Search

Targeted delivery of therapeutic anticancer chimeric molecules enhances drug efficacy. Numerous studies have focused on developing novel treatments by employing cytokines, particularly interleukins, to inhibit the growth of cancer cells. In the present study, we fused interleukin 24 with the tumor-targeting peptide P20 through a rigid linker to selectively target cancer cells. The secondary structure, tertiary structure, and physicochemical characteristics of the constructed chimeric IL-24-P20 protein were predicted by using bioinformatics tools. In-silico analysis revealed that the fusion construct has a basic nature with 175 amino acids and a molecular weight of 20 kDa. By using the Rampage and ERRAT2 servers, the validity and quality of the fusion protein were evaluated. The results indicated that 93% of the chimeric proteins contained 90.1% of the residues in the favoured region, resulting in a reliable structure. Finally, docking and simulation studies were conducted via ClusPro and Desmond Schrödinger, respectively. Our results indicate that the constructed fusion protein exhibits excellent quality, interaction capabilities, validity, and stability. These findings suggest that the fusion protein is a promising candidate for targeted cancer therapy. [ABSTRACT FROM AUTHOR]

Background: Migraine is a highly prevalent and complex neurovascular disease. However, the currently available therapeutic drugs often fall to adequately meet clinical needs due to limited effectiveness and numerous undesirable side effects. This study aims to identify putative novel targets for migraine treatment through proteome-wide Mendelian randomization (MR). Methods: We utilized MR to estimate the causal effects of plasma proteins on migraine and its two subtypes, migraine with aura (MA) and without aura (MO). This analysis integrated plasma protein quantitative trait loci (pQTL) data with genome-wide association studies (GWAS) findings for these migraine phenotypes. Moreover, we conducted a phenome-wide MR assessment, enrichment analysis, protein–protein interaction networks construction, and mediation MR analysis to further validate the pharmaceutical potential of the identified protein targets. Results: We identified 35 protein targets for migraine and its subtypes (p < 8.04 × 10–6), with prioritized targets showing minimal side effects. Phenome-wide MR identified novel protein targets—FCAR, UBE2L6, LATS1, PDCD1LG2, and MMP3—that have no major disease side effects and interacted with current acute migraine medication targets. Additionally, MMP3, PDCD1LG2, and HBQ1 interacted with current preventive migraine medication targets. The causal effects of plasma protein on migraine were partly mediated by plasma metabolites (proportion of mediation from 3.8% to 21.0%). Conclusions: A set of potential protein targets for migraine and its subtypes were identified. These proteins showed rare side effects and were responsible for biological mechanisms involved in migraine pathogenesis, indicating priority for the development of migraine treatments. [ABSTRACT FROM AUTHOR]

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online. [ABSTRACT FROM AUTHOR]

Granulosa cells (GCs) play crucial roles in oocyte maturation. Through gap junctions and extracellular vesicles, they mediate the exchange of molecules such as microRNAs and messenger RNAs. Different ovarian cell types exhibit unique gene expression profiles, reflecting their specialized functions and stages. By combining RNA-seq data from various cell types forming the follicle, we aimed at capturing a wide range of expression patterns, offering insights into the functional diversity and complexity of the transcriptome regulation across GCs. Herein, we performed an integrated bioinformatics analysis of RNA sequencing datasets present in public databases, with a unique and standardized workflow., By combining the data from different studies, we successfully increased the robustness and reliability of our findings and discovered novel genes, miRNAs, and signaling pathways associated with GCs function and oocyte maturation. Moreover, our results provide a valuable resource for further wet-lab research on GCs biology and their impact on oocyte development and competence. [ABSTRACT FROM AUTHOR]

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient's circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins. [ABSTRACT FROM AUTHOR]

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer. [ABSTRACT FROM AUTHOR]

Backgrounds: Homeobox C6 (HOXC6) is a gene that encodes for a transcription factor involved in various cellular processes, including development and differentiation, and regulates cancer progression. However, the carcinogenesis and effect of HOXC6 in lung adenocarcinoma (LUAD) still need further investigation. Methods: The differential HOXC6 expression levels at the mRNA and protein level were explored in multiple public datasets, including The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) dataset. Gene Expression Omnibus (GSE31210), International Cancer Genome Consortium (ICGC) datasets and the LUAD sample from Affiliated Hospital of Guangxi Medical University. We also investigated the relation between HOXC6 expression and clinicopathologic indexes. Furthermore, the correlation of immune infiltration, drug responsiveness and HOXC6 were explored. Results: The upregulated HOXC6 expressions at mRNA and protein levels were found in LUAD tissues compared to the normal lung tissues. Besides, the relatively shorter overall survival time, worse T and N stages, and lower immune scores were found in the high-expression HOXC6 subgroup. Notably, T cells regulatory (Tregs), Macrophages M0, and Plasma cells had the higher infiltration levels in the high-HOXC6 expression subgroup, while NK cells activated, Monocytes, Dendritic cells resting, and Mast cells resting had the lower infiltration levels. In drug sensitivity analysis, we revealed that LUAD patients with high-HOXC6 expression may be more susceptible to Camptothecin, Cytarabine, Docetaxel, Elesclomol, Rapamycin, Sorafinib, Temsirolimus, and Vorinostat. Conclusions: Taken together, there is a great potential for HOXC6 to become a prognosis biomarker and contribute to develop treatment strategies for LUAD patients. Further mechanism exploration and drug development for HOXC6 are needed. [ABSTRACT FROM AUTHOR]

Background: Febrile urinary tract infections (UTIs) are among the most severe bacterial infections in infants, in which a subset of patients develops complications. Identifying infants at risk of recurrent infections or kidney damage based on clinical signs is challenging. Previous observations suggest that genetic factors influence UTI outcomes and could serve as predictors of disease severity. In this study, we conducted a nationwide survey of infant genotypes to develop a strategy for infection management based on individual genetic risk. Our aims were to identify genetic susceptibility variants for renal scarring (RS) and genetic host factors predisposing to dilating vesicoureteral reflux (VUR) and recurrent UTIs. Methods: To assess genetic susceptibility, we collected and analyzed DNA from blood using exome genotyping. Disease-associated genetic variants were identified through bioinformatics analysis, including allelic frequency tests and odds ratio calculations. Kidney involvement was defined using dimercaptosuccinic acid (DMSA) scintigraphy. Results: In this investigation, a cohort comprising 1087 infants presenting with their first episode of febrile UTI was included. Among this cohort, a subset of 137 infants who underwent DMSA scanning was subjected to gene association analysis. Remarkable genetic distinctions were observed between patients with RS and those exhibiting resolved kidney involvement. Notably, the genetic signature indicative of renal scarring prominently featured mitochondrial genes. Conclusions: In this nationwide study of genetic susceptibility to RS after febrile UTIs in infancy, we identified a profile dominated by mitochondrial polymorphisms. This profile can serve as a predictor of future complications, including RS and recurrent UTIs. [ABSTRACT FROM AUTHOR]

