Uterine pathologies pose a challenge to women\'s health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, a user-friendly web application enabling researchers to detect differentially expressed genes using data from NCBI\'s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt for functional enrichment analysis. It also provides visualization plots for enhanced result interpretation. With a Docker image for local execution, DExplore accommodates unpublished data. To illustrate its utility, we showcase two case studies on cancer cells treated with chemotherapeutic drugs. DExplore streamlines microarray data analysis, empowering molecular biologists to focus on genes of biological significance.

The purpose of the study is to propose a multi-class ensemble classifier using interval modeling dedicated to microarray datasets. An approach of creating the uncertainty intervals for the single prediction values of constituent classifiers and then aggregating the obtained intervals with the use of interval-valued aggregation functions is used. The proposed heterogeneous classification employs Random Forest, Support Vector Machines, and Multilayer Perceptron as component classifiers, utilizing cross-entropy to select the optimal classifier. Moreover, orders for intervals are applied to determine the decision class of an object. The applied interval-valued aggregation functions are tested in terms of optimizing the performance of the considered ensemble classifier. The proposed model\'s quality, superior to other well-known and component classifiers, is validated through comparison, demonstrating the efficacy of cross-entropy in ensemble model construction.

Acute lymphoblastic leukemia (ALL) represents around 25% of adult acute leukemias. Despite the increasing improvement in the survival rate of ALL patients during the last decade, the heterogeneous clinical and molecular features of this malignancy still represent a major challenge for treatment and achieving better outcomes. To identify aberrantly expressed genes in bone marrow (BM) samples from adults with ALL, transcriptomic analysis was performed using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0). Differentially expressed genes (DEGs) (±2-fold change, p-value < 0.05, and FDR < 0.05) were detected using the Transcriptome Analysis Console. Gene Ontology (GO), Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA) were employed to identify gene function and define the enriched pathways of DEGs. The protein-protein interactions (PPIs) of DEGs were constructed. A total of 871 genes were differentially expressed, and DNTT, MYB, EBF1, SOX4, and ERG were the top five up-regulated genes. Meanwhile, the top five down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, and VCAN. An association between ERG, CDK6, and SOX4 expression levels and the probability of relapse and death was observed. Regulation of the immune system, immune response, cellular response to stimulus, as well as apoptosis signaling, inflammation mediated by chemokines and cytokines, and T cell activation were among the most altered biological processes and pathways, respectively. Transcriptome analysis of ALL in adults reveals a group of genes consistently associated with hematological malignancies and underscores their relevance in the development of ALL in adults.

Donation after circulatory death (DCD) hearts are predominantly maintained by normothermic blood perfusion (NBP). Nevertheless, it was shown that hypothermic crystalloid perfusion (HCP) is superior to blood perfusion to recondition left ventricular (LV) contractility. However, transcriptomic changes in the myocardium and coronary artery in DCD hearts after HCP and NBP have not been investigated yet. In a pig model, DCD hearts were harvested and maintained for 4 h by NBP (DCD-BP group, N = 8) or HCP with oxygenated histidine-tryptophane-ketoglutarate (HTK) solution (DCD-HTK, N = 8) followed by reperfusion with fresh blood for 2 h. In the DCD group (N = 8), hearts underwent reperfusion immediately after procurement. In the control group (N = 7), no circulatory death was induced. We performed transcriptomics from LV myocardial and left anterior descending (LAD) samples using microarrays (25,470 genes). We applied the Boruta algorithm for variable selection to identify relevant genes. In the DCD-BP group, compared to DCD, six genes were regulated in the myocardium and 1915 genes were regulated in the LAD. In the DCD-HTK group, 259 genes were downregulated in the myocardium and 27 in the LAD; and 52 genes were upregulated in the myocardium and 765 in the LAD, compared to the DCD group. We identified seven genes of relevance for group identification: ITPRIP, G3BP1, ARRDC3, XPO6, NOP2, SPTSSA, and IL-6. NBP resulted in the upregulation of genes involved in mitochondrial calcium accumulation and ROS production, the reduction in microvascular endothelial sprouting, and inflammation. HCP resulted in the downregulation of genes involved in NF-κB-, STAT3-, and SASP-activation and inflammation.

