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BACKGROUND: Primary osteoporosis has a high incidence, but the pathogenesis is not fully understood. Currently, there is a lack of effective early screening indicators and treatment programs. OBJECTIVE: To further explore the mechanism of primary osteoporosis through comprehensive bioinformatics analysis. METHODS: The primary osteoporosis data were obtained from the gene expression omnibus (GEO) database, and the differentially expressed genes were screened for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In addition, the differentially expressed genes were subjected to protein-protein interaction network to determine the core genes related to primary osteoporosis, and the least absolute shrinkage and selection operator algorithm was used to identify and verify the primary osteoporosis-related biomarkers. Immune cell correlation analysis, gene enrichment analysis and drug target network analysis were performed. Finally, the biomarkers were validated using qPCR assay. RESULTS AND CONCLUSION: A total of 126 differentially expressed genes and 5 biomarkers including prostaglandins, epidermal growth factor receptor, mitogen-activated protein kinase 3, transforming growth factor B1, and retinoblastoma gene 1 were obtained in this study. GO analysis showed that differentially expressed genes were mainly concentrated in the cellular response to oxidative stress and the regulation of autophagy. KEGG analysis showed that autophagy and senescence pathways were mainly involved. Immunoassay of biomarkers showed that prostaglandins, retinoblastoma gene 1, and mitogen-activated protein kinase 3 were closely related to immune cells. Gene enrichment analysis showed that biomarkers were associated with immune-related pathways. Drug target network analysis showed that the five biomarkers were associated with primary osteoporosis drugs. The results of qPCR showed that the expression of prostaglandins, epidermal growth factor receptor, mitogen-activated protein kinase 3, and transforming growth factor B1 in the primary osteoporosis sample was significantly increased compared with the control sample (P < 0.001), while the expression of retinoblastoma gene 1 in the primary osteoporosis sample was significantly decreased compared with the control sample (P < 0.001). Overall, the study screened and validated five potential biomarkers of primary osteoporosis, providing a reference basis for further in-depth investigation of the pathogenesis, early screening and diagnosis, and targeted treatment of primary osteoporosis. [ABSTRACT FROM AUTHOR]

Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advancements in network alignment research, focusing on analysing network alignment characteristics and progress in various domains such as social network analysis, bioinformatics, computational linguistics and privacy protection. It provides a detailed analysis of various methods' implementation principles, processes, and performance differences, including structure consistency-based methods, network embedding-based methods, and graph neural network-based (GNN-based) methods. Additionally, the methods for network alignment under different conditions, such as in attributed networks, heterogeneous networks, directed networks, and dynamic networks, are presented. Furthermore, the challenges and the open issues for future studies are also discussed. [ABSTRACT FROM AUTHOR]

Breast cancer is a highly heterogeneous disorder characterized by dysregulated expression of number of genes and their cascades. It is one of the most common types of cancer in women posing serious health concerns globally. Recent developments and discovery of specific prognostic biomarkers have enabled its application toward developing personalized therapies. The basic premise of this study was to investigate key signature genes and signaling pathways involved in triple-negative breast cancer using bioinformatics approach. Microarray data set GSE65194 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus was used for identification of differentially expressed genes (DEGs) using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were carried out using the ClueGO plugin in Cytoscape software. The up-regulated DEGs were primarily engaged in the regulation of cell cycle, overexpression of spindle assembly checkpoint, and so on, whereas down-regulated DEGs were employed in alteration to major signaling pathways and metabolic reprogramming. The hub genes were identified using cytoHubba from protein-protein interaction (PPI) network for top up-regulated and down-regulated DEG's plugin in Cytoscape software. The hub genes were validated as potential signature biomarkers by evaluating the overall survival percentage in breast cancer patients. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) and Huntington's Disease (HD) are distinct neurodevelopmental and neurodegenerative disorders, respectively, characterized by significant genetic and molecular alterations. ASD primarily affects early childhood and is associated with genetic mutations impacting brain development, while HD, an autosomal dominant disorder, leads to progressive neurodegeneration due to mutations in the HTT gene. Despite their differences, both disorders share common genetic pathways and molecular mechanisms. This study aims to explore the genetic and molecular connections between ASD and HD through a comprehensive analysis of differentially expressed genes (DEGs) and protein–protein interaction (PPI) networks to uncover shared pathways and potential overlapping mechanisms. Transcriptomic data were acquired from the NCBI-GEO database, specifically GSE180185 for ASD and GSE1751 for HD. DEGs were identified using thresholds of log2 fold change (FC)>1 and an adjusted p -value <0.05. Common DEGs between the two disorders were determined and analyzed using Cytoscape's STRING app to construct a PPI network with a confidence level of 0.7. Functional enrichment was conducted through KEGG and Gene Ontology (GO) analyses. Key regulatory modules and hubs were identified using CytoNCA and MCODE plugins. The ASD dataset revealed 565 DEGs, with 206 upregulated and 347 downregulated, while the HD dataset had 1091 DEGs, with 743 upregulated and 202 downregulated. Twelve genes were common to both conditions, including 4 upregulated and 8 downregulated. The PPI network comprised 62 nodes and 215 edges, with significant pathways including ascorbate metabolism and steroid hormone biosynthesis. Notably, Module 3, containing 12 nodes, was linked to EGFR tyrosine kinase resistance and apoptosis. This study identifies shared genetic and molecular pathways between ASD and HD, highlighting common regulatory mechanisms and potential targets for further research. The use of transcriptomic data and PPI network analysis reveals significant overlaps in the molecular mechanisms underlying these disorders. Further experimental validation and expanded dataset analyses could elucidate specific interactions and enhance our understanding of the shared pathways. Investigating these common mechanisms may also provide insights into potential therapeutic approaches for both ASD and HD. Differentially expressed genes (DEGs) in autism spectrum disorder (ASD) and Huntington's disease (HD) datasets reveal significant genetic overlaps, including 12 common DEGs identified. Protein-protein interaction (PPI) network analysis highlights key hub genes and functional modules enriched in EGFR tyrosine kinase resistance, apoptosis, and other critical pathways. Functional enrichment and gene ontology analyses indicate shared biological processes, cellular components, and molecular functions, offering potential therapeutic targets for both ASD and HD. [ABSTRACT FROM AUTHOR]