Background: Congenital anomalies of the kidney and urinary tract (CAKUT) represent a frequent cause of pediatric kidney failure. CNVs, as a major class of genomic variations, can also affect miRNA regions. Common CNV corresponding miRNAs (cCNV-miRNAs) are functional variants regulating crucial processes which could affect urinary system development. Thus, we hypothesize that cCNV-miRNAs are associated with CAKUT occurrence and its expressivity. Methods: The extraction and filtering of common CNVs, identified in control samples deposited in publicly available databases gnomAD v2.1 and dbVar, were coupled with mapping of miRNA sequences using UCSC Genome Browser. After verification of the mapped miRNAs using referent miRBase V22.1, prioritization of cCNV-miRNA candidates has been performed using bioinformatic annotation and literature research. Genotyping of miRNA gene copy numbers for MIR9-3, MIR511, and MIR1299, was conducted on 221 CAKUT patients and 192 controls using TaqMan™ technology. Results: We observed significantly different MIR9-3 and MIR1299 gene copy number distribution between CAKUT patients and controls (Chi-square, P = 0.006 and P = 0.0002, respectively), while difference of MIR511 copy number distribution showed nominal significance (Chi-square, P = 0.027). The counts of less and more than two of MIR1299 copy numbers were more frequent within CAKUT patients compared to controls (P = 0.01 and P = 0.008, respectively) and also in cohort of patients with anomalies of the urinary tract compared to controls (P = 0.016 and P = 0.003, respectively). Conclusions: Copy number variations of miRNA genes represent a novel avenue in clarification of the inheritance complexity in CAKUT and provide potential evidence about the association of common genetic variation with CAKUT phenotypes. [ABSTRACT FROM AUTHOR]

Commercial short tandem repeat (STR) kits exclusively contain human-specific primers; however, various non-human organisms with high homology to the STR kit's primer sequences can cause cross-reactivity. Owing to the proprietary nature of the primers in STR kits, the origins and sequences of most non-specific peaks (NSPs) remain unclear. Such NSPs can complicate data interpretation between the casework and reference samples; thus, we developed "NSPlex", an efficient method to discover the biological origins of NSPs. We used leftover STR kit amplicons after capillary electrophoresis and performed advanced bioinformatics analyses using next-generation sequencing followed by BLAST nucleotide searches. Using our method, we could successfully identify NSP generated from PCR amplicons of a sample mixture of human DNA and DNA extracted from matcha powder (finely ground powder of green tea leaves and previously known as a potential source of NSP). Our results showed our method is efficient for NSP analysis without the need for the primer information as in commercial STR kits. [ABSTRACT FROM AUTHOR]

Background: Type-2 diabetes mellitus is a metabolic disorder characterised by hyperglycemia and insulin resistance. This study aims to explore the role and mechanism of DIRAS family GTPase 3 (DIRAS3) in mediating pancreatic β-cell death and insulin secretion. Materials and Methods: A bioinformatic analysis of the GSE118230 and GSE150281 datasets was performed to screen differentially expressed genes. The pancreatic β-cell lines, INS-1 and MIN6, were treated with palmitic acid (PA) to mimic the cell models of type-2 diabetes mellitus. CCK-8 assay, 5-ethynyl-2'-deoxyuridine staining, flow cytometry, enzyme-linked immunoassay, immunofluorescence, qRT-PCR, and western immunoblotting were conducted to illustrate the role of DIRAS3 in the cell models. Results: Unlike in normal controls, DIRAS3 was highly expressed in PA-treated pancreatic β-cells in a dose- and time-dependent manner. Moreover, the silencing of DIRAS3 in the INS-1 cells attenuated PA-induced cell loss by improving cell proliferation and inhibiting apoptosis and prevented the PA-induced impairment of insulin secretion. Consistently, the overexpression of DIRAS3 in the MIN6 cells accelerated PA-induced cell loss and impaired insulin secretion. A Gene Set Enrichment Analysis predicted that phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) was a downstream signalling pathway of DIRAS3, as the inhibitory effects of DIRAS3 on the activation of the PI3K/AKT signalling pathway were confirmed in the INS-1 and MIN6 cells. Moreover, the PI3K inhibitor, LY294002, effectively reversed the protective effects of DIRAS3 silencing on the INS-1 cells. Conclusion: DIRAS3 was highly expressed in the cell models of type-2 diabetes mellitus, contributing to PA-induced cell death and impaired insulin secretion in pancreatic β-cells through the inhibition of the PI3K/AKT signalling pathway. [ABSTRACT FROM AUTHOR]

Endometrial carcinoma is one of most common malignant tumors in women, and ferroptosis is closely related to the development and treatment of endometrial carcinoma. The aim of this study was to screen ferroptosis-related genes associated with endometrial carcinoma and predict targeted drugs through bioinformatics. 761 differentially expressed genes were obtained by the dataset GSE63678 from the GEO database, and most of the genes were enriched in the KEGG_CELL_CYCLE and KEGG_OOCYTE_MEIOSIS signaling pathways. 22 ferroptosis-differentially expressed genes were obtained by intersection with the FerrDb database. These genes were involved in biological processes including macromolecular complex assembly and others, and involved in signal pathways including glutathione metabolism, p53 signaling pathway and others. CDKN2A, IDH1, NRAS, TFRC and GOT1 were obtained as hub genes by PPI network analysis. GEPIA showed that CDKN2A, IDH1, NRAS and TFRC were significantly expressed in endometrial carcinoma. Immunohistochemical results showed that CDKN2A, NRAS and TFRC were significantly expressed in endometrial carcinoma clinical tissue samples. The ROC constructed by TCGA database showed that CDKN2A, NRAS and TFRC had significant value in the diagnosis of endometrial carcinoma, and all had prognostic efficacy. 136,572-09-3 BOSS and others were identified as potential targeted drugs for endometrial carcinoma targeting ferroptosis. Our study has shown that ferroptosis-related genes CDKN2A, NRAS and TFRC are diagnostic markers of endometrial carcinoma, and 136,572-09-3 BOSS, methyprylon BOSS, daunorubicin CTD 00005752, nitroglycerin BOSS and dUTP BOSS, IRON BOSS, Imatinib mesylate BOSS, 2-Butanone BOSS, water BOSS, and L-thyroxine BOSS may be potential therapeutic drugs. [ABSTRACT FROM AUTHOR]

