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Background: Metabolic reprogramming is one of the hallmarks of cancer, and in breast cancer (BC), several metabolic enzymes are overexpressed and overactivated. One of these, Enolase 1 (ENO1), catalyses glycolysis and is involved in the regulation of multiple signalling pathways. Objectives: This study aimed to evaluate in silico the prognostic and predictive effects of ENO1 expression in BC. Design: This is a bioinformatic in silico analysis. Methods: Using available online platforms (Kaplan–Meier [KM] plotter, receiver operating characteristic curve [ROC] plotter, cBioPortal, Genotype-2-Outcome [G-2-O], MethSurv, and Tumour–Immune System Interaction Database [TISIDB]), we performed a bioinformatic in silico analysis to establish the prognostic and predictive effects related to ENO1 expression in BC. A network analysis was performed using the Oncomine platform, and signalling, epigenetic, and immune regulation pathways were explored. Results: ENO1 was overexpressed in all the analysed Oncomine, epigenetic, and immune pathways in triple-negative, but not in hormone receptor–positive BCs. In human epidermal growth factor receptor 2 (HER2)-positive BCs, ENO1 expression showed a mixed profile. Analysis on disease progression and histological types showed ENO1 overexpression in ductal in situ and invasive carcinoma, in high-grade tumours followed by advanced or metastasis and was linked to worse survival. High ENO1 expression was also associated with relapse-free, distant metastasis-free and overall survival, irrespectively of treatment and was mainly related to basal subtype. Conclusion: ENO1 overexpression recruits a range of signalling pathways during disease progression conferring a worse prognosis and can be potentially used as a biomarker of disease progression and therapeutic target, particularly in triple-negative and in ductal invasive carcinoma. [ABSTRACT FROM AUTHOR]

Ovarian cancer is the second leading cause of gynecologic cancer death worldwide, with only 20% of cases detected early due to its elusive nature, limiting successful treatment. Most deaths occur from the disease progressing to advanced stages. Despite advances in chemo- and immunotherapy, the 5-year survival remains below 50% due to high recurrence and chemoresistance. Therefore, leveraging new research perspectives to understand molecular signatures and identify novel therapeutic targets is crucial for improving the clinical outcomes of ovarian cancer. Alternative splicing, a fundamental mechanism of post-transcriptional gene regulation, significantly contributes to heightened genomic complexity and protein diversity. Increased awareness has emerged about the multifaceted roles of alternative splicing in ovarian cancer, including cell proliferation, metastasis, apoptosis, immune evasion, and chemoresistance. We begin with an overview of altered splicing machinery, highlighting increased expression of spliceosome components and associated splicing factors like BUD31, SF3B4, and CTNNBL1, and their relationships to ovarian cancer. Next, we summarize the impact of specific variants of CD44, ECM1, and KAI1 on tumorigenesis and drug resistance through diverse mechanisms. Recent genomic and bioinformatics advances have enhanced our understanding. By incorporating data from The Cancer Genome Atlas RNA-seq, along with clinical information, a series of prognostic models have been developed, which provided deeper insights into how the splicing influences prognosis, overall survival, the immune microenvironment, and drug sensitivity and resistance in ovarian cancer patients. Notably, novel splicing events, such as PIGV|1299|AP and FLT3LG|50,941|AP, have been identified in multiple prognostic models and are associated with poorer and improved prognosis, respectively. These novel splicing variants warrant further functional characterization to unlock the underlying molecular mechanisms. Additionally, experimental evidence has underscored the potential therapeutic utility of targeting alternative splicing events, exemplified by the observation that knockdown of splicing factor BUD31 or antisense oligonucleotide-induced BCL2L12 exon skipping promotes apoptosis of ovarian cancer cells. In clinical settings, bevacizumab, a humanized monoclonal antibody that specifically targets the VEGF-A isoform, has demonstrated beneficial effects in the treatment of patients with advanced epithelial ovarian cancer. In conclusion, this review constitutes the first comprehensive and detailed exposition of the intricate interplay between alternative splicing and ovarian cancer, underscoring the significance of alternative splicing events as pivotal determinants in cancer biology and as promising avenues for future diagnostic and therapeutic intervention. [ABSTRACT FROM AUTHOR]

Malaria is a potentially fatal infective illness caused due to parasites that belong to the Plasmodium genus, which are transferred to humans with the help of the stings of affected female Anopheles mosquitoes, and it persists as a serious public wellness problem worldwide. Cordia myxa is a medicinal plant that possesses various medicinal characteristics like antimicrobial, anti-inflammation, antioxidant, and antidiabetic activities, which makes it an important natural resource for the therapy of different maladies in traditional medicine. In this investigation, a certain network pharmacology method has been utilized to identify the potent active components, possible targets as well as signaling pathways present in C. myxa in relation to malaria therapy. The active compounds were submitted to molecular docking approaches to validate their successful activity against the potential targets. The study concluded that three constituents named cosmosiin, stigmastanol, robinetin, and quercetin were highly active and could regulate the expression of Interleukin 6 (IL6) and Cysteine-aspartic acid protease 3 (CASP3), which may act as a potential therapeutic target for malaria treatment. These analyses are validated by molecular dynamics simulation which reflects on the overall structural stability of the intermolecular conformation and interactions. These results can also be witnessed in simulation-based trajectories binding free energies, which concluded the significant role of electrostatic and van der Waals energies in total intermolecular interactions. Finally, we utilized machine learning to predict the anti-malarial activity of C. myxa compounds, comparing them with approved drugs. Using the Chemprop model and MAIP predictions, we assessed ten compounds, revealing their potential as lead anti-malarial agents. This study establishes a groundwork for comprehending the function of the anti-malaria action of C. myxa. [ABSTRACT FROM AUTHOR]

Although Alzheimer's disease (AD) and intracranial aneurysm (IA) were two different types of diseases that occurred in the brain, ruptured IA (RIA) survivors may experience varying degrees of cognitive dysfunction. Neither AD nor IA is easily recognizable by an early onset so that the incidence of adverse clinical outcomes would be on the rise. Therefore, we focused on the exploration of the shared genes and molecular mechanisms between AD and IA, which would be significant for the efficiency of co-screening and co-diagnosis. Two GEO datasets were selected for the weighted gene co-expression network analysis (WGCNA) and differentially expressed gene screening, obtaining 78 overlapped genes. Next, 9 hub genes were identified by the protein-protein interaction network, including PIK3CA, GAB1, IGF1R, PLCB1, PGR, PDGFRB, PLCE1, FGFR3, and SYNJ1. The interactions among the hub genes, miRNA, and TFs were also explored. Meanwhile, we performed GO and KEGG pathway enrichment analyses for the results of WGCNA and hub genes, which showed that the Ras signaling and Rap1 signaling were the main shared pathogenesis. In conclusion, the present bioinformatics analysis revealed that AD and IA had the shared genes and molecular mechanisms, and these outcomes were associated with inflammation and calcium homeostasis, which could provide research clues for further studies. [ABSTRACT FROM AUTHOR]

In recent years, CD9 has been extensively studied as a potential biomarker for cancer. However, the biological role of CD9 in gliomas remains unclear. This study investigates the function of CD9 in gliomas and its molecular mechanisms. Utilizing pan-cancer analysis with TCGA, CGGA, and GEO databases, differential expression of CD9 was observed in 11 tumor types within the TCGA cohort, and it was associated with patient survival rates. Analysis of the CGGA glioma database revealed that patients with high CD9 expression had lower survival rates. The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability. Through gene set enrichment analysis (GSEA), immune-related analysis, and CD9 mutation detection, CD9 was found to have the strongest correlation with neutrophil involvement (cor = 0.30, P < 0.05), and the high CD9 expression group exhibited higher rejection responses and TIDE scores, suggesting a lower likelihood of successful immunotherapy. The high CD9 expression group was more sensitive to 81 drugs, indicating potential therapeutic effects for gliomas. Furthermore, overexpression of CD9 in gliomas may be associated with gene mutations. Down-regulation or up-regulation of CD9 expression in the glioblastoma cell line LN229 showed that CD9 could positively regulate the migratory ability of LN229 cells. Further, several marker genes, such as VEGFR-2, TGF-β1, CASP1 and PI3K, were down regulated in CD9 knockdown cell lines and up regulated in CD9 overexpression cell lines, compared with control cell line. This study preliminarily explores the role of CD9 in gliomas and its prognostic value, providing new insights for personalized treatment strategies in glioma therapy. [ABSTRACT FROM AUTHOR]