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein–protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor–micro-RNA regulatory and co-regulatory networks, the gene–microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a slow brain degeneration disorder in which the accumulation of beta-amyloid precursor plaque and an intracellular neurofibrillary tangle of hyper-phosphorylated tau proteins in the brain have been implicated in neurodegeneration. In this study, we identified the most important genes that are unique and sensitive in the entorhinal region of the brain to target AD effectively. At first, microarrays data are selected and constructed protein-protein interaction network (PPIN) and gene regulatory network (GRN) from differentially expressed genes (DEGs) using Cytoscape software. Then, networks analysis was performed to determine hubs, bottlenecks, clusters, and signaling pathways in AD. Finally, critical genes were selected as targets for repurposing drugs. Analyzing the constructed PPIN and GRN identified CD44, ELF1, HSP90AB1, NOC4L, BYSL, RRP7A, SLC17A6, and RUVBL2 as critical genes that are dysregulated in the entorhinal region of AD suffering patients. The functional enrichment analysis revealed that DEG nodes are involved in the synaptic vesicle cycle, glutamatergic synapse, PI3K-Akt signaling pathway, retrograde endocannabinoid signaling, endocrine and other factor-regulated calcium reabsorption, ribosome biogenesis in eukaryotes, and nicotine addiction. Gentamicin, isoproterenol, and tumor necrosis factor are repurposing new drugs that target CD44, which plays an important role in the development of AD. Following our model validation using the existing experimental data, our model based on previous experimental reports suggested critical molecules and candidate drugs involved in AD for further investigations in vitro and in vivo. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) is a prevalent condition characterized by defective heart development, causing premature death and stillbirths among infants. Genome-wide association studies (GWASs) have provided insights into the role of genetic variants in CHD pathogenesis through the identification of a comprehensive set of single-nucleotide polymorphisms (SNPs). Notably, 90–95% of these variants reside in the noncoding genome, complicating the understanding of their underlying mechanisms. Here, we developed a systematic computational pipeline for the identification and analysis of CHD-associated SNPs spanning both coding and noncoding regions of the genome. Initially, we curated a thorough dataset of SNPs from GWAS-catalog and ClinVar database and filtered them based on CHD-related traits. Subsequently, these CHD-SNPs were annotated and categorized into noncoding and coding regions based on their location. To study the functional implications of noncoding CHD-SNPs, we cross-validated them with enhancer-specific histone modification marks from developing human heart across 9 Carnegie stages and identified potential cardiac enhancers. This approach led to the identification of 2,056 CHD-associated putative enhancers (CHD-enhancers), 38.9% of them overlapping with known enhancers catalogued in human enhancer disease database. We identified heart-related transcription factor binding sites within these CHD-enhancers, offering insights into the impact of SNPs on TF binding. Conservation analysis further revealed that many of these CHD-enhancers were highly conserved across vertebrates, suggesting their evolutionary significance. Utilizing heart-specific expression quantitative trait loci data, we further identified a subset of 63 CHD-SNPs with regulatory potential distributed across various cardiac tissues. Concurrently, coding CHD-SNPs were represented as a protein interaction network and its subsequent binding energy analysis focused on a pair of proteins within this network, pinpointed a deleterious coding CHD-SNP, rs770030288, located in C2 domain of MYBPC3 protein. Overall, our findings demonstrate that SNPs have the potential to disrupt gene regulatory systems, either by affecting enhancer sequences or modulating protein-protein interactions, which can lead to abnormal developmental processes contributing to CHD pathogenesis. [ABSTRACT FROM AUTHOR]