Enzymes are biological catalysts that accelerate chemical reactions by reducing their activation energy. Enzymes specific environmental conditions to function optimally. Additive molecules and compounds, such as organic solvents, ionic liquids, and deep eutectic solvents, have crucial effects on enzyme behavior by changing activity and stability. However, finding and testing different additives is an expensive process that requires specialists, laboratory equipment, and chemical compounds. Machine learning, which has been present in all fields of science and technology in recent years, is one of the ways to find a suitable additive for our desired enzyme without doing a time-consuming experimental process. In this manuscript, we introduce ADDZYME, a machine learning-based software, to predict the effect of additives on enzyme activity. ADDZYME utilizes an ensemble of extremely randomized trees models alongside physicochemical descriptors to make a prediction. To ease usage, ADDZYME is accompanied by a graphical user interface. ADDZYME is freely available on www.github.com/miladrayka/addzyme for further experiments. [ABSTRACT FROM AUTHOR]

Endoplasmic reticulum stress (ERS) plays an essential role in the development of traumatic brain injury (TBI). We aimed to identify and validate the potential ERS-related genes of TBI through bioinformatics analysis and in vitro cell experiment. A total of 19 TBI and ERS-related genes were obtained from the GeneCards database and Comparative Toxicogenomics Database (CTD). Enrichment analysis primarily enriched in apoptosis. NFE2L2 was identified as a hub gene based on the protein–protein interactions (PPI) network that combined seven ranked methods included in cytoHubba. To further explore the effect of Nrf2, the protein encoded by NFE2L2, on ERS-induced apoptosis, we conducted cell experiments with tert-butylhydroquinone (tBHQ), the classical inducer of Nrf2. Western blot suggested tBHQ pretreatment could diminish ERS and reduce the protein expressions of apoptosis in the primary cultured neuron injury model. These data may establish some theoretical basis for the treatment of TBI and provide inspiration and innovative ideas for clinicians and pathologists to understand TBI comprehensively. [ABSTRACT FROM AUTHOR]

Soy sauce cake (SSC) is a by-product of soy sauce production. SSC often goes underutilized due to its high salt content. To address this, halophilic bacterium was employed for SSC fermentation, aiming to release bioactive peptides in addition to the waste management advantages. Using a bioinformatics approach, in silico proteolysis of soy glycinin which was simulated with six selected proteases mined from the genomes of Virgibacillus spp. yielded 29 non-toxic antioxidant peptides, mainly two to three amino acids in size (250–360 Da). Approximately 59% of these peptides had acidic isoelectric points, negative net charges, and moderate Boman index values. Peptides HL, LW, and TY were predicted as non-allergens with positive human intestinal absorption. Furthermore, submerged fermentation of SSC by Virgibacillus sp. strain CD6 under optimal conditions (2.5% inoculum, 72-h fermentation) produced a hydrolysate with the highest peptide content (2.4 mg/g). This hydrolysate demonstrated potential ferric reducing antioxidant power (FRAP) (57%) and 1,1-diphenyl-2-picryl hydrazine (DPPH) radical scavenging activity (31%). This study pioneers the use of in silico methods and submerged fermentation with halophilic bacterium to enhance the value of high-salt SSC by releasing antioxidant peptides. It addresses waste disposal challenges and reveals new opportunities for SSC in food product development. [ABSTRACT FROM AUTHOR]

Heat stroke (HS) is a severe medical condition characterized by a systemic inflammatory response that may precipitate multi-organ dysfunction, with a particular predilection for inducing profound central nervous system impairments. We aim to employ bioinformatics techniques for the retrieval and analysis of genes associated with heat stroke-induced neurological damage. We performed a comprehensive analysis of the GSE64778 dataset from the Sequence Read Archive, resulting in the identification of 1178 significantly differentially expressed genes (DEGs). We retrieved 2914 genes associated with heat stroke from the GeneCards database and 2377 genes associated with heat stroke from the Comparative Toxicogenomics Database (CTD). The intersection of the top 300 DEGs in the GSE64778 dataset intersected with the search results of GeneCards and CTD, yielding 25 final candidates for DEGs associated with heat stroke. Gene Ontology functional annotation results indicated that the target genes were mainly involved in apoptosis, stress response, and negative regulation of cellular processes and function in processes such as protein dimerization and protein binding. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed a predominant enrichment of candidate target genes within the PI3K-AKT signaling pathway. Subsequent protein–protein interaction network analysis highlighted HSP90aa1 as a central gene, indicating its pivotal role by possessing the highest number of edges among the genes enriched in the PI3K-AKT signaling pathway. Quantitative reverse transcription-polymerase chain reaction analysis performed on blood samples from patients validated the expression of Hsp90aa1 in individuals exhibiting early neurological damage in HS, consistent with the findings from the mRNA bioinformatics analysis. Additionally, the bioinformatics analysis of the upstream microRNAs (miRNAs) regulating HSP90aa1 and the target miRNAs associated with candidate long non-coding RNAs (lncRNAs) identified three lncRNAs, eight miRNAs, and one mRNA in the regulatory network. The DIANA Tools database and algorithms were employed for pathway enrichment and correlation analysis, revealing a significant association between LOC102547734 and MIR-206-3p, with the latter being identified as a target binding site Moreover, the analysis unveiled a correlation between MIR-206-3p and HSP90aa1, implicating the latter as a potential target binding site within the regulatory network. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) offer transformative opportunities in bioinformatics education for medical students by creating interactive experiences. The integration of LLMs enhances educational outcomes through providing accessible code templates, clarifying the function of coding elements, and assisting in error troubleshooting. Here, we demonstrate the practical applications of LLMs with a case study on transcriptome sequencing data processing, a vital component of medical research. However, the reliability of the content that LLMs generate requires rigorous validation. Ensuring the accuracy and appropriateness of the LLM-generated information requires integrating innovative LLMs with traditional educational methods to prepare medical students effectively for future challenges in bioinformatics. [ABSTRACT FROM AUTHOR]

Alcohol abuse, also known as Alcohol Use Disorder (AUD), is a substance dependency psychiatric disorder. We aimed to establish a causal relationship between specific gut microbiota and alcohol abuse using Mendelian Randomisation (MR) and bioinformatics methods. We acquired summary data of genome-wide association studies (GWAS) for gut microbiota and alcohol abuse from the Mibiogen and Finngen databases, respectively. We conducted MR analyses using various methodologies and mapped the single nucleotide polymorphisms (SNPs) to genes via the FUMA GWAS platform. We further performed multiple enrichment analyses and a Multi-variable Mendelian Randomisation (MVMR) approach to examine whether gut microbiota influences alcohol abuse by modulating neurotransmitter-related amino acids. The MR analysis revealed an inverse relationship between the genus Eubacterium ventriosum group and the Porphyromonadaceae family with alcohol abuse. Gene enrichment analysis showed that these genes are expressed in brain tissue and are involved in addictive disorders, psychiatric conditions, immunological processes, neurotransmitter synthesis and synaptic regulation. MVMR analysis suggested that the Porphyromonadaceae family as well as genus Eubacterium ventriosum group may suppress alcohol abuse through the metabolism of neurotransmitter-related amino acids, especially Tryptophan. The MR analysis and bioinformatics investigations indicate that the genus Eubacterium ventriosum group and Porphyromonadaceae family confer a protective effect against alcohol abuse, potentially through the modulation of synaptic function. [ABSTRACT FROM AUTHOR]

