Analysis of the p53 gene mutation status and methylation status of the promoters of p14 and p16 genes in liposarcoma

Липосаркоми, тумори мезенхималног порекла, представљају најучесталији хистолошки тип саркома меких ткива. Групу карактерише велика разноликост како у погледу патохистолошке организације тумора тако и биологије понашања. Уобичајена подела липосаркома обухвата следећа три подтипа: а.) добро диферентовани / дедиферентовани, б.) миксоидни / округлоћелијски и в.) плеоморфни. С обзиром на значајне клиничко-патолошке разлике између три подтипа липосаркома, вероватно је да су различити механизми укључени у њихов настанак и развој. На основу студија које су се фокусирале на истраживање генетике саркома меких ткива и костију, може се закључити да сигнални путеви p53-p14 и Rb-p16 играју одређену улогу у свим типовима саркома. Разјашњавање улоге ових сигналних путева у патогенези липосаркома, може да буде допринос како дијагностици и прогностици липосаркома, тако и дефинисању потенцијалних терапеутских циљева. Студија је обухватила испитивање мутационог статуса гена р53 и метилационог статуса гена р14 и р16, на 33 узорка липосаркома, сва три подтипа. У циљу свеобухватније анализе испитивана је и експресија протеина р16, циклина D1 и фактора пролиферације Ki-67, применом имунохистохемијског бојења. Резултати експеримента су показали да промене у сигналним путевима р53-р14 и р16-Rb могу имати значаја у патогенези сва три подтипа липосаркома. Разлике се огледају у квалитету и квантитету промена.Liposarcoma, tumors of mesenchymal origin, represent the most frequent type of soft tissue sarcomas. The group is characterized by a great diversity regarding pathohistology and biological behavior. The group can be divided into three different subtypes: a.) well differentiated / dedifferentiated, b.) myxoid / round cell and c.) pleomorphic. Given the significant clinical and pathological differences between the three subtypes of liposarcoma, it is likely that different mechanisms are involved in their formation and development. Based on the studies that have been focused on genetic research of sarcoma of soft tissues and bones, conclusion can be drawn that the p53-p14 and Rb-p16 signaling pathways play a certain role in sarcomagenesis. Clarification of the role of these signaling pathways in the pathogenesis of liposarcoma, could be a contribution towards better diagnostic and prognostic criteria and the identification of potential therapeutic targets. The study included examination of the p53 gene mutation status and assessment of the p14/p16 gene methylation status in 33 liposarcoma samples of all three subtypes. For the purpose of more comprehensive analysis, protein expression of the p16, cyclin D1 and Ki-67 has been evaluated by immunohistochemical staining. The results of the study have showed that the alterations of the targeted signal pathways are important in the pathogenesis of all three subtypes of liposarcoma. The differences are reflected in the quality and quantity of the detected alterations

Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques

Information aggregation from various gen, disease, and gen-disease databases such as DisGeNet, COSMIC, HumsaVar, Orphanet, ClinVar, HPO, and Diseases into a unique database would enable researchers to analyze and compare valuable domain findings in a more convenient and systematic way. However, the aggregation poses numerous challenges due to non-uniform information annotation across the databases. In this work, we address the problem of mapping a disease name, when needed, into a standardized disease code (DOID) based on Natural Language Processing text representation techniques. We examine the benefits and limitations of using off-the-shelf embeddings such as Med2vec, and language models such as BioBERT, UmlsBERT, and PubMedBERT in retrieval scenarios with respect to standard full-text search. In addition to qualitative improvements, we elaborate on the technical requirements and computational complexities that come with the embracement of language models and semantic search.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202

Significance of Beta-Catenin Expression for the Incidence of Pathological Fractures in Giant Cell Tumors of Bone

Aim of the study is to determine the possible roles of p53, cyclin D1, B-catenin and Ki-67 in the increase in risk of fractures in patients with giant cell tumor of bone. The study included a total of 164 patients with giant cell tumor of bone (GCTB), 21 (12.8%) with and 143 (87.2%) without fracture. The samples were analyzed immunohistochemically for expression of Ki-67, p53, cyclin D1 and beta-catenin. According to the immunohistochemical expression of p53 and Ki-67 in mononuclear stromal cells, as well as of cyclin D1 in multinuclear giant cells, there was no significant association with immunopositivity and risk of fractures. However, our research revealed that patients with cytoplasmic expression of beta-catenin in stromal cells had three times more frequent occurrence of pathological fractures, which was highly statistically significant (chi(2) = 7.065; p = 0.008). Moreover, a highly statistically significant correlation between the nuclear expression of beta-catenin in giant cells and the incidence of pathological fractures was also found (chi(2) = 8.824; p = 0.003). The study showed that beta-catenin expression highly correlates with the incidence of pathological fractures in patients with GCTB. Taking into account that beta-catenin is closely linked to activation of the Wnt signaling pathway in GCTB pathogenesis, one could postulate that activation of the Wnt pathway is one of the contributing factors to locally destructive behavior of this tumor, as well as to the incidence of pathological fractures

Prediction of GO terms for IDPs based on highly connected components in PPI networks