A protein complex can be regarded as a functional module developed by interacting proteins. The protein complex has attracted significant attention in bioinformatics as a critical substance in life activities. Identifying protein complexes in protein–protein interaction (PPI) networks is vital in life sciences and biological activities. Therefore, significant efforts have been made recently in biological experimental methods and computing methods to detect protein complexes accurately. This study proposed a new method for PPI networks to facilitate the processing and development of the following algorithms. Then, a combination of the improved density peaks clustering algorithm (DPC) and the fuzzy C-means clustering algorithm (FCM) was proposed to overcome the shortcomings of the traditional FCM algorithm. In other words, the rationality of results obtained using the FCM algorithm is closely related to the selection of cluster centers. The objective function of the FCM algorithm was redesigned based on 'high cohesion' and 'low coupling'. An adaptive parameter-adjusting algorithm was designed to optimize the parameters of the proposed detection algorithm. This algorithm is denoted as the DFPO algorithm (DPC-FCM Parameter Optimization). Finally, the performance of the DFPO algorithm was evaluated using multiple metrics and compared with over ten state-of-the-art protein complex detection algorithms. Experimental results indicate that the proposed DFPO algorithm exhibits improved detection accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is one of the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted co-expression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNA to find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules as the second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30a are important miRNAs in breast cancer and also investigated their roles in breast cancer progression.

Abstract Background Infectious uveitis is a significant cause of visual impairment worldwide, caused by diverse pathogens such as viruses, bacteria, fungi, and parasites. Understanding its prevalence, etiology, pathogenesis, molecular mechanism, and clinical manifestations is essential for effective diagnosis and management. Methods A systematic literature search was conducted using PubMed, Google Scholar, Web of Science, Scopus, and Embase, focusing on studies published in the last fifteen years from 2009 to 2023. Keywords included “uveitis,” “infectious uveitis,” “viral uveitis,” and others. Rigorous inclusion and exclusion criteria were applied, and data were synthesized thematically. Gene symbols related to infectious uveitis were analyzed using protein-protein interaction (PPI) networks and pathway analyses to uncover molecular mechanisms associated with infectious uveitis. Results The search from different databases yielded 97 eligible studies. The review identified a significant rise in publications on infectious uveitis, particularly viral uveitis, over the past fifteen years. Infectious uveitis prevalence varies geographically, with high rates in developing regions due to systemic infections and limited diagnostic resources. Etiologies include viruses (39%), bacteria (17%), and other pathogens, substantially impacting adults aged 20–50 years. Pathogenesis involves complex interactions between infectious agents and the ocular immune response, with key roles for cytokines and chemokines. The PPI network highlighted IFNG, IL6, TNF, and CD4 as central nodes. Enriched pathways included cytokine-cytokine receptor interaction and JAK-STAT signaling. Clinical manifestations range from anterior to posterior uveitis, with systemic symptoms often accompanying ocular signs. Diagnostic strategies encompass clinical evaluation, laboratory tests, and imaging, while management involves targeted antimicrobial therapy and anti-inflammatory agents. Conclusion This review underscores the complexity of infectious uveitis, driven by diverse pathogens and influenced by various geographical and systemic factors. Molecular insights from PPI networks and pathway analyses provide a deeper understanding of its pathogenesis. Effective management requires comprehensive diagnostic approaches and targeted therapeutic strategies.

Abstract Angiogenesis-osteogenesis coupling is critical for proper functioning and maintaining the health of bones. Any disruption in this coupling, associated with aging and disease, might lead to loss of bone mass. Osteoporosis (OP) is a debilitating bone metabolic disorder that affects the microarchitecture of bones, gradually leading to fracture. Computational analysis revealed that normal angiogenesis is disrupted during the progression of OP, especially postmenopausal osteoporosis (PMOP). The genes associated with OP and PMOP were retrieved from the DisGeNET database. Hub gene analysis and molecular pathway enrichment were performed via the Cytoscape plugins STRING, MCODE, CytoHubba, ClueGO and the web-based tool Enrichr. Twenty-eight (28) hub genes were identified, eight of which were transcription factors (HIF1A, JUN, TP53, ESR1, MYC, PPARG, RUNX2 and SOX9). Analysis of SNPs associated with hub genes via the gnomAD, I-Mutant2.0, MUpro, ConSurf and COACH servers revealed the substitution F201L in IL6 as the most deleterious. The IL6 protein was modeled in the SWISS-MODEL server and the substitution was analyzed via the YASARA FoldX plugin. A positive ΔΔG (1.936) of the F201L mutant indicates that the mutated structure is less stable than the wild-type structure is. Thirteen hub genes, including IL6 and the enriched molecular pathways were found to be profoundly involved in angiogenesis/endothelial function and immune signaling. Mechanical loading of bones through weight-bearing exercises can activate osteoblasts via mechanotransduction leading to increased bone formation. The present study suggests proper mechanical loading of bone as a preventive strategy for PMOP, by which angiogenesis and the immune status of the bone can be maintained. This in silico analysis could be used to understand the molecular etiology of OP and to develop novel therapeutic approaches.