In recent years, as more and more lung-cancer patients have been treated with immunotherapeutic agents, their survival has been prolonged compared to before. It is well known that BTK (Bruton's tyrosine kinase) is predominantly found in cells of the hematopoietic system. However, there is a distinct lack of literature on BTK expression in lung adenocarcinoma (LUAD) patients and its effect on the immune microenvironment. Consequently, the main goal of this investigation was to analyze how BTK expression in lung adenocarcinoma affects its progression, along with its prognostic significance, through the utilization of bioinformatics online resources and publicly available databases. Data on the sequencing results and clinical records of lung adenocarcinoma patients were gathered from The Cancer Genome Atlas (TCGA) database. Based on the expression level of BKT, TCGA categorized lung adenocarcinoma patients into BTK high-expression and low-expression groups. We investigated the effects of BKT on clinicopathologic, genomic, and immunologic characteristics of lung adenocarcinoma patients. We analyzed BTK mRNA expression in tumors and normal tissues using two key resources: Tumor Immuno Estimation Resource 2.0 (TIMER 2.0) and Gene Expression Profiling Interactive Analysis 2 (GEPIA 2). We analyzed the prognosis of the patients using GEPIA2 and validated the results using univariate and multivariate analyses. In addition, we assessed BTK protein expression by Human Protein Atlas (HPA). We sought to elucidate the clinical prognostic significance of BTK in The TCGA using the online tool GEPIA 2. Furthermore, to clarify the biologic roles and pathways linked to BTK, we conducted a genomic enrichment analysis of the information. To predict the proportion of various immune cell infiltrations in the immune microenvironment of lung adenocarcinoma patients diagnosed in the TCGA database, we performed an analysis using the TIMER online tool. Using TIMER and CIBERSORT, the correlation between genes co-expressed with BTK and the corresponding tumor-infiltrating immune cells was explored; finally, the relationship between BTK expression and immune infiltration and immune checkpoints in the TMB group and the high and low groups was analyzed by R language analysis using the TCGA database. The expression of BTK provides some hints about the prognosis of the patients. The high expression of BTK is involved in immune response regulation signaling pathways, leukocyte-mediated immunity, leukocyte intercellular adhesion, graft rejection, and complement. Analysis of the GEPIA 2 database showed that BTK was co-expressed with the genes FGD2, SASH3, NCKAP1L, CD53, ARHGAP30 and LPXN. Increased expression of the above-mentioned genes resulted in increased proportions of CD8 + T cells, memory CD4 + T cells, B cells, macrophages, and dendritic cells, and decreased proportions of Treg cells and TH2 cells. In addition, our study revealed a strong positive correlation between various key immune checkpoints (e.g., PDCD1, CD274, PDCD1LG2, CTLA4, HAVCR2, LAG3, TIGIT, and SIGLEC15) and BTK expression. In conclusion, increased BTK expression in lung adenocarcinoma is closely associated with prolonged survival of lung-cancer patients. Moreover, the genes classified under the BTK high-expression group exhibit significant enrichment in immune-related pathways, suggesting a potential impact on the tumor microenvironment. We investigated the potential of BTK as a tumor suppressor gene in predicting prolonged patient survival. In addition, we further investigated the possibility that BTK further affects the immunotherapeutic response of patients by influencing the microenvironment of tumor immune infiltration, but the relevant mechanisms remain to be further studied. [ABSTRACT FROM AUTHOR]

The EphA family belongs to a large group of membrane receptor tyrosine kinases. Emerging evidence indicates that the EphA family participates in tumour occurrence and progression. Nonetheless, the expression patterns and prognostic values of the nine EphAs in non-small cell lung cancer (NSCLC) have rarely been studied before. In the current study, we comprehensively analysed the expression and prognostic role of EphA family members by different means. The Cancer Genome Atlas and Gene Expression Profiling Interactive Analysis databases were used to investigate the expression of EphAs in NSCLC. The cBioPortal database was applied to analyse the prognostic values and genetic mutations of EphAs.We discovered that the expression of EphA10 was significantly higher in NSCLC tissues than in adjacent noncancerous tissues, and survival analyses showed that a higher level of EphA10 predicted poor prognosis. Further exploration into the role of EphA10 by ESTIMATE, CIBERSORT, and ssGSEA analysis found that it was also related to immune infiltration and higher expression of targets of ICI targets. In conclusion, this study revealed that among the EphA family members, EphA10 played an oncogenic role and was a promising biomarker for poor prognosis and better immunotherapy response in NSCLC. [ABSTRACT FROM AUTHOR]

Neuroblastoma, the deadliest solid tumor in children, exhibits alarming mortality rates, particularly among high-risk cases. To enhance survival rates, a more precise risk stratification for patients is imperative. Utilizing proteomic data from 34 cases with or without N-Myc amplification, we identified 28 differentially expressed ubiquitination-related proteins (URGs). From these, a prognostic signature comprising 6 URGs was constructed. A nomogram incorporating clinical-pathological parameters yielded impressive AUC values of 0.88, 0.93, and 0.95 at 1, 3, and 5 years, respectively. Functional experiments targeting the E3 ubiquitin ligase FBXO42, a component of the prognostic signature, revealed its TP53-dependent promotion of neuroblastoma cell proliferation. In conclusion, our ubiquitination-related prognostic model robustly predicts patient outcomes, guiding clinical decisions. Additionally, the newfound pro-proliferative role of FBXO42 offers a novel foundation for understanding the molecular mechanisms of neuroblastoma. [ABSTRACT FROM AUTHOR]

Background: Inflammatory cytokines such as Interleukin 1β(IL1β), IL6,Tumor Necrosis Factor-α (TNF-α) can inhibit osteoblast differentiation and induce osteoblast apoptosis. PANoptosis, a newly identified type of programmed cell death (PCD), may be influenced by long noncoding RNA (lncRNAs) which play important roles in regulating inflammation. However, the potential role of lncRNAs in inflammation and PANoptosis during osteogenic differentiation remains unclear. This study aimed to investigate the regulatory functions of lncRNAs in inflammation and apoptosis during osteogenic differentiation. Methods and results: High-throughput sequencing was used to identify differentially expressed genes involved in osteoblast differentiation under inflammatory conditions. Two lncRNAs associated with inflammation and PANoptosis during osteogenic differentiation were identified from sequencing data and Gene Expression Omnibus (GEO) databases. Their functionalities were analyzed using diverse bioinformatics methodologies, resulting in the construction of the lncRNA-miRNA-mRNA network. Among these, lncRNA (MIR17HG) showed a high correlation with PANoptosis. Bibliometric methods were employed to collect literature data on PANoptosis, and its components were inferred. PCR and Western Blotting experiments confirmed that lncRNA MIR17HG is related to PANoptosis in osteoblasts during inflammation. Conclusions: Our data suggest that TNF-α-induced inhibition of osteogenic differentiation and PANoptosis in MC3T3-E1 osteoblasts is associated with MIR17HG. These findings highlight the critical role of MIR17HG in the interplay between inflammation, PANoptosis, and osteogenic differentiation, suggesting potential therapeutic targets for conditions involving impaired bone formation and inflammatory responses. [ABSTRACT FROM AUTHOR]