Partitioning large biological networks can help biologists to retrieve new information for particular biological structures. In literature, various methods for partitioning and clustering biological networks have been proposed. The aim of such a network partitioning is to retrieve smaller structures which are easier to analyse, but still containing important information about relations between the network elements. Highly connected deletion problem is one of such network partitioning, with the aim to partition a network into highly connected components (hcd components) by deleting minimum number of edges. A network component with n nodes is a hcd component if the degree of every vertex is larger than n/2. For the purpose of this research, we used a specially constructed local search based heuristic approach to identify hcd components. Dealing with protein-protein interaction (PPI) networks, it has been noticed that proteins from the same hcd component in a network have same Gene Ontology (GO) annotations. Based on that, we proposed a new method for prediction of GO annotations, which consists of the following steps: (a) starting PPI network is partitioned to hcd components; (b) the obtained hcd components are expanded by proteins which became singletons in the partition set; (c) the newly formed extended hcd components are the subject of further enrichment analysis in DiNGO tool, which returns a list of existing GO terms for proteins from the considered extended component; (d) after propagation through GO hierarchy, the extended list of GO is obtained; (e) each protein from the extended hcd component is annotated by a number of GO terms obtained from the previous step; The proposed method is tested on the data from CAFA-3 challenge. Comparing the F1-measure of the obtained results, a combination of parameters (type of extension, cutoff for enrichment analysis and maximum number of GO terms) with the best performances is selected for the further usage. The method with the selected parameters was further applied on a class of Intrinsically Disordered Proteins (IDP). Preliminary results indicate that this method can be useful for proposing new GO terms for IDP proteins

Methylation-specific PCR: four steps in primer design

Methylation-specific PCR (MSP) is still the method of choice for a single gene methylation study. The proper design of the primer pairs is a prerequisite for obtaining reliable PCR results. Despite numerous protocols describing the rules for MSP primer design, none of them provide a comprehensive approach to the problem. Our aim was to depict a workflow for the primer design that is concise and easy to follow. In order to achieve this goal, adequate tools for promoter sequence retrieval, MSP primer design and subsequent in silico analysis are presented and discussed. Furthermore, a few instructive examples regarding a good versus a poor primer design are provided. Finally, primer design is demonstrated according to the proposed workflow. This article aims to provide researchers, interested in a single gene methylation studies, with useful information regarding successful primer design

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.EpiMut is freely available at [https://www.vin.bg.ac.rs/180/tools/epimut.php]

Critical assessment of protein intrinsic disorder prediction

Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has F max = 0.483 on the full dataset and F max = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with F max = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

Immunohistochemical expression of nestin in rhabdomyosarcoma: implications for clinicopathology and patient outcome

Rhabdomyosarcoma (RMS) is a highly malignant cancer. Over the last two decades, prognosis for RMS patients has significantly improved, with the exception of those in the high-risk group. In order to identify new prognostic factors, we investigated the expression of nestin in RMS cells and its correlation with clinicopathological features and patient outcome. The analysis of overall survival for all patients (N = 30) revealed 1-, 2-, 3-, 4-, and 5-year survival rates of 93.3, 83.3, 66.7, 63.3, and 63.3%, respectively. Nestin overexpression significantly correlated with survival (P = 0.044). Survival of patients with = 50% nestin-positive cells was 90, 70, and 40% after 1, 2, and 3 years, respectively, and remained unchanged until the end of the investigation period. The study group composed of patients exhibiting nestin expression in GT 50% of cells showed 1-, 2-, 3-, and 4-year survival rates of 95, 90, 80, and 75%, respectively, remaining stable at 75% for the fifth year of observation. A nestin-positive expression rate lower than 50% was observed more frequently in older patients (43.60 +/- 27.58 years; P = 0.028). In addition, higher rates of nestin expression were observed in most embryonal RMS specimens and low-grade tumors, in early stages of the disease, and among younger patients. Our results lead us to propose nestin as possible positive prognostic factor in RMS

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure. © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research

Combined analysis of p16 and p14 methylation and VEGF expression status could predict more aggressive phenotype of locally advanced rectal cancers

Background: Preoperative chemoradiotherapy (CRT) represents the standard treatment for patients with locally advanced rectal cancer. Since only subset of patients has benefit from this preoperative treatment, development of reliable molecular biomarkers is required. In this retrospective study, we investigated methylation status of p16 and p14 tumor suppressor genes in locally advanced rectal cancer, in order to evaluate their potential predictive and prognostic role. Methods: Methylation-specific PCR was used to examine methylation status of p16 and p14 genes in pretherapeutic and preoperative biopsy specimens of 60 patients with locally advanced rectal cancer. Results: Aberrant methylation of p16 and p14 genes was detected in 43.3% (26/60) and 39.6% (23/58) of cases, respectively. In general, p16 and p14 methylation status did not affect the response to CRT, recurrences rate and overall survival. However, patients with simultaneous presence of either p16 or p14 methylation and high vascular endothelial growth factor (VEGF) expression showed significantly worse response to CRT (p = 0.005 and p = 0.038, respectively). In addition, tendency toward more frequent local recurrences and metastasis was observed in cases with concurrent presence of methylation of either p16 or p14 gene and high VEGF expression (p = 0.075 and p = 0.072, respectively), while patients with both of p16 methylation and high VEGF expression had significantly shorter overall survival (p = 0.010). Conclusion: Obtained results strongly suggest the importance of p16 and p14 methylation analyses in combination with other parameters, particularly VEGF expression, in order to better predict treatment response and patient outcome