Opioid use disorder (OUD) affects millions of people worldwide. While it is known that OUD originates from many factors, including social and environmental factors, the role of genetic variants in developing the disease has also been reported. This study aims to investigate the genetic variants associated with the risk of developing OUD upon exposure. Twenty-three subjects who had previously been given opioid-based painkillers to undergo minor surgical treatment were recruited at Prisma Health Upstate clinic and elsewhere. Eleven were considered nonpersistent opioid users (controls), and 12 were persistent opioid users (cases) at the time of sample collection after an initial surgery. The subjects were asked to provide saliva samples, which were subjected to DNA sequencing at Clemson University Center for Human Genetics, and variant calling was performed. The genome-wide association studies (GWASs) for genes known to be associated with OUD resulted in 13 variants (intronic or SNV) with genome-wide significance (raw p-value < 0.01) and two missense variants, rs6265 (p.Val66Met in BNDF isoform a) and rs1799971 (p.Asn40Asp) in OPRM1, previously reported in the literature. Furthermore, extending the GWASs to find all genomic variants and filtering the variants to include only variants found in cases (persistent opioid users) but not in controls (nonpersistent opioid users) resulted in 11 new variants (p-value < 0.005). Considering that OUD is a complex disease and the effect might come from different variants in the same genes, we performed a co-occurrence analysis of variants on the genes. We identified eight additional genes that harbor multiple variants, including four genes: LRFN3, ZMIZ1, RYR3, and OR1L6, with three or more variants in the case subjects but not in the control individuals. The performed PPI network construction, along with functional enrichment, indicated that the variants occur in calcium signaling, circadian entrainment, morphine addiction, alcoholism, and opioid signaling pathways, which are closely related to OUD or addiction in general. [ABSTRACT FROM AUTHOR]

We aimed to develop and validate a protein risk score for predicting Alzheimer's disease (AD) and compare its performance with a validated clinical risk model (Cognitive Health and Dementia Risk Index for AD [CogDrisk‐AD]) and apolipoprotein E (APOE) genotypes. The development cohort, consisting of 35,547 participants from England in the UK Biobank, was randomly divided into a 7:3 training–testing ratio. The validation cohort included 4667 participants from Scotland and Wales in the UK Biobank. In the training set, an AD protein risk score was constructed using 31 proteins out of 2911 proteins. In the testing set, the AD protein risk score had a C‐index of 0.867 (95% CI, 0.828, 0.906) for AD prediction, followed by CogDrisk‐AD risk factors (C‐index, 0.856; 95% CI, 0.823, 0.889), and APOE genotypes (C‐index, 0.705; 95% CI, 0.660, 0.750). Adding the AD protein risk score to CogDrisk‐AD risk factors (C‐index increase, 0.050; 95% CI, 0.008, 0.093) significantly improved the predictive performance for AD. However, adding CogDrisk‐AD risk factors (C‐index increase, 0.040; 95% CI, −0.007, 0.086) or APOE genotypes (C‐index increase, 0.000; 95% CI, −0.054, 0.055) to the AD protein risk score did not significantly improve the predictive performance for AD. The top 10 proteins with the highest coefficients in the AD protein risk score contributed most of the predictive power for AD risk. These results were verified in the external validation cohort. EGFR, GFAP, and CHGA were identified as key proteins within the protein network. Our result suggests that the AD protein risk score demonstrated a good predictive performance for AD risk. [ABSTRACT FROM AUTHOR]