We elucidated the molecular fingerprint of vulnerable excitatory neurons within select cortical lamina of individuals with Down syndrome (DS) for mechanistic understanding and therapeutic potential that also informs Alzheimer's disease (AD) pathophysiology. Frontal cortex (BA9) layer III (L3) and layer V (L5) pyramidal neurons were microisolated from postmortem human DS and age- and sex-matched controls (CTR) to interrogate differentially expressed genes (DEGs) and key biological pathways relevant to neurodegenerative programs. We identified > 2300 DEGs exhibiting convergent dysregulation of gene expression in both L3 and L5 pyramidal neurons in individuals with DS versus CTR subjects. DEGs included over 100 triplicated human chromosome 21 genes in L3 and L5 neurons, demonstrating a trisomic neuronal karyotype in both laminae. In addition, thousands of other DEGs were identified, indicating gene dysregulation is not limited to trisomic genes in the aged DS brain, which we postulate is relevant to AD pathobiology. Convergent L3 and L5 DEGs highlighted pertinent biological pathways and identified key pathway-associated targets likely underlying corticocortical neurodegeneration and related cognitive decline in individuals with DS. Select key DEGs were interrogated as potential hub genes driving dysregulation, namely the triplicated DEGs amyloid precursor protein (APP) and superoxide dismutase 1 (SOD1), along with key signaling DEGs including mitogen activated protein kinase 1 and 3 (MAPK1, MAPK3) and calcium calmodulin dependent protein kinase II alpha (CAMK2A), among others. Hub DEGs determined from multiple pathway analyses identified potential therapeutic candidates for amelioration of cortical neuron dysfunction and cognitive decline in DS with translational relevance to AD. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing large datasets presents challenges due to the identification of nonlinear gene–gene associations and the need to process an ever-growing number of gene pairs and their potential network connections. These challenges mean that some experiments are discarded because the techniques do not support these intense workloads. This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments. To do this, pyEnGNet harnesses CPU and multi-GPU parallel computing resources, efficiently handling large datasets. These implementations have optimised memory management and processing, delivering timely results. We have used synthetic datasets to prove the runtime and intensive workload improvements. In addition, pyEnGNet was used in a real-life study of patients after allogeneic stem cell transplantation with invasive aspergillosis and was able to detect biological perspectives in the study. [ABSTRACT FROM AUTHOR]

Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to discern ferroptosis-related genes (FRGs) linked to non-obstructive azoospermia and investigate the associated molecular mechanisms. Method: A dataset related to azoospermia was retrieved from the Gene Expression Omnibus database, and FRGs were sourced from GeneCards. Ferroptosis-related differentially expressed genes (FRDEGs) were discerned. Subsequently, these genes underwent analyses encompassing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as protein–protein interaction (PPI) networks and assessments of functional similarity. Following the identification of hub genes, an exploration of immune infiltration, single-cell expression, diagnostic utility, and interactions involving hub genes, RNA-binding proteins (RBPs), transcription factors (TFs), microRNAs (miRNAs), and drugs was conducted. Results: A total of 35 differentially expressed FRGs were discerned. These genes demonstrated enrichment in functions and pathways associated with ferroptosis. From the PPI network, eight hub genes were selected. Functional similarity analysis highlighted the potential pivotal roles of HMOX1 and GPX4 in azoospermia. Analysis of immune cell infiltration indicated a significant decrease in activated dendritic cells in the azoospermia group, with notable correlations between hub genes, particularly SAT1 and HMGCR, and immune cell infiltration. Unique expression patterns of hub genes across various cell types in the human testis were observed, with GPX4 prominently enriched in spermatid/sperm. Eight hub genes exhibited robust diagnostic value (AUC > 0.75). Lastly, a comprehensive hub gene-miRNA-TF-RBP-drug network was constructed. Conclusion: In summary, our investigation unveiled eight FRDEGs associated with azoospermia, which hold potential as biomarkers for the diagnosis and treatment of azoospermia. [ABSTRACT FROM AUTHOR]

Aims: Accumulating evidences indicate that abnormalities in tubular lipid metabolism play a crucial role in the development of diabetic kidney disease (DKD). We aim to identify novel lipid metabolism-related genes associated with tubular injury in DKD by utilizing bioinformatics approaches. Methods: Differentially expressed genes (DEGs) between control and DKD tubular tissue samples were screened from the Gene Expression Omnibus (GEO) database, and then were intersected with lipid metabolism-related genes. Hub genes were further determined by combined weighted gene correlation network analysis (WGCNA) and protein–protein interaction (PPI) network. We performed enrichment analysis, immune analysis, clustering analysis, and constructed networks between hub genes and miRNAs, transcription factors and small molecule drugs. Receiver operating characteristic (ROC) curves were employed to evaluate the diagnostic efficacy of hub genes. We validated the relationships between hub genes and DKD with external datasets and our own clinical samples. Results: There were 5 of 37 lipid metabolism-related DEGs identified as hub genes. Enrichment analysis demonstrated that lipid metabolism-related DEGs were enriched in pathways such as peroxisome proliferator-activated receptors (PPAR) signaling and pyruvate metabolism. Hub genes had potential regulatory relationships with a variety of miRNAs, transcription factors and small molecule drugs, and had high diagnostic efficacy. Immune infiltration analysis revealed that 13 immune cells were altered in DKD, and hub genes exhibited significant correlations with a variety of immune cells. Through clustering analysis, DKD patients could be classified into 3 immune subtypes and 2 lipid metabolism subtypes, respectively. The tubular expression of hub genes in DKD was further verified by other external datasets, and immunohistochemistry (IHC) staining showed that except ACACB, the other 4 hub genes (LPL, AHR, ME1 and ALOX5) exhibited the same results as the bioinformatics analysis. Conclusion: Our study identified several key lipid metabolism-related genes (LPL, AHR, ME1 and ALOX5) that might be involved in tubular injury in DKD, which provide new insights and perspectives for exploring the pathogenesis and potential therapeutic targets of DKD. [ABSTRACT FROM AUTHOR]