Heat shock protein 70 (HSP70) is one kind of molecular chaperones which are widely found in organisms, and its members are highly conserved among each other, with important roles in plant growth and development. In this study, 56 HSP70 genes were identified from the apple genome database. Analysis of gene duplication events showed that tandem and segmental duplication events play an important role in promoting the amplification of the MdHSP70 gene family. Collinearity analysis showed that HSP70 family members of apple were more closely related to HSP70 family members of Arabidopsis, tomato and soybean. The promoter region of the apple HSP70 genes contains a large number of cis-acting elements in response to hormones and stress. Tissue-specific expression analysis showed that some of the genes were associated with various stages of the apple growth process. Codon preference analysis showed small differences between codon bases 1 and 3 in the apple HSP70 genome, and the codon base composition had a small effect on codon usage preference. The multiple expression patterns of the MdHSP70 gene suggested that MdHSP70 gene members play important roles in growth and development and in response to hormonal and abiotic stresses. The yeast two-hybrid (Y2H) demonstrated that MdHSP70-53 interacts with MdDVH24_032563. The qRT-PCR analysis showed that most MdHSP70 members' hormonal and abiotic stresses (MdHSP70-6, MdHSP70-26 and MdHSP70-45) appeared to be highly expressed. To further elucidate the function of MdHSP70 (6, 26, 45), we introduced them into tobacco to confirm subcellular locations and noted that these genes are located in the cytoplasm and cell membrane. This study serves as a theoretical basis for further studies of the MdHSP70 gene and helps to further investigate the functional characterization of MdHSP70 gene. [ABSTRACT FROM AUTHOR]

Valvular Heart Disease (VHD) is a globally significant cause of mortality, particularly among aging populations. Despite advancements in percutaneous and surgical interventions, there are still uncertainties that remain regarding the risk factors that significantly contribute to this condition within the domain of cardiovascular disease. This study investigates these uncertainties and the role of machine learning in categorizing VHD based on cardiovascular risk factors. It follows a two-part investigation comprising feature extraction and classification phases. Feature extraction is initially performed using a wrapping approach and refined further with binary logistic regression. The second phase employs five classifiers: Artificial Neural Network (ANN), XGBoost, Random Forest (RF), Naïve Bayes, and Support Vector Machine (SVM), along with advanced methods such as SVM combined with Principal Component Analysis (PCA) and a majority-voting ensemble method (MV5). Data on VHD cases were collected from DHQ Hospital Faisalabad using simple random sampling. Various statistical measures, such as the ROC curve, F-measure, sensitivity, specificity, accuracy, MCC, and Kappa are applied to assess the results. The findings reveal that the combination of SVM with PCA achieves the highest overall performance while the MV5 ensemble method also demonstrates high accuracy and balance in sensitivity and specificity. The variation in VHD prevalence linked to specific risk factors highlights the importance of a comprehensive approach to reduce this disease's burden. The Exceptional performance of SVM + PCA and MV5 highlights their significance in diagnosing VHD and advancing knowledge in biomedicine. [ABSTRACT FROM AUTHOR]

Background: Human cancers, including head and neck squamous cell carcinoma (HNSCC), are complex and heterogeneous diseases driven by uncontrolled cell growth and proliferation. Post-translational modifications (PTMs) of proteins play a crucial role in cancer progression, making them a promising target for pharmacological intervention. This study aims to identify key exercise-related genes with prognostic value in HNSCC through comprehensive bioinformatics analysis, with a particular focus on the therapeutic potential of placental growth factor (PIGF). Methods: Transcriptome data for HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. Differently expressed genes (DEGs) were identified and analyzed for their prognostic significance. Exercise-related gene sets were retrieved from the Gene Set Enrichment Analysis (GSEA) database. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA, were conducted. The biological functions and clinical implications of key genes were further explored through single-gene expression analysis, immune infiltration analysis, and in vitro cellular experiments. Results: The study identified exercise-related genes associated with survival prognosis in HNSCC. GO and KEGG pathway analyses highlighted the biological functions of these genes, and Kaplan-Meier survival curves confirmed their prognostic value. PIGF expression analysis using TCGA data showed its diagnostic potential, with higher expression linked to advanced tumor stages. Single-cell sequencing revealed PIGF's role in the tumor microenvironment. In vitro experiments demonstrated that PIGF plays a pivotal role in enhancing cell proliferation and colony formation in HNSCC, with PIGF knockdown significantly impairing these functions, highlighting its importance in tumor growth regulation. Additionally, PIGF's predictive performance in drug sensitivity across cancer datasets suggests its potential as a pharmacological target, offering opportunities to modulate the immune microenvironment and improve therapeutic outcomes in cancer treatment. Conclusion: This study provides new insights into the molecular mechanisms underlying HNSCC and identifies exercise-related genes, particularly PIGF, as promising biomarkers for clinical treatment and personalized medicine. By focusing on PTMs and their role in cancer progression, our findings suggest that targeting PIGF may offer innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life science research, particularly microscopy. In this Review, we focus on the opportunities and challenges associated with applying ML-based pipelines for microscopy datasets from a user point of view. We investigate the significance of different data characteristics -- quantity, transferability and content -- and howthis determines whichMLmodel(s) to use, as well as their output(s). Within the context of cell biological questions and applications, we further discuss ML utility range, namely data curation, exploration, prediction and explanation, and what they entail and translate to in the context of microscopy. Finally, we explore the challenges, common artefacts and risks associated with ML in microscopy. Building on insights from other fields, we propose how these pitfalls might be mitigated for in microscopy. [ABSTRACT FROM AUTHOR]

Septic patients with T2DM were prone to prolonged recovery and unfavorable prognoses. Thus, this study aimed to pinpoint potential genes related to sepsis with T2DM and develop a predictive model for the disease. The candidate genes were screened using protein–protein interaction networks (PPI) and machine learning algorithms. The nomogram and receiver operating characteristic curve were developed to assess the diagnostic efficiency of the biomarkers. The relationship between sepsis and immune cells was analyzed using the CIBERSORT algorithm. The biomarkers were validated by qPCR and western blotting in basic experiments, and differences in organ damage in mice were studied. Three genes (MMP8, CD177, and S100A12) were identified using PPI and machine learning algorithms, demonstrating strong predictive capabilities. These biomarkers presented significant differences in gene expression patterns between diseased and healthy conditions. Additionally, the expression levels of biomarkers in mouse models and blood samples were consistent with the findings of the bioinformatics analysis. The study elucidated the common molecular mechanisms associated with the pathogenesis of T2DM and sepsis and developed a gene signature‐based prediction model for sepsis. These findings provide new targets for the diagnosis and intervention of sepsis complicated with T2DM. [ABSTRACT FROM AUTHOR]

Simple Summary: Intracranial Epidermoid Cysts (IECs) are rare tumors. Despite their benign course, IECs can adhere to critical brain structures resulting in impairment and morbidity. The primary treatment is surgery; however, cyst adherence often complicates complete removal, resulting in notably high progression rates after subtotal resection. Due to the rarity of IECs, there has been limited research focused on understanding the mechanisms of the disease and advancing therapeutic options. As a result, there are currently no effective drug therapies available for treating patients with IECs. Targeted therapy is an effective form of treatment for tumors. It focuses on mutations that turn healthy cells into tumor cells. The mutation profile of IECs has not been investigated. We aimed to investigate the somatic landscape of IECs to gain insights into tumor biology and identify mutations that could potentially serve as targets for additional therapies. Surgery is still the most effective treatment for these patients. Background/Objectives: Intracranial Epidermoid Cysts (IECs) are rare intracranial tumors primarily treated through surgery. Cyst adherence complicates complete removal, leading to high rates of tumor progression after subtotal resection. The molecular drivers of IEC remain unknown. Consequently, advances in treatment have fallen short. Tumor genetic profiling has revealed potential targets for drug development, including FDA-approved options and reshaping treatment. The genetic landscape of IECs has not been explored. We applied Whole Exome Sequencing (WES) to IECs to gain insights into the mechanisms of oncogenesis and identify potential therapeutic targets. Methods: We performed WES on tumor tissue and matched blood samples, when available. Following GATK best practices, we conducted read processing, quality control, somatic variant calling, and copy-number inference. Data analyses and visualization were conducted in R. Results: Top altered genes are associated with the immune system and tumor microenvironment, suggesting a mechanism of immune evasion. Gene and pathway enrichment revealed a high mutation burden in genes associated with Extracellular Matrix (ECM) and PI3K-AKT-mTOR cascades. Recurrent and deleterious alterations in NOTCH2 and USP8 were identified in 50% and 30% of the cohort, respectively. Frequent amplifications in deubiquitinases and beta-defensins strengthened the involvement of immune mechanisms for oncogenic transformation. Conclusions: Top altered genes and recurrent mutations may play a role in shaping the microenvironment and modulating immune evasion in IECs. USP8 and NOTCH2 may serve as clinically relevant target for IECs. Finally, we present evidence that the crosstalk between the PI3K-Akt-mTOR and ECM signaling pathways may play a role in modulating the immune escape mechanism in IECs. [ABSTRACT FROM AUTHOR]