Background: Infectious uveitis is a significant cause of visual impairment worldwide, caused by diverse pathogens such as viruses, bacteria, fungi, and parasites. Understanding its prevalence, etiology, pathogenesis, molecular mechanism, and clinical manifestations is essential for effective diagnosis and management. Methods: A systematic literature search was conducted using PubMed, Google Scholar, Web of Science, Scopus, and Embase, focusing on studies published in the last fifteen years from 2009 to 2023. Keywords included "uveitis," "infectious uveitis," "viral uveitis," and others. Rigorous inclusion and exclusion criteria were applied, and data were synthesized thematically. Gene symbols related to infectious uveitis were analyzed using protein-protein interaction (PPI) networks and pathway analyses to uncover molecular mechanisms associated with infectious uveitis. Results: The search from different databases yielded 97 eligible studies. The review identified a significant rise in publications on infectious uveitis, particularly viral uveitis, over the past fifteen years. Infectious uveitis prevalence varies geographically, with high rates in developing regions due to systemic infections and limited diagnostic resources. Etiologies include viruses (39%), bacteria (17%), and other pathogens, substantially impacting adults aged 20–50 years. Pathogenesis involves complex interactions between infectious agents and the ocular immune response, with key roles for cytokines and chemokines. The PPI network highlighted IFNG, IL6, TNF, and CD4 as central nodes. Enriched pathways included cytokine-cytokine receptor interaction and JAK-STAT signaling. Clinical manifestations range from anterior to posterior uveitis, with systemic symptoms often accompanying ocular signs. Diagnostic strategies encompass clinical evaluation, laboratory tests, and imaging, while management involves targeted antimicrobial therapy and anti-inflammatory agents. Conclusion: This review underscores the complexity of infectious uveitis, driven by diverse pathogens and influenced by various geographical and systemic factors. Molecular insights from PPI networks and pathway analyses provide a deeper understanding of its pathogenesis. Effective management requires comprehensive diagnostic approaches and targeted therapeutic strategies. [ABSTRACT FROM AUTHOR]

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes. [ABSTRACT FROM AUTHOR]

Being a core tool, graph theory, as a mathematical formalism in mathematical chemistry, has become an essential approach for studying the complex behavior and interactions in protein systems. Here, we review recent advances in the field, particularly in our group, including the methods developed to access protein functions and their applications in disease biology. First, we provide the necessary background and definitions of graph-based structures and network centralities, and methodologies developed at the node-, subgraph- and pathway-levels. We then review the applications of how to use these algorithms to gain new biological insights, ranging from protein structures to protein dynamics, and interactions for linking genotypes and phenotypes. Furthermore, we discuss immediate challenges in the multilayer network, which is more realistic in the biological world, and hope to draw increasing attention from mathematicians, especially graph theorists, to reveal the basic principles of "networks of networks". [ABSTRACT FROM AUTHOR]

Angiogenesis-osteogenesis coupling is critical for proper functioning and maintaining the health of bones. Any disruption in this coupling, associated with aging and disease, might lead to loss of bone mass. Osteoporosis (OP) is a debilitating bone metabolic disorder that affects the microarchitecture of bones, gradually leading to fracture. Computational analysis revealed that normal angiogenesis is disrupted during the progression of OP, especially postmenopausal osteoporosis (PMOP). The genes associated with OP and PMOP were retrieved from the DisGeNET database. Hub gene analysis and molecular pathway enrichment were performed via the Cytoscape plugins STRING, MCODE, CytoHubba, ClueGO and the web-based tool Enrichr. Twenty-eight (28) hub genes were identified, eight of which were transcription factors (HIF1A, JUN, TP53, ESR1, MYC, PPARG, RUNX2 and SOX9). Analysis of SNPs associated with hub genes via the gnomAD, I-Mutant2.0, MUpro, ConSurf and COACH servers revealed the substitution F201L in IL6 as the most deleterious. The IL6 protein was modeled in the SWISS-MODEL server and the substitution was analyzed via the YASARA FoldX plugin. A positive ΔΔG (1.936) of the F201L mutant indicates that the mutated structure is less stable than the wild-type structure is. Thirteen hub genes, including IL6 and the enriched molecular pathways were found to be profoundly involved in angiogenesis/endothelial function and immune signaling. Mechanical loading of bones through weight-bearing exercises can activate osteoblasts via mechanotransduction leading to increased bone formation. The present study suggests proper mechanical loading of bone as a preventive strategy for PMOP, by which angiogenesis and the immune status of the bone can be maintained. This in silico analysis could be used to understand the molecular etiology of OP and to develop novel therapeutic approaches. [ABSTRACT FROM AUTHOR]

A total of 138 cDEGs were screened from mediastinal lymph nodes and peripheral whole blood. Among them, 6 hub cDEGs including CTSS, CYBB, FPR2, MNDA, TLR1 and TLR8 with elevated degree and betweenness levels were illustrated in protein-protein interaction network. In comparison to healthy controls, CTSS (1.61 vs. 1.05), CYBB (1.68 vs. 1.07), FPR2 (2.77 vs. 0.96), MNDA (2.14 vs. 1.23), TLR1 (1.56 vs. 1.09), and TLR8 (2.14 vs. 0.98) displayed notably elevated expression levels within pulmonary sarcoidosis PBMC samples (P < 0.0001 for FPR2 and P < 0.05 for others), echoing with prior mRNA microarray findings. The most significant functional pathways were immune response, inflammatory response, plasma membrane and extracellular exosome, with 6 hub cDEGs distributing along these pathways. CTSS, CYBB, FPR2, MNDA, TLR1, and TLR8 could be conducive to improving the diagnostic process and understanding the underlying mechanisms of pulmonary sarcoidosis. [ABSTRACT FROM AUTHOR]