The Integrated Course Design (ICD), using Fink's taxonomy of significant learning, popularly known as ICD/SL, is a handy way to create a better learning environment for students. It is a learner-centered approach with the desired end-product, but at the same time, it upgrades the teaching by improving the instructors' delivery mechanism. Our goal of this study was to see whether ICD/SL affects students' class participation and academic performance in the "Introduction to Bioinformatics" course offered at the Department of Biological Science, BITS Pilani, Pilani campus, India. Three class groups were chosen for this purpose: 2019–2020 (51 students), 2020–2021 (77 students), and 2021–2022 (72 students). The control group, 2019–2020, received no ICD/SL instruction; the remaining two groups, 2020–2021 and 2021–2022, received ICD/SL instruction that included revised learning goals based on Fink's taxonomy and new teaching and evaluation activities. A Likert scale was utilized to assess students' academic feedback using the Kruskal–Wallis test to determine the P-value. The findings showed that the treatment groups had higher class participation and academic performance in the summative assessment of final grades. In the experimental groups, the class participation was 23 to 27% higher compared to the control group. The absenteeism rate on the course decreased from 14% in 2019–2020 to 9% in 2020–2021 and 4% in 2021–2022. Also, in the treatment groups, 83 to 90% of students were in the High to Excellent category, compared to 74% in the control group. The failure rate of the course decreased from nearly 18 to 10% in 2021–2022 and only 6% in 2020–2021. There were significant differences between the treatment and control groups in class participation and academic performance (P < 0.05). This study has shown that the use of ICD/SL has the potential to improve students' class participation and academic performance. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a prominent public health issue and ranks as the third most prevalent cause of cancer-related mortality on a global scale. The role of long non-coding RNAs (lncRNAs) in cancer is not yet fully understood, particularly in relation to GC development. The objective of this study was to examine the expression levels of lncRNAs in GC tissues using a bioinformatics-based ranking approach. A bioinformatics methodology was employed to prioritize lncRNAs that are hypothesized to play a role in GC tumorigenesis. Moreover, a selection was made for experimental validation of the highest-ranked lncRNAs, which include HCG18, OIP5-AS1, FGD5-AS1, and NORAD. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to confirm the results obtained from bioinformatics analysis in a total of 35 GC samples and their corresponding adjacent non-tumoral samples. Receiver operating characteristic (ROC) curves and the corresponding area under the ROC curve (AUC) were utilized to evaluate the diagnostic efficacy of the lncRNAs. The bioinformatics analysis revealed that the lncRNA HCG18 is the highest-ranked lncRNA associated with GC. Furthermore, the expression levels of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were found to be significantly elevated in GC samples when compared to adjacent non-tumoral samples. The calculated values for the AUC of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were 0.80, 0.74, 0.73, and 0.71, respectively. The results of the study indicate that the lncRNAs HCG18, OIP5-AS1, FGD5-AS1, and NORAD may play a role in the development of GC. Additionally, the present study revealed that utilizing bioinformatic techniques can prove to be a highly effective strategy in identifying potential lncRNAs pertinent to the progression of GC. [ABSTRACT FROM AUTHOR]

Pancreatic cancer (PC) is one of the world's most aggressive and deadly cancers, owing to non-specific early clinical symptoms, late-stage diagnosis, and poor survival. Therefore, it is critical to identify specific biomarkers for its early diagnosis. Annexin A2 (ANXA2) is a calcium-dependent phospholipid-binding protein that has been reported to be upregulated in several cancer types, making it an emerging biomarker and potential cancer therapeutic target. However, the mechanism underlying the regulation of ANXA2 overexpression is still unclear. It is well established that genetic and epigenetic alterations may lead to widespread dysregulation of gene expression. Hence, in this study, we focused on exploring the regulatory mechanism of ANXA2 by investigating the transcriptional profile, methylation pattern, somatic mutation, and prognostic value of ANXA2 in PC using several bioinformatics databases. Our results revealed that the expression levels of ANXA2 were remarkably increased in PC tissues comparing to normal tissues. Furthermore, the high expression of ANXA2 was significantly related to the poor prognosis of PC patients. More importantly, we demonstrated for the first time that the ANXA2 promoter is hypomethylated in PC tissues compared to normal tissues which may result in ANXA2 overexpression in PC. However, more experimental research is required to corroborate our findings. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD. [ABSTRACT FROM AUTHOR]

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML. [ABSTRACT FROM AUTHOR]

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes. [ABSTRACT FROM AUTHOR]

Immune response is pivotal in the secondary injury of spinal cord injury (SCI). Polarization of macrophages (MΦ) influences the immune response in the secondary injury, which is regulated by several immune-related proteins. M2Φ plays the immunomodulatory role in the central nervous system. This study used bioinformatic analysis and machine algorithms to screen hub immune-related proteins after SCI and experimentally investigate the role of the target protein in the M2Φ polarization and immunomodulation in rats and in vitro after SCI. We downloaded GSE151371 and GSE45006, hub immune-related genes were screened using machine learning algorithms, and the expression of S100A9 was verified by datasets. Allen's weight-drop injury SCI model in Sprague–Dawley rat and bone marrow-derived rat MΦ with myelin debris model were used to study the effects of S100A9 on M2Φ polarization and immunomodulation at the lesion site and in vitro. Bioinformatic analysis showed that S100A9 acts as a hub immune-related gene in the SCI patients and rats. S100A9 increased at the lesion site in SCI rats, and its inhibition reduced CD206 and ARG-1 expression. Exogenous S100A9 promoted CD206 and ARG-1 expression in MΦ. S100A9 also increased the expression of PD-L1 and decreased MHC II at the lesion site in SCI rats and MΦ with myelin debris, and enhanced mitochondrial activity in rat MΦ with myelin debris. In conclusion, S100A9 is an indispensable factor in the immune process in secondary injury following SCI. [ABSTRACT FROM AUTHOR]

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Despite occupying only a small portion of the Earth's surface, tropical rainforests are some of the most diverse ecosystems on the planet. Given the hyperdiversity of tropical rainforests, these ecosystems are considered essential for the preservation of global biodiversity. However, many of them are located in economically marginalized countries, which frequently suffer from uncontrolled exploitation of their natural resources. In this sense, biodiversity information can help in planning conservation strategies and designing protection measures. Despite this, recording biodiversity is a challenging and time-consuming task that usually requires the involvement of important financial and manpower resources. So, any strategy to facilitate or accelerate this labor could be of significant interest for ecological and conservation purposes. In this review, we highlight why we consider that routinely incorporating metabarcoding strategies can facilitate biodiversity surveillance in tropical rainforests. As insects are key players in tropical ecosystems and are a threatened, hyperdiverse group, here we used them to exemplify the main challenges that the implementation of metabarcoding approaches faces in these complex environments, discussing some methodological obstacles for their implementation. We also emphasize the significant gap between the number of published studies using metabarcoding for biodiversity monitoring in tropical regions with respect to other (often less diverse) regions of the planet. We consider that acknowledging and addressing this disparity will encourage the adoption of these strategies in tropical ecosystems, as these regions are in need—possibly more than any other—of innovative approaches to assess their diversity. [ABSTRACT FROM AUTHOR]