A high-altitude, low-pressure hypoxic environment has severe effects on the health and work efficiency of its residents, and inadequate preventive measures and adaptive training may lead to the occurrence of AMS. Acute exposure to hypoxia conditions can have a less-favorable physiological effect on the human immune system. However, the regulation of the immune system in high-altitude environments is extremely complex and remains elusive. This study integrated system bioinformatics methods to screen for changes in immune cell subtypes and their associated targets. It also sought potential therapeutically effective natural compound candidates. The present study observed that monocytes, M1 macrophages and NK cells play a crucial role in the inflammatory response in AMS. IL15RA, CD5, TNFSF13B, IL21R, JAK2 and CXCR3 were identified as hub genes, and JAK2 was positively correlated with monocytes; TNFSF13B was positively correlated with NK cells. The natural compound monomers of jasminoidin and isoliquiritigenin exhibited good binding affinity with JAK2, while dicumarol and artemotil exhibited good binding affinity with TNFSF13B, and all are expected to become a potential therapeutic agents. [ABSTRACT FROM AUTHOR]

Despite recent advances in chronic obstructive pulmonary disease (COPD) research, few studies have identified the potential therapeutic targets systematically by integrating multiple-omics datasets. This project aimed to develop a systems biology pipeline to identify biologically relevant genes and potential therapeutic targets that could be exploited to discover novel COPD treatments via drug repurposing or de novo drug discovery. A computational method was implemented by integrating multi-omics COPD data from unpaired human samples of more than half a million subjects. The outcomes from genome, transcriptome, proteome, and metabolome COPD studies were included, followed by an in silico interactome and drug-target information analysis. The potential candidate genes were ranked by a distance-based network computational model. Ninety-two genes were identified as COPD signature genes based on their overall proximity to signature genes on all omics levels. They are genes encoding proteins involved in extracellular matrix structural constituent, collagen binding, protease binding, actin-binding proteins, and other functions. Among them, 70 signature genes were determined to be druggable targets. The in silico validation identified that the knockout or over-expression of SPP1, APOA1, CTSD, TIMP1, RXFP1, and SMAD3 genes may drive the cell transcriptomics to a status similar to or contrasting with COPD. While some genes identified in our pipeline have been previously associated with COPD pathology, others represent possible new targets for COPD therapy development. In conclusion, we have identified promising therapeutic targets for COPD. This hypothesis-generating pipeline was supported by unbiased information from available omics datasets and took into consideration disease relevance and development feasibility. [ABSTRACT FROM AUTHOR]

Objective To screen and validate the aging-related diagnostic markers of synovial tissue in osteoarthritis (OA) by bioinformatics methods. Methods GSE206848, GSE55235 and GSE554573 datasets of bone and joint synovial tissue were obtained from Gene Expression Omnibus (GEO) database and GSE206848 differentially expressed genes (DEGs) were screened. Senescence-associated genes (SRGs) were downloaded from the CellAge database. Differentially expressed senescence-related genes (DESRGs) were obtained by intersection of DEGs and SRGs. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed, and protein-protein interaction (PPI) was analyzed by using STRING online database and Cyto- scape software. The predicted area under curve (AUC) value and gene expression level were verified. The key genes were verified by GSE55235 and GSE55457 datasets, and the immune infiltration analysis was performed by CIBERSORT. Results Finally, 30 DESRGs were obtained. GOs were enriched in the regulation of transcription regulation, nucleoplasm and protein binding of RNA polymerase II, while KEGGs were mainly enriched in the signaling pathways such as "cellular senescence, endocrine resistance, and breast cancer". SNAIL, NOX4 and DHX9 were identified as synovial cell senescence-related diagnostic genes. OA synovial tissue contained fewer resting memory CD4 T cells (P=0.007), and there was a correlation between immune cells (P<0.05). Diagnostic marker NOX4 was correlated with a variety of immune cells (P<0.05). Conclusion Bioinformatics analysis is used to screen out the diagnostic markers related to synovial aging in OA, which is helpful to understand the pathogenesis of OA and provide research directions for targeted therapy in the future. [ABSTRACT FROM AUTHOR]

Yiwei decoction (YWD) is a classic prescription with the function of nourishing stomach yin. In this study, the effective components of antioxidant activity of YWD and its possible mechanism were discussed from the point of view of spectral effect relationship and network pharmacology. Firstly, the fingerprints of 10 batches of YWD were established by UPLC‐PDA technique, and the 1,1‐diphenyl‐2‐picryl‐hydrazyl radical (DPPH) scavenging rate and total antioxidant capacity (T‐AOC) were used as the indicators for antioxidant activity in vitro. Then, the spectral effect relationship between the fingerprint profiles and antioxidant capacity was analyzed through grey relational analysis (GRA) and orthogonal projections to latent structures (OPLS). In addition, network pharmacology was employed to predict the potential mechanisms of YWD in the treatment of antioxidant‐related diseases. The spectrum‐effect relationship indicated that three common peaks were likely to be the most decisive active components, identified as verbascoside, psoralen, and vitexin, respectively. Based on network pharmacology analysis, a total of 83 target genes shared by the active components and antioxidant‐related diseases were collected. AKT1, HSP90AA1, SRC, CASP3, and MTOR were closely related to antioxidant therapy and considered as core therapeutic targets. The potential mechanisms of YWD were obtained through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, molecular docking simulations were conducted to evaluate the binding activities between the core therapeutic targets and corresponding compounds. The excellent core protein‐compound complexes obtained by molecular docking were simulated by molecular dynamics simulation. The results showed that the active compounds had good binding ability with the selected targets. This study successfully identified the effective components of YWD and predicted the potential targets and pathways, which provided a new idea for the application of YWD in the treatment of antioxidant stress in the future. In addition, the potential active components provide valuable implications for drug screening of related diseases. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI. Methods: The aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity. Results: A total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI. Conclusions: Analysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI. [ABSTRACT FROM AUTHOR]

Background: Allergic diseases are systemic chronic inflammatory diseases associated with multiorgan damage and complex pathogenesis. Several studies have revealed the association of gene expression abnormalities with the development of allergic diseases, but the biomedical field still lacks a public platform for comprehensive analysis and visualization of transcriptomic data of allergic diseases. Objective: The aim of the study is to provide a comprehensive web tool for multiple analysis in allergic diseases. Methods: We retrieved and downloaded human and mouse gene expression profile data associated with allergic diseases from the Gene Expression Omnibus (GEO) database and standardized the data uniformly. We used gene sets obtained from the MSigDB database for pathway enrichment analysis and multiple immune infiltration algorithms for the estimation of immune cell proportion. The basic construction of the web pages was based on the Shiny framework. Additionally, more convenient features were added to the server to improve the efficiency of the web pages, such as jQuery plugins and a comment box to collect user feedback. Results: We developed CTPAD, an interactive R Shiny application that integrates public databases and multiple algorithms to explore allergic disease-related datasets and implement rich transcriptomic visualization capabilities, including gene expression analysis, pathway enrichment analysis, immune infiltration analysis, correlation analysis, and single-cell RNA sequencing analysis. All functional modules offer customization options and can be downloaded in PDF format with high-resolution images. Conclusions: CTPAD largely facilitates the work of researchers without bioinformatics background to enable them to better explore the transcriptomic features associated with allergic diseases. CTPAD is available at https://smuonco.shinyapps.io/CTPAD/. [ABSTRACT FROM AUTHOR]

ABSTRACT Bioactive peptides (BAPs) have attracted considerable interest in scientific research due to their heterogeneity in sequence and structure, which underpins various biological functionalities. In this context, goat casein, an abundant by‐product of the dairy industry, emerges as a valuable source of BAPs. The present study undertook a meticulous evaluation of the bioactive potentials of goat casein‐derived peptides through an integrated approach combining computational simulations, high‐throughput screening, and molecular docking techniques. The initial phase involved the enzymatic digestion of goat milk casein using trypsin, followed by the identification of peptides via liquid chromatography–tandem mass spectrometry (LC–MS/MS), uncovering a total of 597 peptides. Subsequent prioritization using the PeptideRanker algorithm identified 70 peptides exhibiting potential bioactivity, as denoted by scores above 0.8. Advanced screening employing the BIOPEP database and the AutoDock and CAMPR4 tools facilitated the elucidation of 16 antioxidant, 59 hypotensive, 63 hypoglycemic, 70 hypolipidemic, and 25 antimicrobial peptides. Molecular docking studies further elucidated the spontaneous nature of the interactions between the peptides and their respective receptors, predominantly mediated by hydrogen bonding and hydrophobic interactions. Four peptides specifying all activities simultaneously were synthesized, and their activities were verified by in vitro experiments. These results not only highlight an effective strategy for the high‐throughput screening of goat casein‐derived peptides but also underscore the potential of utilizing casein as a viable source of functional food ingredients. This study thereby contributes significantly to the expanding field of functional food research, suggesting a sustainable approach to explore the potential of dairy by‐products. [ABSTRACT FROM AUTHOR]