ABSTRACT Background The breakthrough discovery of novel biomarkers with prognostic and diagnostic value enables timely medical intervention for the survival of patients diagnosed with gastric cancer (GC). Typically, in studies focused on biomarker analysis, highly connected nodes (hubs) within the protein–protein interaction network (PPIN) are proposed as potential biomarkers. However, this study revealed an unexpected finding following the clustering of network nodes. Consequently, it is essential not to overlook weakly connected nodes (nonhubs) when determining suitable biomarkers from PPIN. Methods and Results In this study, several potential biomarkers for GC were proposed based on the findings from RNA‐sequencing (RNA‐Seq) datasets, along with differential gene expression (DGE) analysis, PPINs, and weighted gene co‐expression network analysis (WGCNA). Considering the overall survival (OS) analysis and the evaluation of expression levels alongside statistical parameters of the PPIN cluster nodes, it is plausible to suggest that THY1, CDH17, TGIF1, and AEBP1, categorized as nonhub nodes, along with ITGA5, COL1A1, FN1, and MMP2, identified as hub nodes, possess characteristics that render them applicable as biomarkers for the GC. Additionally, insulin‐like growth factor (IGF)‐binding protein‐2 (IGFBP2), classified as a nonhub node, demonstrates a significant negative correlation with both groups within the same cluster. This observation underscores the conflicting findings regarding IGFBP2 in various cancer studies and enhances the potential of this gene to serve as a biomarker. Conclusion The findings of the current study not only identified the hubs and nonhubs that may serve as potential biomarkers for GC but also revealed a PPIN cluster that includes both hubs and nonhubs in conjunction with IGFBP2, thereby enhancing the understanding of the complex behavior associated with IGFBP2.

Abstract Background Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive motoneuron degenerative disorder. There are still no drugs capable of slowing disease evolution or improving life quality of ALS patients. Thus, autologous stem cell therapy has emerged as an alternative treatment regime to be investigated in clinical ALS. Method Using Proteomics and Protein-Protein Interaction Network analyses combined with bioinformatics, the possible cellular mechanisms and molecular targets related to mesenchymal stem cells (MSCs, 1 × 106 cells/kg, intrathecally in the lumbar region of the spine) were investigated in cerebrospinal fluid (CSF) of ALS patients who received intrathecal infusions of autologous bone marrow-derived MSCs thirty days after cell therapy. Data are available via ProteomeXchange with identifier PXD053129. Results Proteomics revealed 220 deregulated proteins in CSF of ALS subjects treated with MSCs compared to CSF collected from the same patients prior to MSCs infusion. Bioinformatics enriched analyses highlighted events of Extracellular matrix and Cell adhesion molecules as well as related key targets APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG in the CSF of cell treated ALS subjects. Conclusions Extracellular matrix and cell adhesion molecules as well as their related highlighted components have emerged as key targets of autologous MSCs in CSF of ALS patients. Trial registration Clinicaltrial.gov identifier NCT0291768. Registered 28 September 2016.

Abstract Bluetongue virus (BTV) infection induces profound and intricate changes in the transcriptional profile of the host to facilitate its survival and replication. However, there have been no whole-transcriptome studies on ovine lung microvascular endothelial cells (OLMECs) infected with BTV. In this study, we comprehensively analysed the whole-transcriptome sequences of BTV-1 serotype-infected and mock-infected OLMECs and subsequently performed bioinformatics differential analysis. Our analysis revealed 1215 differentially expressed mRNA transcripts, 82 differentially expressed long noncoding RNAs (lncRNAs) transcripts, 63 differentially expressed microRNAs (miRNAs) transcripts, and 42 differentially expressed circular RNAs (circRNAs) transcripts. Annotation from Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of endogenous competing RNA network analysis revealed that the differentially expressed RNAs primarily participated in viral sensing and signal transduction pathways, antiviral and immune responses, inflammation, and extracellular matrix (ECM)-related pathways. Furthermore, protein‒protein interaction network analysis revealed that BTV may regulate the conformation of ECM receptor proteins and change their biological activity through a series of complex mechanisms. Finally, on the basis of real-time fluorescence quantitative polymerase chain reaction results, the expression trends of the differentially expressed RNA were consistent with the whole-transcriptome sequencing data, such as downregulation of the expression of COL4A1, ITGA8, ITGB5, and TNC and upregulation of the expression of CXCL10, RNASEL, IRF3, IRF7, and IFIHI. This study provides a novel perspective for further investigations of the mechanism of the ECM in the BTV-host interactome and the pathogenesis of lung microvascular endothelial cells.