The obvious degeneration of articular cartilage occurs in the late stage of osteonecrosis of the femoral head (ONFH), which aggravates the condition of ONFH. This study aimed to demonstrate aberrant activation of autophagy processes in ONFH chondrocytes through bioinformatics and to predict and identify relevant hub genes and pathways. Differentially expressed genes (DEGs) were identified using R software in the GSE74089 dataset from the GEO database. DEGs were crossed with the Human Autophagy Database (HADb) autophagy genes to screen out autophagy-related differential genes (AT-DEGs). GSEA, GSVA, GO, and KEGG pathway enrichment analyses of AT-DEGs were performed. The STRING database was used to analyze the protein-protein interaction (PPI) of the AT-DEGs network, and the MCODE and CytoHubba plugin in the Cytoscape software was used to analyze the key gene cluster module and screen the hub genes. The PPI network of hub genes was constructed using the GeneMANIA database, and functional enrichment and gene connectivity categories were analyzed. The expression levels of hub genes of related genes in the ONFH patients were verified in the dataset GSE123568, and the protein expression was verified by immunohistochemistry in tissues. The analysis of DEGs revealed abnormal autophagy in ONFH cartilage. AT-DEGs in ONFH have special enrichment in macroautophagy, autophagosome membrane, and phosphatidylinositol-3-phosphate binding. In the GSE123568 dataset, it was also found that ATG2B, ATG4B, and UVRAG were all significantly upregulated in ONFH patients. By immunohistochemistry, it was verified that ATG2B, ATG4B, and UVRAG were significantly overexpressed. These three genes regulate the occurrence and extension of autophagosomes through the PI3KC3C pathway. Finally, we determined that chondrocytes in ONFH undergo positive regulation of autophagy through the corresponding pathways involved in three genes: ATG2B, ATG4B, and UVRAG. [ABSTRACT FROM AUTHOR]

Background: NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. Objective: We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. Methods: We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. Results: Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. Conclusion: The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers. [ABSTRACT FROM AUTHOR]

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design. [ABSTRACT FROM AUTHOR]

Introduction: Hepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes. Methods: The GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein–protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database. Results: A total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients. Conclusions: Through comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM. [ABSTRACT FROM AUTHOR]

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/. Scientific contribution This work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application. [ABSTRACT FROM AUTHOR]

Background: Nephrotic syndrome (NS) is a common kidney disease in children. While Steroid-Sensitive Nephrotic Syndrome (SSNS) is frequently observed, Steroid-Resistant Nephrotic Syndrome (SRNS) has a poor prognosis and often leads to chronic kidney disease. The pathogenesis of SRNS is complex, with immunological modulation of T helper subtypes 1 and 2 cytokines increasing susceptibility to the disease. Currently, no established biomarkers can accurately predict SRNS. However, a group of cytokines might serve as potential indicators of responsiveness, aiding in the identification of patients with SRNS. The discovery of these cytokines as novel biomarkers for early diagnosis could greatly benefit patients. This includes preventing the adverse effects of glucocorticoid treatment and enabling a timely transition to more effective therapeutic alternatives. Methods: This study aims to investigate the association between the gene expression patterns of cytokines, including IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, IL-17A, NF-κB, and TNFα, in healthy participants (n = 100), SSNS patients (n = 100), and SRNS patients (n = 100). Using qRT-PCR, followed by Receiver-operating characteristic analysis, the study assesses their potential as biomarkers. Additionally, clinicodemographic data were analyzed, and bioinformatic analyses such as coexpression analysis, gene enrichment, pathway analysis, and Cytoscape were performed to enhance our understanding of the inflammatory cascade initiating podocyte injury in NS. Results: The results of our study suggest that specific candidate genes, including IL-2, IL-5, IL-6, IL-9, IL-17A, IL-10, IL-13, and TNFα, exhibit upregulation and hold significant importance, with an Area Under the Curve value of 0.9. Conclusion: These genes have the potential to serve as valuable prognostic and management tools for NS, forming a promising panel of inflammatory gene biomarkers. Furthermore, conducting an extensive analysis that integrates cytokine genes with their respective targeted microRNAs could offer deeper insights into the pathogenesis of the disease. [ABSTRACT FROM AUTHOR]

Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was designed to develop a HCM signature applying bioinformatics and machine learning methods. Data of HCM and normal tissues were obtained from public databases to screen differentially expressed genes (DEGs) using the R software limma package. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed for enrichment analysis of HCM-associated DEGs. Hub genes for HCM were determined using weighted gene co-expression network analysis (WGCNA) together with two machine learning algorithms (SVM-RFE and LASSO). Finally, we introduced a zebrafish model to simulate changes in the hub genes in the HCM and to observe their effects on cardiac disease development. The mRNA expression data from a total of 106 HCM tissues and 39 normal samples were collected and we screened 157 DEGs. Enrichment analysis showed that immune pathways played an important role in the pathogenesis of HCM. Three hub genes (FCN3, MYH6 and RASD1) were identified using WGCNA, SVM-RFE, and LASSO analysis. In a zebrafish model, knockdown of MYH6 and RASD1 resulted in cardiac malformations with reduced ventricular capacity and heart rate, which validated the clinical significance of these genes in the diagnosis of HCM. Based on machine learning algorithms, our study created a signature with potential impact on cardiac function and cardiac quality index for HCM. The current findings had important implications for the early diagnosis and treatment of HCM. [ABSTRACT FROM AUTHOR]

The available Epstein Barr virus vaccine has tirelessly harnessed the gp350 glycoprotein as its target epitope, but the result has not been preventive. Right here, we designed a global multi-epitope vaccine for EBV; with special attention to making sure all strains and preventive antigens are covered. Using a robust computational vaccine design approach, our proposed vaccine is armed with 6–16 mers linear B-cell epitopes, 4–9 mer CTL epitopes, and 8–15 mer HTL epitopes which are verified to induce interleukin 4, 10 & IFN-gamma. We employed deep computational mining coupled with expert intelligence in designing the vaccine, using human Beta defensin-3—which has been reported to induce the same TLRs as EBV—as the adjuvant. The tendency of the vaccine to cause autoimmune disorder is quenched by the assurance that the construct contains no EBNA-1 homolog. The protein vaccine construct exhibited excellent physicochemical attributes such as Aliphatic index 59.55 and GRAVY − 0.710; and a ProsaWeb Z score of − 3.04. Further computational analysis revealed the vaccine docked favorably with EBV indicted TLR 1, 2, 4 & 9 with satisfactory interaction patterns. With global coverage of 85.75% and the stable molecular dynamics result obtained for the best two interactions, we are optimistic that our nontoxic, non-allergenic multi-epitope vaccine will help to ameliorate the EBV-associated diseases—which include various malignancies, tumors, and cancers—preventively. [ABSTRACT FROM AUTHOR]