To explore the potential mechanisms which O‐linked‐N‐acetylglucosaminylation (O‐GlcNAcylation) regulates osteogenesis, a publicly RNA‐seq dataset was re‐analyzed with literature‐mining and showed the primary targets of O‐GlcNAcylation in osteoblasts are mitochondria/cytoskeleton. Although the O‐GlcNAcylation‐regulated mitochondria/cytoskeleton has been extensively studied, its specific role during osteogenesis remains unclear. To address this, we knocked out Ogt (Ogt‐KO) in MC3T3‐E1 osteoblastic cells. Then, significantly reduced osteoblast differentiation, motility, proliferation, mitochondria–endoplasmic reticulum (Mito–ER) coupling, volume of ER, nuclear tubulins, and oxygen metabolism were observed in Ogt‐KO cells. Through artificial intelligence (AI)‐predicted cellular structures, the time‐lapse live cells imaging with reactive‐oxygen‐species/hypoxia staining showed that lower cell proliferation and altered oxygen metabolism in the Ogt‐KO cells were correlated with the Mito–ER coupling. Bioinformatics analysis, combined with correlated mRNA and protein expression, suggested that Ezh2 and its downstream targets (Opa1, Gsk3a, Wnt3a, Hif1a, and Hspa9) may be involved in O‐GlcNAcylation‐regulated Mito–ER coupling, ultimately impacting osteoblast differentiation. In conclusion, our findings indicate that O‐GlcNAcylation‐regulated osteoblast differentiation is linked to morphological changes in mitochondria, cytoskeleton, and ER, with Ezh2 potentially playing a crucial role. [ABSTRACT FROM AUTHOR]

Here we review the ‘what and how’ of molecular techniques used in the context of haematopathological diagnostics of both lymphoid and myeloid neoplasms. Keeping in mind that the required resources for molecular testing are not universally available, we will not only discuss novel and emerging techniques that allow more high‐throughput and sophisticated analyses of lymphoid and myeloid neoplasms, but also the more classical, low‐cost alternatives and even some workarounds for molecular testing approaches. In this review we also address other key aspects around molecular techniques for haematopatholgy diagnostics, including preanalytics, data interpretation, and data management, bioinformatics, and interlaboratory precision and performance evaluation. [ABSTRACT FROM AUTHOR]

Background: Endometriosis is a common cause of endometrial-type mucosa outside the uterine cavity with symptoms such as painful periods, chronic pelvic pain, pain with intercourse and infertility. However, the early diagnosis of endometriosis is still restricted. The purpose of this investigation is to identify and validate the key biomarkers of endometriosis. Methods: Next-generation sequencing dataset GSE243039 was obtained from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) between endometriosis and normal control samples were identified. After screening of DEGs, gene ontology (GO) and REACTOME pathway enrichment analyses were performed. Furthermore, a protein–protein interaction (PPI) network was constructed and modules were analyzed using the Human Integrated Protein–Protein Interaction rEference database and Cytoscape software, and hub genes were identified. Subsequently, a network between miRNAs and hub genes, and network between TFs and hub genes were constructed using the miRNet and NetworkAnalyst tool, and possible key miRNAs and TFs were predicted. Finally, receiver operating characteristic curve analysis was used to validate the hub genes. Results: A total of 958 DEGs, including 479 upregulated genes and 479 downregulated genes, were screened between endometriosis and normal control samples. GO and REACTOME pathway enrichment analyses of the 958 DEGs showed that they were mainly involved in multicellular organismal process, developmental process, signaling by GPCR and muscle contraction. Further analysis of the PPI network and modules identified 10 hub genes, including vcam1, snca, prkcb, adrb2, foxq1, mdfi, actbl2, prkd1, dapk1 and actc1. Possible target miRNAs, including hsa-mir-3143 and hsa-mir-2110, and target TFs, including tcf3 (transcription factor 3) and clock (clock circadian regulator), were predicted by constructing a miRNA-hub gene regulatory network and TF-hub gene regulatory network. Conclusions: This investigation used bioinformatics techniques to explore the potential and novel biomarkers. These biomarkers might provide new ideas and methods for the early diagnosis, treatment and monitoring of endometriosis. [ABSTRACT FROM AUTHOR]

Our previous research revealed a close association between the acetylation of peroxisome proliferator-activated receptor γ (PPARγ) histone H3K27 and the adipogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). We preliminarily explored the epigenetic mechanism of steroid-induced avascular necrosis of the femoral head (SANFH) development, but the specific histone deacetylase (HDAC) involved in this regulatory process remains unknown. In this study, we combined cell, animal, and clinical specimen experiments to screen for specific HDAC genes that could regulate BMSC adipogenic differentiation and to explore their roles. The results showed that dexamethasone (DEX) significantly exacerbated the imbalance between the adipogenic and osteogenic differentiation of BMSCs, and there were differences in HDAC expression in the adipogenic differentiation cell models, with histone deacetylase 10 (HDAC10) showing the most significant decrease in expression. Subsequent use of a chromatin immunoprecipitation assay kit and quantitative polymerase chain reaction (ChIP‒qPCR) revealed a decrease in HDAC10 expression at predicted potential sites within the PPARγ promoter, indicating a significant decrease in HDAC10 enrichment in the PPARγ promoter region of BMSCs, thereby promoting sustained PPARγ expression. Additionally, immunohistochemistry of samples collected from mice and humans with SANFH and normal femoral heads revealed an imbalance between adipogenic and osteogenic differentiation in the necrotic area of femoral heads, with a significant decrease in the relative expression of HDAC10 in the necrotic area of femoral heads with SANFH. In summary, we speculate that HDAC10 affects the progression of SANFH by regulating BMSC adipogenic differentiation, a process possibly related to PPARγ histone acetylation. These findings provide a promising direction for the treatment of SANFH. [ABSTRACT FROM AUTHOR]

Cancer is a global health problem despite the most developed therapeutic modalities. The delivery of specific therapeutic agents to a target increases the effectiveness of cancer treatment by reducing side effects and post‐treatment issues.Our aim in this study was to design a recombinant protein consisting of nanobody molecules and exotoxin that targets the surface GRP78 receptor on tumor cells. Bioinformatics methods make drug design and recombinant protein evaluation much easier before the laboratory steps.Two constructs were designed from a single‐variable domain on heavy chain nanobody domains and PE toxin domains II, Ib, and III. The physicochemical properties, secondary structure, and solubility of the chimeric protein were analyzed using different software. Prostate cancer DU‐145 and breast cancer MDA‐MB‐468 cell lines were used as GRP78‐positive and negative controls, respectively. Accordingly, the cytotoxicity, binding affinity, cell internalization, and apoptosis were evaluated using MTT, enzyme‐linked immunosorbent assay, and western blot. The results showed that in the DU‐145 cell line, the cytotoxicity of two recombinant immunotoxins is dose and time‐dependent. In MDA‐MB‐468 and HEK‐293 cells, such an event does not occur. It is possible that two constructs designed for immunotoxins can attach to GRP78‐positive cancer cells and then eradicate cancer cells by internalization and apoptosis. As our in vitro results were in line with in silico data confirming the Bioinformatics predictions, it can be concluded that the designed recombinant immunotoxins may exhibit therapeutic potential against GRP78‐positive tumor cells. [ABSTRACT FROM AUTHOR]

ABSTRACT The exponential growth of next‐generation sequencing (NGS) data requires innovative bioinformatics approaches to unravel the genetic underpinnings of diseases. Hemiplegic migraine (HM), a debilitating neurological disorder with a genetic basis, is one such condition that warrants further investigation. Notably, the genetic heterogeneity of HM is underscored by the fact that approximately two‐thirds of patients lack pathogenic variants in the known causal ion channel genes. In this context, we have developed PathVar, a novel bioinformatics algorithm that harnesses publicly available tools and software for pathogenic variant discovery in NGS data. PathVar integrates a suite of tools, including HaplotypeCaller from the Genome Analysis Toolkit (GATK) for variant calling, Variant Effect Predictor (VEP) and ANNOVAR for variant annotation, and TAPES for assigning the American College of Medical Genetics and Genomics (ACMG) pathogenicity labels. Applying PathVar to whole exome sequencing data from 184 HM patients, we detected 648 variants that are probably pathogenic in multiple patients. Moreover, we have identified several candidate genes for HM, many of which cluster around the Rho GTPases pathway. Future research can leverage PathVar to generate high quality, candidate pathogenic variants, which may enhance our understanding of HM and other complex diseases. [ABSTRACT FROM AUTHOR]

