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Background/Aim: Oncogenic processes are delineated by metabolic dysregulation. Drug likeness is pharmacokinetically tested through the CYP450 enzymatic system, whose genetic aberrations under epigenetic stress could shift male organisms into prostate cancer pathways. Our objective was to predict the susceptibility to prostate neoplasia, focused on benign prostatic hyperplasia (BPH) and prostate cancer (PCa), based on the pharmacoepigenetic and the metabolic profile of Caucasians. Materials and Methods: Two independent cohorts of 47,389 individuals in total were assessed to find risk associations of CYP450 genes with prostatic neoplasia. The metabolic profile of the first cohort was statistically evaluated and frequencies of absorption-distribution-metabolism-excretion-toxicity (ADMET) properties were calculated. Prediction of miRNA pharmacoepigenetic targeting was performed. Results: We found that prostate cancer and benign prostatic hyperplasia patients of the first cohort shared common cardiometabolic trends. Drug classes C08CA, C09AA, C09CA, C10AA, C10AX of the cardiovascular, and G04CA, G04CB of the genitourinary systems, were associated with increased prostate cancer risk, while C03CA and N06AB of the cardiovascular and nervous systems were associated with low-risk for PCa. CYP3A4*1B was the most related pharmacogenetic polymorphism associated with prostate cancer susceptibility. miRNA-200c-3p and miRNA-27b-3p seem to be associated with CYP3A4 targeting and prostate cancer predisposition. Metabolomic analysis indicated that 11ß-OHT, 2ß-OHT, 15ß-OHT, 2a-OHT and 6ß-OHT had a high risk, and 16a-OHT, and 16ß-OHT had an intermediate disease-risk. Conclusion: These findings constitute a novel integrated signature for prostate cancer susceptibility. Further studies are required to assess their predictive value more fully. [ABSTRACT FROM AUTHOR]

Background/Objectives: Salivary gland carcinomas encompass a broad group of malignant lesions characterized by varied prognoses. Stem cells have been associated with the potential for self-renewal and differentiation to various subpopulations, resulting in histopathological variability and diverse biological behavior, features that characterize salivary gland carcinomas. This study aims to provide a thorough systematic review of immunohistochemical studies regarding the expression and prognostic significance of stem cell markers between different malignant salivary gland tumors (MSGTs). Methods: The English literature was searched via the databases MEDLINE/PubMed, EMBASE via OVID, Web of Science, Scopus, and CINHAL via EBSCO. The Joanna Briggs Institute Critical Appraisal Tool was used for risk of bias (RoB) assessment. Meta-analysis was conducted for markers evaluated in the same pair of diseases in at least two studies. Results: Fifty-four studies reported the expression of stem cell markers, e.g., c-KIT, CD44, CD133, CD24, ALDH1, BMI1, SOX2, OCT4, and NANOG, in various MSGTs. Low, moderate, and high RoB was observed in twenty-five, eleven, and eighteen studies, respectively. Meta-analysis revealed an outstanding discriminative ability of c-KIT for adenoid cystic carcinoma (AdCC) over polymorphous adenocarcinoma [P(LG)A] but did not confirm the prognostic significance of stem cell markers in MSGTs. Conclusions: This study indicated a possible link between stem cells and the histopathological heterogeneity and diverse biological behavior that characterize the MSGTs. c-KIT might be of diagnostic value in discriminating between AdCC and P(LG)A. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-Seq) provides detailed insight into gene expression at the individual cell level, revealing hidden cell diversity. However, scRNA-Seq data pose challenges due to high-dimensionality and sparsity. High-dimensionality stems from analysing numerous cells and genes, while sparsity arises from zero counts in gene expression data, known as dropout events. This necessitates robust data processing methods of the scRNA-Seq gene counts, for meaningful interpretation. Dimensionality reduction techniques, such as principal component analysis, transform gene count data into lower-dimensional spaces retaining biological information, aiding in downstream analyses, while dimensionality reduction-based visualisation methods, such as t-distributed stochastic neighbour embedding, and uniform manifold approximation and projection are used for cell or gene clustering. Deep learning techniques, such as variational autoencoders and generative adversarial networks compress data and generate synthetic gene expression profiles, augmenting datasets and improving utility in biomedical research. In recent years, the interest for scRNA-Seq dimensionality reduction has markedly increased, not only leading to the development of a multitude of methods, but also to the integration of these approaches into scRNA-Seq data processing pipelines. The present review aimed to list and explain, in layman's terms, the current popular dimensionality reduction methods, as well as include advancements and software package implementations of them. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) play a major role in the regulation of gene expression. Discovering the TFs which bind to the regulatory regions of each gene has been long-term focus of research. Since the experimental verification of TF binding sites (TFBSs) is a complex process, webtools that perform predictions have been developed. However, the majority of the tools do not provide a user-friendly environment for data input and a number of these tools produce a large number of false-positive results. The present study introduces TFBSPred, a TFBS prediction webtool that utilises hidden Markov model-based TF flexible models (TFFM) for predicting binding sites while providing an automated and minimal input user interface. TFBSPred uses DNAse I hypersensitivity data from ENCODE to identify open chromatin regions and takes advantage of the conservation between Homo sapiens and Mus musculus, by using Ensembl Compara pairwise alignments, to increase the true positive rate of the prediction. The users input a gene name or genomic location of a human or mouse genome, select the cell types of interest and TFBSPred outputs the conserved open chromatin region of the selected regulatory sequences and cell types as a pairwise alignment and displays the predicted TFBSs. The present study benchmarked TFBSPred and several similar functioning webtools using experimentally verified TFBSs. TFBSPred exhibited the optimal trade-off between sensitivity and specificity in the case of the well-studied IFNB1 enhanceosome, while outperforming the other web tools in subsequent use-cases. TFBSPred may thus prove to be a valuable tool for TFBS prediction and for the provision of hypotheses for experimental validation. TFBSPred also has the potential for further improvement with future updates of TFFM data. TFBSPred is freely available at: https://www.michalopoulos.net/tfbspred/. [ABSTRACT FROM AUTHOR]