes to different enzyme activity or gene expression. However, the existing tools have the limitation to treat only the assembled lipid molecules. In reality, each individual molecule can be considered as an assembly of smaller parts, often called building blocks. These are the result of a myriad of biochemical synthesis and transformation processes that, from a systems biology perspective, should not be ignored. Here, we present LipidOne, a new lipidomic tool which highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and among experimental groups. Thanks to LipidOne, even differences in lipid building blocks can now be linked to the activity of specific classes of enzymes, transcripts and genes. AVAILABILITY AND IMPLEMENTATION: LipidOne software is freely available at www.dcbb.unipg.it/LipidOne and https://github.com/matteogiulietti/LipidOne. CONTACT: roberto.pellegrino@unipg.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of extremely low-frequency magnetic fields on human MDA-MB-231 breast cancer cells: proteomic characterization.

Extremely low-frequency electromagnetic fields (ELF-MF) can modify the cell viability and regulatory processes of some cell types, including breast cancer cells. Breast cancer is a multifactorial disease where a role for ELF-MF cannot be excluded. ELF-MF may influence the biological properties of breast cells through molecular mechanisms and signaling pathways that are still unclear. This study analyzed the changes in the cell viability, cellular morphology, oxidative stress response and alteration of proteomic profile in breast cancer cells (MDA-MB-231) exposed to ELF-MF (50 Hz, 1 mT for 4 h). Non-tumorigenic human breast cells (MCF-10A) were used as control cells. Exposed MDA-MB-231 breast cancer cells increased their viability and live cell number and showed a higher density and length of filopodia compared with the unexposed cells. In addition, ELF-MF induced an increase of the mitochondrial ROS levels and an alteration of mitochondrial morphology. Proteomic data analysis showed that ELF-MF altered the expression of 328 proteins in MDA-MB-231 cells and of 242 proteins in MCF-10A cells. Gene Ontology term enrichment analysis demonstrated that in both cell lines ELF-MF exposure up-regulated the genes enriched in "focal adhesion" and "mitochondrion". The ELF-MF exposure decreased the adhesive properties of MDA-MB-231 cells and increased the migration and invasion cell abilities. At the same time, proteomic analysis, confirmed by Real Time PCR, revealed that transcription factors associated with cellular reprogramming were upregulated in MDA-MB-231 cells and downregulated in MCF-10A cells after ELF-MF exposure. MDA-MB-231 breast cancer cells exposed to 1 mT 50 Hz ELF-MF showed modifications in proteomic profile together with changes in cell viability, cellular morphology, oxidative stress response, adhesion, migration and invasion cell abilities. The main signaling pathways involved were relative to focal adhesion, mitochondrion and cellular reprogramming.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Keratinocytes Exposed to Blue or Red Light: Proteomic Characterization Showed Cytoplasmic Thioredoxin Reductase 1 and Aldo-Keto Reductase Family 1 Member C3 Triggered Expression.

Several cell-signaling mechanisms are activated by visible light radiation in human keratinocytes, but the key regulatory proteins involved in this specific cellular response have not yet been identified. Human keratinocytes (HaCaT cells) were exposed to blue or red light at low or high irradiance for 3 days in cycles of 12 h of light and 12 h of dark. The cell viability, apoptotic rate and cell cycle progression were analyzed in all experimental conditions. The proteomic profile, oxidative stress and mitochondrial morphology were additionally evaluated in the HaCaT cells following exposure to high-irradiance blue or red light. Low-irradiance blue or red light exposure did not show an alteration in the cell viability, cell death or cell cycle progression. High-irradiance blue or red light reduced the cell viability, induced cell death and cell cycle G2/M arrest, increased the reactive oxygen species (ROS) and altered the mitochondrial density and morphology. The proteomic profile revealed a pivotal role of Cytoplasmic thioredoxin reductase 1 (TXNRD1) and Aldo-keto reductase family 1 member C3 (AKR1C3) in the response of the HaCaT cells to high-irradiance blue or red light exposure. Blue or red light exposure affected the viability of keratinocytes, activating a specific oxidative stress response and inducing mitochondrial dysfunction. Our results can help to address the targets for the therapeutic use of light and to develop adequate preventive strategies for skin damage. This in vitro study supports further in vivo investigations of the biological effects of light on human keratinocytes.

Expression and co-expression analyses of TMPRSS2, a key element in COVID-19.

The ACE2 receptor is, so far, the best-known host factor for SARS-CoV-2 entry, but another essential element, the TMPRSS2 protease, has recently been identified. Here, we have analysed TMPRSS2 expression data in the lung correlating them with age, sex, diabetes, smoking habits, exposure to pollutant and other stimuli, in order to highlight which factors might alter TMPRSS2 expression, and thus impact the susceptibility to infection and COVID-19 prognosis. Moreover, we reported TMPRSS2 polymorphisms affecting its expression and suggested the ethnic groups more prone to COVID-19. Finally, we also highlighted a gender-specific co-expression between TMPRSS2 and other genes related to SARS-CoV-2 entry, maybe explaining the higher observed susceptibility of infection in men. Our results could be useful in designing potential prevention and treatment strategies regarding the COVID-19.