Introduction: FAS has been implicated in the development of various cancers, but its involvement in lung cancer has not been systematically characterized. In this study, we performed data mining in online tumor databases to investigate the expression, methylation, alterations, protein interactions, co-expression and prognostic significance of FAS in lung cancer. Method: The expression, prognostic significance and molecular interactions of FAS in lung cancer was mined and analyzed using GENT2, GEPIA2, UALCAN, cBioPortal, STRING, GeneMANIA, UCSC Xena, Enrichr, and OSluca databases. FAS expression was subsequently investigated at the protein level in samples from 578 lung cancer patients to understand its protein-level expression. In vitro validation of FAS gene expression was performed on H1299, H1993, A549 and HBE cell lines. Result: We found that the expression of FAS was significantly downregulated in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) compared to normal lung tissue. In addition, we observed a higher level of FAS promoter methylation in LUSC tissue than in normal tissue. FAS alterations were rare (1.9%) in lung cancer samples, with deep deletions being more common than missense mutations, which occurred mainly in the TNFR-like cysteine-rich domain and the death domain. We also identified a list of proteins interacting with FAS and genes co-expressed with FAS, with LUAD having 11 co-expressed genes and LUSC having 90 co-expressed genes. Our results also showed that FAS expression has limited prognostic significance (HR=1.302, 95% CI=0.935-1.139, P=0.530). Protein level investigation revealed that FAS expression varied among individuals, with nTPM values ranging from 5.2 to 67.2. Conclusion: This study provides valuable insights into the involvements and characteristics of FAS in lung cancer. Further studies are needed to investigate the clinical significance of FAS alterations in lung cancer and to explore the potential of targeting FAS for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development. [ABSTRACT FROM AUTHOR]

Sacha Inchi (Plukenetia volubilis) oil press-cake (SIPC) represents a new source of proteins of high biological value, with promissory food applications. However, knowledge of these proteins remains limited. In this study, a Sacha Inchi protein concentrate (SPC) was extracted from the SIPC, and proteomic analysis was performed to identify the major alkaline-soluble proteins. The electrophoretic profile highlighted the efficacy of alkaline pH and moderate temperature to extract the major proteins, from which a group of proteins, not previously reported, were registered. LC-MS/MS analyses produced abundant high-quality fragmentation spectra. Utilizing the Euphorbiaceae database (DB), 226 proteins were identified, with numerous well-assigned spectra remaining unidentified. PEAKS Studio v11.5 software generated 1819 high-quality de novo peptides. Data are available via ProteomeXchange with identifier PXD052665. Gene ontology (GO) classification allowed the identification of sequenced proteins associated with biological processes, molecular functions, and cellular components in the seed. Consequently, the principal alkali-soluble proteins from SPC were characterized through derived functional analysis, covering 24 seed-storage-, 27 defense-, and 12 carbohydrate- and lipid-metabolism-related proteins, crucial for human nutrition due to their sulfur-containing amino acids, antioxidant properties, and oil yields, respectively. This research makes a significant contribution to the current understanding of the Sacha Inchi proteome and offers valuable insights for its potential applications in the food industry. [ABSTRACT FROM AUTHOR]

Objective: To explore the role of cuproptosis in Alzheimer's disease (AD). Methods: An AD-related microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database (GSE140830). Weighted gene co-expression network analysis was used to identify AD-related modular genes. The Venn analysis was performed to obtain module genes associated with apoptosis and cuproptosis. Besides, we conducted an enrichment analysis of overlapped genes and constructed the protein–protein interaction (PPI) network, followed by screening hub genes and those significantly associated with AD were used to construct models of apoptosis and cuproptosis, respectively. Further, receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and subgroup analysis were used to compare the AD prediction performance of two models. Finally, the accuracy and reliability of AD prediction models were verified by GSE26927. Results: We obtained 42 module genes related to apoptosis and 9 module genes related to cuproptosis. The enrichment analysis results revealed MAPK signaling pathway as the common signaling pathway of apoptosis- and cuproptosis-related genes. Next, the hub genes associated with apoptosis (TRADD, FADD, BIRC2, and CASP2) and cuproptosis (MAP2K1, SLC31A1, and PDHB) in AD were identified, which were used to construct apoptosis and cuproptosis models to distinguish AD patients from the control group (P < 0.05). The ROC, DCA, and subgroup analysis results showed that apoptosis-related models and cuproptosis-related models had comparable ability in predicting AD. GSE26927 further confirmed that the two models have comparable predictive effects for AD. Conclusions: The cuproptosis model had a certain performance in predicting AD. Three hub genes (MAP2K1, SLC31A1, and PDHB) closely related to cuproptosis in AD might serve as biomarkers for AD diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Bone metastasis from prostate cancer severely impacts patient outcomes and quality of life. Anoikis, a form of programmed cell death triggered by the loss of cell-matrix interactions, plays a critical role in cancer progression. However, its precise relationship with prostate cancer-induced bone metastasis remains unclear. This study aims to elucidate this relationship, focusing on anoikis-related gene signatures, molecular pathways, and therapeutic implications. Methods: We used the TCGA-PRAD dataset for training, with MSKCC and GSE70769 as validation cohorts. To evaluate immunotherapy efficacy, we examined IMvigor 210 and GSE91016 datasets, and GSE137829 provided single-cell insights into prostate cancer. Specific anoikis-related genes (ARGs) were identified, and Random Survival Forest analysis and multivariate Cox regression were employed to develop anoikis-linked features. The ‘clustanoikisProfilanoikis’ and ‘GSEA’ packages were used to explore potential ARG-related pathways. Results: Analyzing 553 samples from TCGA, 231 from MSKCC, 94 from GSE70769, and single-cell data from 6 prostate cancer patients (GSE137829), we constructed a prognostic model based on 9 ARGs. GSVA revealed upregulation of carcinogenic pathways, including epithelial-mesenchymal transition, E2F targets, and angiogenesis, with downregulation of metabolic pathways. Significant differences in somatic mutations were observed between cohorts, with a positive correlation between anoikis scores and tumor mutational burden (TMB). Immune landscape analysis suggested high-risk patients might benefit more from chemotherapy than immunotherapy based on their risk score. Single-cell analysis indicated overactivation of carcinogenic pathways in the high anoikis score group. Conclusion: This study elucidates the complex interplay between anoikis and bone metastasis in prostate cancer. Our findings highlight the critical role of anoikis in metastatic progression, enhancing the understanding of key biomarkers and molecular dynamics. The identified anoikis-related gene signatures and disrupted pathways offer promising avenues for predictive and therapeutic strategies in prostate cancer management. [ABSTRACT FROM AUTHOR]

The advent of LLMs has reopened a debate about the limits of machine intelligence — and requires new benchmarks of what reasoning consists of. [ABSTRACT FROM AUTHOR]

Background: The ability to predict and comprehend molecular interactions offers significant insights into the biological functions of proteins. The interaction between surface protein 47 of Plasmodium falciparum (Pfs47) and receptor of the protein 47 (P47Rec) has attracted increased attention due to their role in parasite evasion of the mosquito immune system and the concept of geographical coevolution between species. The aims of this study were as follows: to apply a bioinformatics approach to investigate the interaction between Pfs47 and P47Rec proteins and to identify the potential binding sites, protein orientations and receptor specificity sites concerning the geographical origins of the vectors and the parasite. Methods: Public sequences of the pfs47 and p47rec genes were downloaded and subsequently filtered to predict functional and structural annotations of the Pfs47-P47Rec complex. Phylogenetic analyses of both proteins were carried out. In addition, the p47Rec gene was subjected to sequencing and subsequent analysis in 2 distinct Anopheles species collected in Honduras. Results: The examination of motifs reveals a significant degree of conservation in pfs47, suggesting that Pfs47 might have undergone recent evolutionary development and adaptation. Structural models and docking analyses supported the theory of selectivity of Plasmodium falciparum strains towards their vectors in diverse geographical regions. A detailed description of the putative interaction between the Pfs47-P47Rec complex is shown. Conclusions: The study identifies coevolutionary patterns between P47Rec and Pfs47 related to the speciation and geographic dispersion of Anopheles species and Plasmodium falciparum, with Pfs47 evolving more recently than P47Rec. This suggests a link between the parasite's adaptability and existing anopheline species across different regions. P47Rec likely has a cytoplasmic localization due to its lack of membrane attachment elements. However, these findings are based on simulations and require validation through methods like cryo-electron microscopy. A significant limitation is the scarcity of sequences in global databases, which restricts precise interaction modelling. Further research with diverse parasite isolates and anopheline species is recommended to enhance understanding of these proteins' structure and interaction. [ABSTRACT FROM AUTHOR]