A macroscopic perspective is indispensable for understanding the intricate relationship between deubiquitinases and tumorigenesis. Proteomics has been proposed as a viable approach for elucidating the complex role of deubiquitylation in cellular progression. Instead of studying the function of a single ubiquitinase, research on a deubiquitinase family with similar catalytic core(s) may provide a new perspective for the pathological understanding of cancer. The Ubiquitin C-terminal hydrolase L (UCHL) family consists of four members: UCHL1, UCHL3, UCHL5, and BRAC1 associated protein-1 (BAP1), and they have been implicated in tumorigenesis and metastasis. Some members are considered hallmarks of intracranial lesions, colon cancer, chromatin remodeling, and histone stability. The present study uncovered an unknown correlation between the UCHL family and renal cancer. We discovered that UCHLs exhibit diverse regulatory effects in renal cancer, establishing connections between the renal cancer and truncated gene mutations, mitochondrial energetic metastasis, immune cell infiltration, and chromosomal stability of UCHLs family. Notably, we found that the increase of UCHL5 expression in renal cancer cells decreases the antigen processing and presentation of RCC tumor-infiltrating B cells. Further research identified that the expression of UCHL5 in RCC tumors is correlated with transport proteins, which led us to find that the abundance of UCHL5 in the blood of late-stage renal cell cancer patients is upregulated from 18 ng/L to 500 ng/L. Therefore, we propose that the abundance of UCHL5 in patients' blood can be a possible indicator of poor prognosis for renal cell cancer. Highlights: Our study established the unknown relationship between UCHLs and renal cancer tumorigenesis. UCHL3 and UCHL5 showed immunosuppressive in CD8+ T cells and B cells. The blood abundance of UCHL5 is a putative prognostic marker of RCC. [ABSTRACT FROM AUTHOR]

Bioinformatics tools are essential for performing analyses in the omics sciences. Given the numerous experimental opportunities arising from advances in the field of omics and easier access to high-throughput sequencing platforms, these tools play a fundamental role in research projects. Despite the considerable progress made possible by the development of bioinformatics tools, some tools are tailored to specific analytical goals, leading to challenges for non-bioinformaticians who need to integrate the results of these specific tools into a customized pipeline. To solve this problem, we have developed the BioPipeline Creator, a user-friendly Java-based GUI that allows different software tools to be integrated into the repertoire while ensuring easy user interaction via an accessible graphical interface. Consisting of client and server software components, BioPipeline Creator provides an intuitive graphical interface that simplifies the use of various bioinformatics tools for users without advanced computer skills. It can run on less sophisticated devices or workstations, allowing users to keep their operating system without having to switch to another compatible system. The server is responsible for the processing tasks and can perform the analysis in the user's local or remote network structure. Compatible with the most important operating systems, available at https://github.com/allanverasce/bpc.git. [ABSTRACT FROM AUTHOR]

Purpose: Colorectal cancer is one of the major leading causes of death worldwide, and available treatments for advanced colorectal cancer are not successful. Therefore, early detection of colorectal cancer is essential to improve patient survival, and biomarkers are potential tools to achieve this goal. Considering the key role of lncRNAs in cancers, the aim of this study is to identify lncRNAs involved in colorectal cancer as new potential prognosis biomarkers for CRC. Methods: In this observational study, gene expression data obtained from the TCGA database were analyzed, Identification of differentially expressed mRNAs, miRNAs, and lncRNAs was performed, and ceRNA network was drawn. Also, survival analysis of patients was performed in order to identify potential biomarkers related to the diagnosis and prognosis of colon cancer. After confirming the results using the GSE39582 dataset, the expression of target lncRNAs in colorectal tumor tissues was also investigated to confirm the bioinformatic data. Results: Analysis of the TCGA data showed that the expression of three lncRNAs—SNHG7, ASMTL-AS1, and LINC02604—that had the highest interaction with other miRNAs and mRNAs identified based on the ceRNA network was increased in colorectal cancer. Also, based on the ceRNA network, three microRNAs, hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423-5p, and eight mRNAs, including CPA4, MSI2, RRM2, IGF2BP1, ONECUT2, HMGA1, SOX4, and SRM, were associated with all three mentioned lncRNAs, the expression of microRNAs was decreased and the expression of mRNAs was increased. By enrichment analysis, it was found that the target lncRNAs are involved in the processes of cell proliferation, apoptosis, and metastasis, indicating their importance in the development and malignancy of colorectal cancer. Furthermore, Kaplan–Meier analysis showed a significant increase in mortality in patients with higher expression levels of these lncRNAs. Analysis of the GSE39582 dataset, and real-time RT-PCR analysis, confirmed our bioinformatic results. Also, ROC analysis showed that SNHG7 was a relatively good promising biomarker (AUC = 0.73, p value = 0.02), while ASMTL-AS1 (AUC = 0.92, p value < 0.0001) and LINC02604 (AUC = 1.00, p value < 0.0001) emerged as excellent diagnostic biomarkers in colorectal cancer. Conclusion: It seems that increased expression of lncRNAs ASMTL-AS1 and LINC02604 can serve as molecular biomarkers for CRC, possibly through the sponge hsa-let-7d-5p, hsa-mir-92a-3p, and hsa-mir-423 5p, which increases target mRNAs, which are effective in the carcinogenesis process. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is one of the most common digestive tract malignant tumors in the world. At the time of initial diagnosis, it frequently presents with local or distant metastasis, contributing to poor prognosis in patients. Neutrophil extracellular traps (NETs) constitute a mechanism employed by neutrophils that is intricately associated with tumor progression, prognosis, and response to immunotherapy and chemotherapy. Despite this, the specific involvement of NETs-related long non-coding RNAs (lncRNAs) in gastric cancer remains unclear. A prognostic model for NETs-related lncRNAs was constructed through correlation analysis, COX regression analysis, and least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the model was assessed using Kaplan–Meier survival curves, receiver operating characteristic (ROC) curves, facilitating the exploration of the relationship between disease onset and prognosis in gastric cancer. Additionally, differences in the tumor microenvironment and response to immunotherapy among gastric cancer patients across high- and low-risk groups were analyzed. Furthermore, a prognostic nomogram integrating the risk score with relevant clinicopathological parameters was developed. The prognostic prediction model for gastric cancer, derived from NETs-related lncRNAs in this study, demonstrates robust prognostic capabilities, serving as a valuable adjunct to traditional tumor staging. This model holds promise in offering novel guidelines for the precise treatment of gastric cancer, thereby potentially improving patient outcomes. [ABSTRACT FROM AUTHOR]