Abstract Background Although rheumatoid arthritis (RA) is a chronic systemic tissue disease often accompanied by osteoporosis (OP), the molecular mechanisms underlying this association remain unclear. This study aimed to elucidate the pathogenesis of RA and OP by identifying differentially expressed mRNAs (DEmRNAs) and long non-coding RNAs (lncRNAs) using a bioinformatics approach. Methods Expression profiles of individuals diagnosed with OP and RA were retrieved from the Gene Expression Omnibus database. Differential expression analysis was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway enrichment analyses were performed to gain insights into the functional categories and molecular/biochemical pathways associated with DEmRNAs. We identified the intersection of common DEmRNAs and lncRNAs and constructed a protein-protein interaction (PPI) network. Correlation analysis between the common DEmRNAs and lncRNAs facilitated the construction of a coding-non-coding network. Lastly, serum peripheral blood mononuclear cells (PBMCs) from patients with RA and OP, as well as healthy controls, were obtained for TRAP staining and qRT-PCR to validate the findings obtained from the online dataset assessments. Results A total of 28 DEmRNAs and 2 DElncRNAs were identified in individuals with both RA and OP. Chromosomal distribution analysis of the consensus DEmRNAs revealed that chromosome 1 had the highest number of differential expression genes. GO and KEGG analyses indicated that these DEmRNAs were primarily associated with " platelets (PLTs) degranulation”, “platelet alpha granules”, “platelet activation”, “tight junctions” and “leukocyte transendothelial migration”, with many genes functionally related to PLTs. In the PPI network, MT-ATP6 and PTGS1 emerged as potential hub genes, with MT-ATP6 originating from mitochondrial DNA. Co-expression analysis identified two key lncRNA-mRNA pairs: RP11 − 815J21.2 with MT − ATP6 and RP11 − 815J21.2 with PTGS1. Experimental validation confirmed significant differential expression of RP11-815J21.2, MT-ATP6 and PTGS1 between the healthy controls and the RA + OP groups. Notably, knockdown of RP11-815J21.2 attenuated TNF + IL-6-induced osteoclastogenesis. Conclusions This study successfully identified shared dysregulated genes and potential therapeutic targets in individuals with RA and OP, highlighting their molecular similarities. These findings provide new insights into the pathogenesis of RA and OP and suggest potential avenues for further research and targeted therapies.

Introduction: Breast cancer, which is one of the most common cancers with high mortality in women, has always been the focus of researchers, and every day, scientists are trying to identify mechanisms, genes, and medicines related to this disease. Nowadays, bioinformatics methods are used to identify and repurpose drugs for the treatment of diseases, especially cancer. Material & Methods: In this study, bioinformatics and biological network analysis were used to identify candidate drugs for breast cancer treatment. In this regard, analysis of the protein interaction network and drug-gene network were employed. The needed data were collected from the GEO database with the access code GSE54002. For the selected data set, genes with significant expression changes between two groups of healthy people and people with breast cancer cases were selected and considered primary genes. Thereafter, the protein-protein interaction network was constructed using the STRING database, and a significant gene module was obtained from the network. Following that, gene ontology studies and biological pathways were conducted. Next, the drug-gene network was constructed to identify drugs that target module genes and were introduced as essential drugs for the treatment of breast cancer. Cytoscape software and STRING and OncoDB databases were used to reconstruct and analyze the networks. Results: After analyzing the protein-protein interaction network and the drug-gene network, three important drugs that target the genes of the modules were identified and introduced as candidate drugs for the treatment of breast cancer. These drugs were RG-1530, R-406, and GW441756x. Discussion & Conclusion: The obtained results demonstrated that the introduced drugs (RG-1530, R-406, and GW441756x) can be effective in the treatment of breast cancer

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein–protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein–protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92–96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

Bluetongue virus (BTV) infection induces profound and intricate changes in the transcriptional profile of the host to facilitate its survival and replication. However, there have been no whole-transcriptome studies on ovine lung microvascular endothelial cells (OLMECs) infected with BTV. In this study, we comprehensively analysed the whole-transcriptome sequences of BTV-1 serotype-infected and mock-infected OLMECs and subsequently performed bioinformatics differential analysis. Our analysis revealed 1215 differentially expressed mRNA transcripts, 82 differentially expressed long noncoding RNAs (lncRNAs) transcripts, 63 differentially expressed microRNAs (miRNAs) transcripts, and 42 differentially expressed circular RNAs (circRNAs) transcripts. Annotation from Gene Ontology, enrichment from the Kyoto Encyclopedia of Genes and Genomes, and construction of endogenous competing RNA network analysis revealed that the differentially expressed RNAs primarily participated in viral sensing and signal transduction pathways, antiviral and immune responses, inflammation, and extracellular matrix (ECM)-related pathways. Furthermore, protein‒protein interaction network analysis revealed that BTV may regulate the conformation of ECM receptor proteins and change their biological activity through a series of complex mechanisms. Finally, on the basis of real-time fluorescence quantitative polymerase chain reaction results, the expression trends of the differentially expressed RNA were consistent with the whole-transcriptome sequencing data, such as downregulation of the expression of COL4A1, ITGA8, ITGB5, and TNC and upregulation of the expression of CXCL10, RNASEL, IRF3, IRF7, and IFIHI. This study provides a novel perspective for further investigations of the mechanism of the ECM in the BTV-host interactome and the pathogenesis of lung microvascular endothelial cells. [ABSTRACT FROM AUTHOR]