Background: Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis due to the absence of effective targeted therapies. Emerging evidence indicates that the gut microbiota and its metabolites play a key role in the occurrence and development of TNBC. This study aimed to explore the metabolic changes and potential mechanisms associated with TNBC. Objectives: This study aimed to explore the potential relationship between targeted metabolites and the gut microbiota in TNBC. Design: We recruited 8 participants, including 4 with TNBC and 4 with benign fibroadenomas as controls. Methods: The gut microbiota was analyzed using metagenomics on fecal samples. Liquid chromatography–mass spectrometry (LC-MS) was employed to identify differential metabolites in serum and fecal samples. The correlation between the gut microbiota and metabolites was analyzed using Spearman's correlation analysis. Results: Analysis of altered serum metabolites in the TNBC group revealed changes, particularly in carboxylic acids and derivatives, benzene, and substituted derivatives. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis revealed significant enrichment in 18 pathways. Regarding fecal metabolites, differences between the 2 groups also included carboxylic acids and derivatives, benzene, and substituted derivatives, with 28 metabolic pathways enriched based on KEGG pathway analysis. Metagenomics analysis showed differences in the relative abundance of Anaerococcus, Fischerella, and Schizosaccharomyces at the genus level, which have been previously associated with breast cancer. Furthermore, 4 serum metabolites—L-glutamine, citrate, creatinine, and creatine—along with 9 fecal metabolites, were associated with the aforementioned microbiota. Conclusion: Our findings highlight distinct metabolite profiles in the serum and feces of patients with TNBC. The identification of gut microbiota and their associated metabolites provides new insights into the pathophysiological mechanisms underlying TNBC. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD. [ABSTRACT FROM AUTHOR]

Background: Evidence has been presented that the tumor protein D52 (TPD52) family plays a critical role in tumor development and progression. As a member of the TPD52 family, the changes in TPD52L2 gene status are instrumental in kinds of cancer development. However, its effects on patient prognosis and immune infiltration in Head and Neck Squamous Carcinoma (HNSCC) are still poorly understood. Methods: The Tumor Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and c-BioPortal database was used to explore the expression pattern, prognostic value, and variation of gene status in HNSCC. The LinkedOmics database was used to obtain the co-expression genes of TPD52L2 and identify the diagnostic value of TPD52L2 in HNSCC. The correlations between TPD52L2 expression and six main types of immune cell infiltrations and immune signatures were explored using Tumor Immune Estimation Resource (TIMER). The correlation between TPD52L2 expression and immune checkpoint genes (ICGs) was analyzed by TCGA database. Immunohistochemistry (IHC) was performed to validate the expression of three ICGs (PDL1, PDL2, EGFR) and TPD52L2 using 5 paired HNSCC and normal head and neck tissues. Polymerase Chain Reaction (PCR) and Western Blot (WB) of HNSCC and normal head and neck cell lines were performed to verify the high level of TPD52L2 mRNA and protein expression. protein expression of TPD52L2 in pan-cancer was also validated using UALCAN. Results: TPD52L2 was overexpressed in tumor tissues, and it predicted worse survival status in HNSCC. ROC analysis suggested that TPD52L2 had a diagnostic value. Multivariate Cox analysis identified TPD52L2 as an independent negative prognostic marker of overall survival. Functional network analysis suggested that TPD52L2 was associated with immune-related signaling pathways, cell migration pathways, and cancer-related pathways. High expression of TPD52L2 was associated with a more mutant frequency of TP53. Notably, we found that the expression of TPD52L2 was closely negatively correlated with the infiltration levels of 15 types of immune cells and positively correlated with several immune markers. PCR, WB experiments, and UALCAN database verified the high level of TPD52L2 mRNA and protein expression. Conclusion: TPD52L2 is upregulated in HNSCC, which is an independent factor for adverse prognosis prediction. It probably plays a role in the negative regulation of immune cell infiltration. TPD52L2 might be a promising prognostic biomarker and therapeutic target in HNSCC. [ABSTRACT FROM AUTHOR]

AIM: To investigate the impact and regulatory mechanisms of zinc finger protein 36-like protein 1 （ZFP36L1） on pancreatic carcinoma cell growth. METHODS: The ZFP36L1 expression in pancreatic carcinoma and its correlation with patient prognosis were analyzed using online databases UALCAN and GEPIA. Western blot was utilized to detect ZFP36L1 protein expression in pancreatic ductal cells （HPNE） and three different pancreatic carcinoma cell lines. CCK-8 and cell colony formation assays were performed to evaluate the effects of ZFP36L1 on pancreatic cancer cell proliferation. Wound healing and Transwell assays were used to assess the impact of ZFP36L1 expression changes on pancreatic carcinoma cell migration and invasion. Flow cytometry experiments were used to analyze the effect of ZFP36L1 on the pancreatic carcinoma cell cycle process. Bioinformatics analysis was conducted to predict potential ZFP36L1 interacting proteins. Co-immunoprecipitation experiments were carried out to confirm the interaction between ZFP36L1 and mitogen-activated protein kinase 14 （MAPK14）. Rescue experiments were performed to assess the function of MAPK14 in ZFP36L1-regulated pancreatic carcinoma cell growth. RESULTS: （ 1） ZFP36L1 is highly expressed in pancreatic carcinoma and is positively correlated with poor prognosis in pancreatic carcinoma patients. Compared to HPNE, ZFP36L1 is highly expressed in MIA PaCa-2 and ASPC-1 cells, but relatively low in PANC-1 cells. （2） ZFP36L1 overexpression significantly increased the cell viability, colony formation, migration, and invasion abilities of PANC-1 and MIA PaCa-2 cells, while siRNA interference of ZFP36L1 led to opposite results.（ 3） ZFP36L1 promotes the entry of pancreatic carcinoma cells into the S phase of the cell cycle. （4） ZFP36L1 interacts with MAPK14 to regulate pancreatic cancer cell growth. MAPK14 overexpression reversed the cell viability and migration abilities of pancreatic carcinoma cells overexpressing ZFP36L1. Furthermore, it also decreased the cell viability and migration abilities of pancreatic carcinoma cells with ZFP36L1 interference. CONCLUSION: ZFP36L1 is a potential oncogene in pancreatic carcinoma growth and may regulate pancreatic carcinoma cell growth through cell cycle modulation and interaction with MAPK14. [ABSTRACT FROM AUTHOR]

Purpose: The primary aim of this study was to develop an open-source Python-based software for the automated analysis of dynamic cell behaviors in microphysiological models using non-confocal microscopy. This research seeks to address the existing gap in accessible tools for high-throughput analysis of endothelial tube formation and cell invasion in vitro, facilitating the rapid assessment of drug sensitivity. Methods: Our approach involved annotating over 1000 2 mm Z-stacks of cancer and endothelial cell co-culture model and training machine learning models to automatically calculate cell coverage, cancer invasion depth, and microvessel dynamics. Specifically, cell coverage area was computed using focus stacking and Gaussian mixture models to generate thresholded Z-projections. Cancer invasion depth was determined using a ResNet-50 binary classification model, identifying which Z-planes contained invaded cells and measuring the total invasion depth. Lastly, microvessel dynamics were assessed through a U-Net Xception-style segmentation model for vessel prediction, the DisPerSE algorithm to extract an embedded graph, then graph analysis to quantify microvessel length and connectivity. To further validate our software, we reanalyzed an image set from a high-throughput drug screen involving a chemotherapy agent on a 3D cervical and endothelial co-culture model. Lastly, we applied this software to two naive image datasets from coculture lumen and microvascular fragment models. Results: The software accurately measured cell coverage, cancer invasion, and microvessel length, yielding drug sensitivity IC50 values with a 95% confidence level compared to manual calculations. This approach significantly reduced the image processing time from weeks down to h. Furthermore, the software was able to calculate cell coverage, microvessel length, and invasion depth from two additional microphysiological models that were imaged with confocal microscopy, highlighting the versatility of the software. Conclusions: Our free and open source software offers an automated solution for quantifying 3D cell behavior in microphysiological models assessed using non-confocal microscopy, providing the broader Cellular and Molecular Bioengineering community with an alternative to standard confocal microscopy paired with proprietary software.This software can be found in our GitHub repository: https://github.com/fogg-lab/tissue-model-analysis-tools. [ABSTRACT FROM AUTHOR]