Predicting future cancer burden in the United States by artificial neural networks.

Aims: To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials & methods: Two artificial neural network (ANN) algorithms were adopted: a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted. Results: The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050. Conclusion: This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.

LncRNA co-expression network analysis reveals novel biomarkers for pancreatic cancer.

High mortality and low survival rates for pancreatic ductal adenocarcinoma (PDAC) mainly result from the delay in diagnosis and treatment. Therefore, there is an urgent need to identify early PDAC biomarkers and new therapeutic targets. In this study, we applied a commonly used systems biology approach, the weighted gene co-expression network analysis (WGCNA), on lncRNA expression data. Eleven lncRNAs, namely A2M-AS1, DLEU2, LINC01133, LINC00675, MIR155HG, SLC25A25-AS1, LINC01857, LOC642852 (LINC00205), ITGB2-AS1, TSPOAP1-AS1 and PSMB8-AS1 have been identified and validated on an independent PDAC expression dataset. Furthermore, we characterized them by functional and pathway enrichment analysis and identified which lncRNAs showed differential expression, differential promoter methylation levels and copy number alterations between normal and PDAC samples. Finally, we also performed a survival analysis and identified A2M-AS1, LINC01133, LINC00205 and TSPOAP1-AS1 as prognostic biomarkers for PDAC. Interestingly, although only a few cancer-associated lncRNAs have been functionally characterized, LINC00675 and LINC01133 lncRNAs have already been demonstrated to be involved in PDAC development and progression. Therefore, our results provide new potential diagnostic/prognostic biomarkers and therapeutic targets for PDAC that deserve to be further investigated. Moreover, these lncRNAs may improve the understanding about molecular pathogenesis of PDAC.

ExportAid: database of RNA elements regulating nuclear RNA export in mammals.

MOTIVATION: Regulation of nuclear mRNA export or retention is carried out by RNA elements but the mechanism is not yet well understood. To understand the mRNA export process, it is important to collect all the involved RNA elements and their trans-acting factors. RESULTS: By hand-curated literature screening we collected, in ExportAid database, experimentally assessed data about RNA elements regulating nuclear export or retention of endogenous, heterologous or artificial RNAs in mammalian cells. This database could help to understand the RNA export language and to study the possible export efficiency alterations owing to mutations or polymorphisms. Currently, ExportAid stores 235 and 96 RNA elements, respectively, increasing and decreasing export efficiency, and 98 neutral assessed sequences. AVAILABILITY AND IMPLEMENTATION: Freely accessible without registration at http://www.introni.it/ExportAid/ExportAid.html. Database and web interface are implemented in Perl, MySQL, Apache and JavaScript with all major browsers supported.

Lgr5 expression, cancer stem cells and pancreatic cancer: results from biological and computational analyses.

AIMS: To determine the relationship between Lgr5 and other stemness markers and pathologic features in pancreatic ductal adenocarcinoma (PDAC) samples. MATERIALS & METHODS: In 69 samples, Lgr5 was analyzed by qRT-PCR together with a panel of 29 genes. Bioinformatic analysis was carried out to identify a possible pathway regulating Lgr5 expression in PDAC. RESULTS: Lgr5 expression was not associated with the expression of tested cancer stem cell markers. Moreover, it was not an independent predictor of survival neither at univariate analysis (p = 0.21) nor at multivariate analysis (p = 0.225). CONCLUSION: Based on the lack of correlation between Lgr5 and tested cancer stem cell markers, Lgr5 does not seem to be a potential stemness marker or prognostic factor in PDAC.

SpliceAid-F: a database of human splicing factors and their RNA-binding sites.

A comprehensive knowledge of all the factors involved in splicing, both proteins and RNAs, and of their interaction network is crucial for reaching a better understanding of this process and its functions. A large part of relevant information is buried in the literature or collected in various different databases. By hand-curated screenings of literature and databases, we retrieved experimentally validated data on 71 human RNA-binding splicing regulatory proteins and organized them into a database called 'SpliceAid-F' (http://www.caspur.it/SpliceAidF/). For each splicing factor (SF), the database reports its functional domains, its protein and chemical interactors and its expression data. Furthermore, we collected experimentally validated RNA-SF interactions, including relevant information on the RNA-binding sites, such as the genes where these sites lie, their genomic coordinates, the splicing effects, the experimental procedures used, as well as the corresponding bibliographic references. We also collected information from experiments showing no RNA-SF binding, at least in the assayed conditions. In total, SpliceAid-F contains 4227 interactions, 2590 RNA-binding sites and 1141 'no-binding' sites, including information on cellular contexts and conditions where binding was tested. The data collected in SpliceAid-F can provide significant information to explain an observed splicing pattern as well as the effect of mutations in functional regulatory elements.

A Comparison of Tools That Identify Tumor Cells by Inferring Copy Number Variations from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma.

Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumor cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analyzed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.

Effects of Eribulin on the RNA Content of Extracellular Vesicles Released by Metastatic Breast Cancer Cells.