The animal models used in biomedical research cover virtually every human disease. RatDEGdb, a knowledge base of the differentially expressed genes (DEGs) of the rat as a model object in biomedical research is a collection of published data on gene expression in rat strains simulating arterial hypertension, age-related diseases, psychopathological conditions and other human afflictions. The current release contains information on 25,101 DEGs representing 14,320 unique rat genes that change transcription levels in 21 tissues of 10 genetic rat strains used as models of 11 human diseases based on 45 original scientific papers. RatDEGdb is novel in that, unlike any other biomedical database, it offers the manually curated annotations of DEGs in model rats with the use of independent clinical data on equal changes in the expression of homologous genes revealed in people with pathologies. The rat DEGs put in RatDEGdb were annotated with equal changes in the expression of their human homologs in affected people. In its current release, RatDEGdb contains 94,873 such annotations for 321 human genes in 836 diseases based on 959 original scientific papers found in the current PubMed. RatDEGdb may be interesting first of all to human geneticists, molecular biologists, clinical physicians, genetic advisors as well as experts in biopharmaceutics, bioinformatics and personalized genomics. RatDEGdb is publicly available at https://www.sysbio.ru/RatDEGdb.
Животные модели, используемые в биомедицинских исследованиях, в настоящее время охватывают практически весь известный спектр заболеваний человека. База знаний RatDEGdb по дифференциально экспрессирующимся генам (ДЭГ) крысы как модельного объекта в биомедицинских исследованиях представляет собой коллекцию опубликованных данных по экспрессии генов у крыс разных линий, предназначенных для изучения артериальной гипертонии, болезней пожилого возраста, психопатологических состояний и других заболеваний человека. Текущий выпуск RatDEGdb содержит 25 101 ДЭГ, представляющих 14 320 уникальных генов крысы, которые изменяют уровень транскрипции в 21 ткани 10 генетических линий крысы в качестве моделей 11 заболеваний человека согласно 45 оригинальным научным статьям. Новшество RatDEGdb по сравнению с другими биомедицинскими базами данных заключается в курируемой аннотации отклонений ДЭГ крысы как модельного объекта с использованием независимых клинических данных об однонаправленных изменениях экспрессии гомологичных генов, выявленных у людей при различных патологиях. Собранные ДЭГ крыс были аннотированы однонаправленными изменениями экспрессии гомологичных им генов человека у больных людей относительно здоровых. К настоящему времени выпуск RatDEGdb содержит 94 873 такие аннотации для 321 гена человека при 836 заболеваниях согласно 959 оригинальным научным статьям, найденным в текущем выпуске базы данных PubMed. Представленная база знаний может быть интересна в первую очередь специалистам по генетике человека, молекулярным биологам, клиницистам и генетическим консультантам, а также специалистам в области биофармацевтики, биоинформатики и персонализированной геномики. RatDEGdb является общедоступной (https://www.sysbio.ru/RatDEGdb).

African swine fever (ASF) is an acute, highly contagious and deadly infectious disease. It is a threat to animal health with major potential economic and societal impact. Despite decades of ASF vaccine research, still some gaps in knowledge are hindering the development of a functional vaccine. Worth mentioning are gaps in understanding the mechanism of ASF infection and immunity, as well as the fact that - in case of this disease - virus proteins, so-called protective antigens, responsible for inducing protective immune responses in pigs are not identified yet. In this paper we elaborate on a methodology to identify protective antigens based on epitope mapping by microarray technology. High density peptide microarrays, combined with fluorescence scanning, have been used to analyze the interaction of peptide sequences of African swine fever virus (ASFV) proteins with antibodies present in inactivated serum from infected and healthy animals. The study evidenced ASFV proteins already under the radar for vaccine development, such as p54, and identified specific sequences in those proteins that may become the focus for future vaccine candidates. Such methodology is amenable to automation and high-throughput and may help developing better targeting for next generation vaccines.

The world population is experiencing colossal growth and thus demand for food, leading to an increase in the use of pesticides. Persistent pesticide contamination, such as carbendazim, remains a pressing environmental concern, with potentially long-term impacts on aquatic ecosystems. In the present study, Daphnia magna was exposed to carbendazim (5 µg L-1) for 12 generations, with the aim of assessing gene transcription alterations induced by carbendazim (using a D. magna custom microarray). The results showed that carbendazim caused changes in genes involved in the response to stress, DNA replication/repair, neurotransmission, ATP production, and lipid and carbohydrate metabolism at concentrations already found in the environment. These outcomes support the results of previous studies, in which carbendazim induced genotoxic effects and reproduction impairment (increasing the number of aborted eggs with the decreasing number of neonates produced). The exposure of daphnids to carbendazim did not cause a stable change in gene transcription between generations, with more genes being differentially expressed in the F0 generation than in the F12 generation. This could show some possible daphnid acclimation after 12 generations and is aligned with previous multigenerational studies where few ecotoxicological effects at the individual and populational levels and other subcellular level effects (e.g., biochemical biomarkers) were found.

Among malignant neoplasms, pancreatic ductal adenocarcinoma (PDAC) has one of the highest fatality rates due to its late detection. Therefore, it is essential to discover a noninvasive, early, specific, and sensitive diagnostic method. MicroRNAs (miRNAs) are attractive biomarkers because they are accessible, highly specific, and sensitive. It is crucial to find miRNAs that could be used as possible biomarkers because PDAC is the eighth most common cause of cancer death in Mexico. With the help of microRNA microarrays, differentially expressed miRNAs (DEmiRNAs) were found in PDAC tissues. The presence of these DEmiRNAs in the plasma of Mexican patients with PDAC was determined using RT-qPCR. Receiver operating characteristic curve analysis was performed to determine the diagnostic capacity of these DEmiRNAs. Gene Expression Omnibus datasets (GEO) were employed to verify our results. The Prisma V8 statistical analysis program was used. Four DEmiRNAs in plasma from PDAC patients and microarray tissues were found. Serum samples from patients with PDAC were used to validate their overexpression in GEO databases. We discovered a new panel of the two miRNAs miR-222-3p and miR-221-3p that could be used to diagnose PDAC, and when miR-221-3p and miR-222-3p were overexpressed, survival rates decreased. Therefore, miR-222-3p and miR-221-3p might be employed as noninvasive indicators for the diagnosis and survival of PDAC in Mexican patients.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients\' progression in lung cancer.
METHODS: Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.
RESULTS: A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.
CONCLUSIONS: Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.