A new derivative of mono-cyclopentyl-substituted 1,3,4-thiadiazole-2,5-dithiol (DMTD) was prepared by a telescopic synthesis procedure via the formation of the potassium 1,3,4-thiadiazole-2,5-dithiolate followed by a nucleophile attack on chlorocyclopentane. The pure product obtained in needle-like colorless crystals by recrystallization. The product was characterized by melting point, and FTIR, NMR, and mass spectra, and single-crystal X-ray diffraction. A comparative FTIR study on DMTD, mono- and bis-cyclopentyl-substituted DMTD showed the characteristic vibrational mode to differentiate thiol and thione structures. The crystallography analysis demonstrated the existence of thione tautomer, having C=S and C–N–H in [1,3,4]thiadiazole ring, with a possible hyper-conjugation between the S–H and double bond of C=N. Moreover, the TGA/DTA and DSC of DMTD, bis-cyclopentyl-substituted, and mono-cyclopentyl-substituted DMTD were studied to find a relationship between their thermal behavior and chemical structure. Then, DMTD, bis- and mono-cyclopentyl-substituted DMTD pharmaceutical activities were screened by the SwissADME tool. Moreover, they were evaluated in vitro for their antibacterial activity against the gram-negative (Escherichia coli ATCC 25922) and gram-positive (Staphylococcus aureus ATCC 25923) bacterial species by the disk diffusion method. [ABSTRACT FROM AUTHOR]

Motivation Nucleotide-binding leucine-rich repeat (NLR) family is a class of immune receptors capable of detecting and defending against pathogen invasion. They have been widely used in crop breeding. Notably, the correspondence between NLRs and effectors (CNE) determines the applicability and effectiveness of NLRs. Unfortunately, CNE data is very scarce. In fact, we've found a substantial 91 291 NLRs confirmed via wet experiments and bioinformatics methods but only 387 CNEs are recognized, which greatly restricts the potential application of NLRs. Results We propose a deep learning algorithm called ProNEP to identify NLR-effector pairs in a high-throughput manner. Specifically, we conceptualized the CNE prediction task as a protein–protein interaction (PPI) prediction task. Then, ProNEP predicts the interaction between NLRs and effectors by combining the transfer learning with a bilinear attention network. ProNEP achieves superior performance against state-of-the-art models designed for PPI predictions. Based on ProNEP, we conduct extensive identification of potential CNEs for 91 291 NLRs. With the rapid accumulation of genomic data, we expect that this tool will be widely used to predict CNEs in new species, advancing biology, immunology, and breeding. Availability and implementation The ProNEP is available at http://nerrd.cn/#/prediction. The project code is available at https://github.com/QiaoYJYJ/ProNEP. [ABSTRACT FROM AUTHOR]

The implementation of omics technologies and associated bioinformatics approaches hold significant promise for generating additional evidence for food and feed risk assessments thereby enhancing the European Food Safety Authority (EFSA) capacity to deliver scientific opinions and guidance documents in the future. To explore this possibility, EFSA launched a Call for the development of a roadmap to identify the main actions needed for a wider use of Omics in future risk assessments. To address this objective, this action roadmap outlines six project proposals. These proposals are based on a comprehensive mapping of the state‐of‐the‐art omics and associated bioinformatics technologies in research, EFSA's activities as well as current and planned activities from other relevant regulatory bodies and organisations. The outlined recommendations also address some of the identified main knowledge gaps and highlight the added value that further investments in the different food & feed safety scientific domains could bring. In addition, the work in this roadmap addresses some key challenges and blockers that might hinder a wider integration of omics in risk assessment and leverages on the opportunities for cooperation with external stakeholders. Finally, this roadmap provides suggestions on how EFSA may more broadly and effectively engage with relevant stakeholders in the use of omics technologies and associated bioinformatics approaches in regulatory science. [ABSTRACT FROM AUTHOR]

Spermatogenesis constitutes a complex and intricate cascade of differentiation, indispensable for the male reproductive competence. The intercellular communication conduits of Sertoli cells (SCs) are pivotal in orchestrating this cascade ensuring sustenance and development of germ cells. Single cells and bioinformatics recently demonstrated articles are used for the regulatory modalities through which SCs modulate spermatogenesis, specifically via androgen receptors (ARs), the transforming growth factor-beta/Smad axis, mitogen-activated protein kinases, cAMP/protein kinase A (PKA), phosphatidylinositol 4,5-bisphosphate 3-kinase (PI3k)/AKT serine threonine kinase (Akt), AMP-activated protein kinase, and AR pathways. Within this framework, homeostasis of gap junction dynamics, cryptic sites and the activities at tight junctions and adherens junctions, with the integrity of the testicular barrier, glucose assimilation, lactate distribution, being governed also along with SC maturation. Disruptions in activities or abnormal concentration in derangements in AR, cAMP/PKA, and PI3k/Akt pathways, and as well as the molecules that comprise them, would present male infertility. [ABSTRACT FROM AUTHOR]

Purpose: Coronavirus Disease 2019 (COVID‐19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID‐19 patients with sepsis. Patients and Methods: We obtained a COVID‐19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co‐expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID‐19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration. Results: The COVID‐19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID‐19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes. Conclusion: Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA‐DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID‐19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID‐19 patients with sepsis. [ABSTRACT FROM AUTHOR]

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved. [ABSTRACT FROM AUTHOR]

Previous research suggests that genetic variants in the interleukin-6 (IL-6) gene contribute to sport-related traits and athletic performance. We aimed to identify sequence variants in the IL-6 gene region comprising the 3' untranslated region (UTR) in the Turkish triathletes and sedentary individuals and assessed their putative roles in tendency to athletic phenotype. Sequence variants were identified in the Turkish triathletes (n = 47) and sedentary individuals (n = 46) by Sanger sequencing. Allele/genotype frequencies and linkage disequilibrium (LD) patterns were calculated by the Haploview program. The functional significance of the detected variants was analyzed using in silico prediction tools. Four single nucleotide variants (rs13306435, rs747302620, rs2069849, rs13306436) were detected in saliva samples of the participants by sequencing the target region. Notably, rs13306436-3'UTR/IL-6 was only seen in the triathletes, while the exonic rs747302620 was observed in only sedentary group. Also, rs13306436G>A causes loss/gain sites for binding multiple miRNAs that may be associated with athletic performance. Our findings indicate that the 3'UTR/IL-6 may have functional relevance in determining sports talent. Future comprehensive studies focusing on the IL-6 gene in athletes may pave the way for not only determining the athletic status of the individuals but also have implications for translational medicine. [ABSTRACT FROM AUTHOR]

Alopecia areata (AA) is an immune‐mediated chronic alopecia disease, but its specific pathogenesis is unclear. Gene expression data for AA patients (AAs) and healthy controls (HCs) were retrieved from the GEO database, and the differentially expressed genes (DEGs) between AAs and HCs were identified. Then, GO, KEGG and GSEA analysis were performed. A PPI network for the DEGs was then constructed to screen for hub genes, which were validated by three additional datasets. Subsequently, the potential miRNAs interacting with the hub genes were obtained through TarBase and miRNet. The differentially expressed lncRNAs (DElncRs) were obtained for subcellular localisation analysis, and the DElncRs located in the cytoplasm were further screened to identify miRNAs that interact with them. The shared miRNAs interacting with the hub genes and lncRNAs were used to construct a network of mRNA‐miRNA‐lncRNA interactions. Lastly, ROC analysis was performed to evaluate the potential diagnostic value of the hub genes and DElncRs identified. A total of 173 DEGs were obtained, mainly enriched in cytokines, chemokines, hair follicle development and hair cycle related signalling pathways. Through PPI screening and validation based on 3 additional datasets, 24 hub genes were finally yielded. Of them, five hub genes were upregulated and the potential miRNAs that interact with these five hub genes were identified. Additionally, 26 DElncRs were obtained, including 9 upregulated lncRNAs located in the cytoplasm that were predicted to interact with the miRNAs. Finally, an mRNA‐miRNA‐lncRNA regulatory network was constructed using five hub genes, four lncRNAs and their shared five miRNAs. The regulatory relationship between CD8A, mir‐185‐5p and FOXD2‐AS1 might be crucial in AA pathogenesis, with CD8A and FOXD2‐AS1 exhibiting diagnostic potential. CD8A and FOXD2‐AS1 may serve as potential therapeutic targets in AA. [ABSTRACT FROM AUTHOR]

COVID-19 remains a significant threat, particularly to vulnerable populations. The emergence of new variants necessitates the development of treatments and vaccines that induce both humoral and cellular immunity. This study aimed to identify potentially immunogenic SARS-CoV-2 peptides and to explore the intricate host–pathogen interactions involving peripheral immune responses, memory profiles, and various demographic, clinical, and lifestyle factors. Using in silico and experimental methods, we identified several CD8-restricted SARS-CoV-2 peptides that are either poorly studied or have previously unreported immunogenicity: fifteen from the Spike and three each from non-structural proteins Nsp1-2-3-16. A Spike peptide, LA-9, demonstrated a 57% response rate in ELISpot assays using PBMCs from 14 HLA-A*02:01 positive, vaccinated, and mild-COVID-19 recovered subjects, indicating its potential for diagnostics, research, and multi-epitope vaccine platforms. We also found that younger individuals, with fewer vaccine doses and longer intervals since infection, showed lower anti-Spike (ELISA) and anti-Wuhan neutralizing antibodies (pseudovirus assay), higher naïve T cells, and lower central memory, effector memory, and CD4hiCD8low T cells (flow cytometry) compared to older subjects. In our cohort, a higher prevalence of Vδ2-γδ and DN T cells, and fewer naïve CD8 T cells, seemed to correlate with strong cellular and lower anti-NP antibody responses and to associate with Omicron infection, absence of confusional state, and habitual sporting activity. [ABSTRACT FROM AUTHOR]