Extracellular vesicles (EVs) are small lipid particles secreted by almost all human cells into the extracellular space. They perform the essential function of cell-to-cell communication, and their role in promoting breast cancer progression has been well demonstrated. It is known that EVs released by triple-negative and highly aggressive MDA-MB-231 breast cancer cells treated with paclitaxel, a microtubule-targeting agent (MTA), promoted chemoresistance in EV-recipient cells. Here, we studied the RNA content of EVs produced by the same MDA-MB-231 breast cancer cells treated with another MTA, eribulin mesylate. In particular, we analyzed the expression of different RNA species, including mRNAs, lncRNAs, miRNAs, snoRNAs, piRNAs and tRNA fragments by RNA-seq. Then, we performed differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and miRNA-target identification. Our findings demonstrate the possible involvement of EVs from eribulin-treated cells in the spread of chemoresistance, prompting the design of strategies that selectively target tumor EVs.

Accurate Analysis of Lipid Building Blocks Using the Tool LipidOne.

LC/MS-based analysis techniques combined with specialized lipid platforms allow the qualitative and quantitative determination of thousands of lipid molecules. Each individual molecule can be considered as an assembly of smaller parts, often called building blocks that are the result of a myriad of biochemical synthesis and transformation processes. LipidOne is a new lipidomic tool that automatically highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and between experimental groups. Thanks to LipidOne, the discovered differences among lipid building blocks can be easily linked to the activity of specific enzymes.

Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics?

BACKGROUND: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. METHODS: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). RESULTS: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. CONCLUSIONS: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable.

SpliceAid 2: a database of human splicing factors expression data and RNA target motifs.

Splicing is the most frequently altered biological process by mutations within gene regions. Information for splicing is recognized by several factors that bind pre-mRNA sequence and, through coordinated interaction, yield mature transcripts. Some in silico methods have been developed to predict if a mutation leads to aberrant splicing patterns. We previously created SpliceAid tool that is able to minimize false positive predictions because it adopts strictly experimental RNA target motifs bound by splicing proteins in humans. In order to improve prediction accuracy and better understand the splicing outcome, the tissue specificity of each splicing regulatory factor has to be taken into account. Here, we have developed SpliceAid 2 by adding the expression data related to the splicing factors extracted from the main proteomic and transcriptomic databases, true 5' and 3' splice sites, polypyrimidine tracts, and branch point sequences. The new version collects 2,220 target sites of 62 human splicing proteins and their expression data in 320 tissues per cell. SpliceAid 2 can be useful to foresee the splicing pattern alteration, to guide the identification of the molecular effect due to the mutations and to understand the tissue-specific alternative splicing. SpliceAid 2 is freely accessible at www.introni.it/spliceaid.html.

Effects of the Exposure of Human Non-Tumour Cells to Sera of Pancreatic Cancer Patients.

Pancreatic ductal adenocarcinoma (PDAC) has high metastatic potential. The "genometastasis" theory proposes that the blood of some cancer patients contains elements able to transform healthy cells by transferring oncogenes. Since findings on genometastasis in PDAC are still scarce, we sought supporting evidence by treating non-tumour HEK293T and hTERT-HPNE human cell lines with sera of PDAC patients. Here, we showed that HEK293T cells have undergone malignant transformation, increased the migration and invasion abilities, and acquired a partial chemoresistance, whereas hTERT-HPNE cells were almost refractory to transformation by patients' sera. Next-generation sequencing showed that transformed HEK293T cells gained and lost several genomic regions, harbouring genes involved in many cancer-associated processes. Our results support the genometastasis theory, but further studies are needed for the identification of the circulating transforming elements. Such elements could also be useful biomarkers in liquid biopsy assays.

Estrogen Related Receptor Alpha (ERRα) a Bridge between Metabolism and Adrenocortical Cancer Progression.

The aim of this study was to investigate the metabolic changes that occur in adrenocortical cancer (ACC) cells in response to the modulation of Estrogen Related Receptor (ERR)α expression and the impact on ACC progression. Proteomics analysis and metabolic profiling highlighted an important role for ERRα in the regulation of ACC metabolism. Stable ERRα overexpression in H295R cells promoted a better mitochondrial fitness and prompted toward a more aggressive phenotype characterized by higher Vimentin expression, enhanced cell migration and spheroids formation. By contrast, a decrease in ERRα protein levels, by molecular (short hairpin RNA) and pharmacological (inverse agonist XCT790) approaches modified the energetic status toward a low energy profile and reduced Vimentin expression and ability to form spheroids. XCT790 produced similar effects on two additional ACC cell lines, SW13 and mitotane-resistant MUC-1 cells. Our findings show that ERRα is able to modulate the metabolic profile of ACC cells, and its inhibition can strongly prevent the growth of mitotane-resistant ACC cells and the progression of ACC cell models to a highly migratory phenotype. Consequently, ERRα can be considered an important target for the design of new therapeutic strategies to fight ACC progression.

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources.

BACKGROUND: Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input. RESULTS: TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified. CONCLUSIONS: TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.