Seminal extracellular vesicles (SemEVs) are repositories of biomolecules, including metabolites involved in the regulation of sperm function. The correlation between the metabolite profile of SemEVs and semen parameters, along with their role in regulating sperm function, is an unexplored area. This preliminary study evaluated the metabolomic content of SemEVs. Semen samples were obtained from 18 healthy men, and SemEVs were extracted from seminal plasma using the size exclusion chromatography qEV Gen 2–35 nm column coupled with an automatic fraction collector. The physical characterization of SemEVs was carried out with the ZetaView PMX-430-Z QUATT laser system. EV protein markers were detected using Western blot. In addition, these SemEVs were used for metabolomic profiling and functional bioinformatic analysis. The mean concentration of isolated SemEVs was 1.7 ± 1.1 × 1011/mL of seminal plasma, whereas SemEVs size and zeta potential were 129.5 ± 5.5 nm and −40.03 ± 3.99 mV, respectively. Western blot analysis confirmed the presence of EV specific markers such as CD81, ALIX, and TSG101. A total of 107 metabolites were identified using this untargeted metabolomic approach in SemEVs. Bioinformatics analysis further revealed that metabolites associated with tyrosine metabolism were highly enriched in these SemEVs. Ingenuity Pathway Analysis (IPA) also indicated that these metabolites present in SemEVs were involved in the regulation of the free radical scavenging pathway. Furthermore, our metabolomic results suggest that these SemEV-associated metabolites may play a pivotal role in the maintenance of seminal plasma redox homeostasis. [ABSTRACT FROM AUTHOR]

Simple Summary: The relationship between genomic characteristics and species traits is of paramount importance for biology. For genomic characteristics, we considered characteristics close to the rate of gene change in the process of evolution, including such extreme cases as pseudogenization and gene loss. This can be conceptualized as selection at the gene level. Amino acid substitutions in the positions that functionally determine protein domains were also considered. For species traits, we considered the maximal reported lifespan, the body weight of an adult animal, and the related longevity quotient. The first and second traits are also related, but only at the stochastic level; therefore, their behavior is a priori not similar. We proposed a novel technique that allows one to determine the relationship between any genomic characteristic and species traits. This technique is exemplified in the physiologically significant genes involved in regulating circadian rhythms, which change quite rapidly during evolution. Regardless of devising this technique, the study of the genes that are critical for circadian rhythms is of interest on its own. For instance, we thoroughly examined the paralogous genes Fbxl21 and Fbxl3, which are involved in the regulation of circadian rhythms. We found out that the above-mentioned characteristic of the Fbxl21 gene correlates with the maximal reported lifespan and body weight only in two superorders of placental mammals, Euarchontoglires (the clades Euarchonta, Lagomorpha, and Rodentia) and Afrotheria. On the contrary, such a correlation is not observed in other superorders, such as Laurasiatheria and Xenarthra. The presence or absence of the correlation is confirmed statistically with a very high accuracy. The rate of change in the Fbxl21 gene indicates, for example, the peculiarity of its evolution in apes. This article proposes a methodology for establishing a relationship between the change rate of a given gene (relative to a given taxon) together with the amino acid composition of the proteins encoded by this gene and the traits of the species containing this gene. The methodology is illustrated based on the mammalian genes responsible for regulating the circadian rhythms that underlie a number of human disorders, particularly those associated with aging. The methods used are statistical and bioinformatic ones. A systematic search for orthologues, pseudogenes, and gene losses was performed using our previously developed methods. It is demonstrated that the least conserved Fbxl21 gene in the Euarchontoglires superorder exhibits a statistically significant connection of genomic characteristics (the median of dN/dS for a gene relative to all the other orthologous genes of a taxon, as well as the preference or avoidance of certain amino acids in its protein) with species-specific lifespan and body weight. In contrast, no such connection is observed for Fbxl21 in the Laurasiatheria superorder. This study goes beyond the protein-coding genes, since the accumulation of amino acid substitutions in the course of evolution leads to pseudogenization and even gene loss, although the relationship between the genomic characteristics and the species traits is still preserved. The proposed methodology is illustrated using the examples of circadian rhythm genes and proteins in placental mammals, e.g., longevity is connected with the rate of Fbxl21 gene change, pseudogenization or gene loss, and specific amino acid substitutions (e.g., asparagine at the 19th position of the CRY-binding domain) in the protein encoded by this gene. [ABSTRACT FROM AUTHOR]

Objective: Using bioinformatics methods to study the immune infiltration mechanism of lupus nephritis (LN) and to explore potential target Chinese medicines, which can provide new directions for clinical treatment of LN. Methods: Gene expression profile microarray dataset of LN was downloaded from GEO database, differential expressed genes (DEGs) were screened using R software, and GO and KEGG enrichment analysis were performed on these DEGs. Protein interaction network analysis of DEGs was performed by applying STRING database, key genes were screened by using Cytoscape, core gene expression was validated by Nephroseq database, and the infiltration and correlation of 22 kinds of immune cells in LN were calculated by CIBERSORT deconvolution algorithm. Finally, herbal prediction of significantly enriched immune-related biological processes and key target genes were performed by Coremine Medical database. Results: A total of 367 LN-related DEGs were obtained, of which 253 were up-regulated and 114 were down-regulated. GO was mainly enriched in response to viruses, defense response to viruses, regulation of viral life cycle, regulation of viral processes, etc; KEGG was mainly enriched in S. aureus infection, COVID-19, influenza A, complement and coagulation cascade, pertussis, etc. Ten key genes were screened: IFIT3, OAS2, MX1, OAS1, RSAD2, XAF1, ISG15, IFIT1, ITGAM and PTPRC. Immune infiltration by CIBERSORT deconvolution algorithm showed that monocytes and initial B cells were highly expressed in LN, while memory B cells, naive CD4+T cells, follicular helper T cells and resting natural killer cells were lowly expressed in LN. Immune cell correlation analysis showed a positive correlation between initial B cells and plasma cells, a positive correlation between resting natural killer cells and resting dendritic cells, a negative correlation between monocytes and regulatory T cells, and a negative correlation between resting CD4+ memory T cells and monocytes. Coremine Medical predicted that Salvia miltiorrhiza, Panax notoginseng, Scutellaria baicalensis, ginseng, honey, monkey mushroom and peony were found to be most closely related to immunity in LN. Conclusion: The development and progression of LN are results of the combined involvement of multiple genes and pathways. Monocytes, memory B cells, initial CD4+T cells and initial B cells are most closely associated with LN. The predicted Salvia miltiorrhiza, Panax notoginseng, Scutellaria baicalensis, ginseng, honey, monkey mushroom and peony may be used as target herbs for the potential treatment of LN. [ABSTRACT FROM AUTHOR]

Objective: To explore expression and prognostic value of oncostatin M (OSM) in endometrial cancer and to analyze relationship between OSM expression and immune cell infiltration in endometrial cancer tissues. Methods: OSM expression in pan-cancer was analyzed by TIMER database, OSM expression in endometrial cancer and normal tissues was compared, and survival analysis for patients with different OSM expression was performed; relationship between OSM expression and immune cell infiltration was analyzed by TIMER and TISIDB, and ssGSEA algorithm was used to calculate difference in abundance of immune cell infiltration in samples with different OSM expression; GSEA software was applied to perform enrichment analysis; clinical tissue samples were collected for validation. Results: OSM expression was higher in endometrial cancer tissues than that in normal endometrial tissues (P=4.1e-28), and endometrial cancer patients with high OSM expression had prolonged recurrence-free survival (RFS) (P=0.004 8). OSM expression was positively correlated with abundance of immune cell infiltration and genetic markers of immune cells (P<0.05). OSM was mainly enriched in immune-related signaling pathways. OSM expression was higher in endometrial cancer tissues than normal and atypical hyperplastic tissues (P=0.016 9). Proportions of immune cell markers CD4, CD8, and CD68 were increased in tumor tissues with high OSM expression (all P<0.05), which were positively correlated with OSM expression. Conclusion: OSM is highly expressed in endometrial cancer tissues and correlated with prognosis; OSM expression is positively correlated with immune cell infiltration level and can be used as a biomarker for immunotherapy and prognosis. [ABSTRACT FROM AUTHOR]