Nicotine, an addictive compound found in tobacco, functions as an agonist of nicotinic acetylcholine receptors (nAChRs) in the brain. Interestingly, nicotine has been reported to act as a cognitive enhancer in both human subjects and experimental animals. However, its effects in animal studies have not always been consistent, and sex differences have been identified in the effects of nicotine on several behaviors. Specifically, the role that sex plays in modulating the effects of nicotine on discrimination learning and cognitive flexibility in rodents is still unclear. Here, we evaluated sex‐dependent differences in the effect of daily nicotine intraperitoneal (i.p.) administration at various doses (0.125, 0.25, and 0.5 mg/kg) on visual discrimination (VD) learning and reversal (VDR) learning in mice. In male mice, 0.5 mg/kg nicotine significantly improved performance in the VDR, but not the VD, task, while 0.5 mg/kg nicotine significantly worsened performance in the VD, but not VDR task in female mice. Furthermore, 0.25 mg/kg nicotine significantly worsened performance in the VD and VDR task only in female mice. Next, to investigate the cellular mechanisms that underlie the sex difference in the effects of nicotine on cognition, transcriptomic analyses were performed focusing on the medial prefrontal cortex tissue samples from male and female mice that had received continuous administration of nicotine for 3 or 18 days. As a result of pathway enrichment analysis and protein–protein interaction analysis using gene sets of differentially expressed genes, decreased expression of postsynaptic‐related genes in males and increased expression of innate immunity‐related genes in females were identified as possible molecular mechanisms related to sex differences in the effects of nicotine on cognition in discrimination learning and cognitive flexibility. Our result suggests that nicotine modulates cognitive function in a sex‐dependent manner by alternating the expression of specific gene sets in the medial prefrontal cortex. [ABSTRACT FROM AUTHOR]

Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related. Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment. Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-β 25–35 (Aβ25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes. Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified. Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive motoneuron degenerative disorder. There are still no drugs capable of slowing disease evolution or improving life quality of ALS patients. Thus, autologous stem cell therapy has emerged as an alternative treatment regime to be investigated in clinical ALS. Method: Using Proteomics and Protein-Protein Interaction Network analyses combined with bioinformatics, the possible cellular mechanisms and molecular targets related to mesenchymal stem cells (MSCs, 1 × 106 cells/kg, intrathecally in the lumbar region of the spine) were investigated in cerebrospinal fluid (CSF) of ALS patients who received intrathecal infusions of autologous bone marrow-derived MSCs thirty days after cell therapy. Data are available via ProteomeXchange with identifier PXD053129. Results: Proteomics revealed 220 deregulated proteins in CSF of ALS subjects treated with MSCs compared to CSF collected from the same patients prior to MSCs infusion. Bioinformatics enriched analyses highlighted events of Extracellular matrix and Cell adhesion molecules as well as related key targets APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG in the CSF of cell treated ALS subjects. Conclusions: Extracellular matrix and cell adhesion molecules as well as their related highlighted components have emerged as key targets of autologous MSCs in CSF of ALS patients. Trial registration: Clinicaltrial.gov identifier NCT0291768. Registered 28 September 2016. [ABSTRACT FROM AUTHOR]

Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes. [ABSTRACT FROM AUTHOR]

Background: Although rheumatoid arthritis (RA) is a chronic systemic tissue disease often accompanied by osteoporosis (OP), the molecular mechanisms underlying this association remain unclear. This study aimed to elucidate the pathogenesis of RA and OP by identifying differentially expressed mRNAs (DEmRNAs) and long non-coding RNAs (lncRNAs) using a bioinformatics approach. Methods: Expression profiles of individuals diagnosed with OP and RA were retrieved from the Gene Expression Omnibus database. Differential expression analysis was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway enrichment analyses were performed to gain insights into the functional categories and molecular/biochemical pathways associated with DEmRNAs. We identified the intersection of common DEmRNAs and lncRNAs and constructed a protein-protein interaction (PPI) network. Correlation analysis between the common DEmRNAs and lncRNAs facilitated the construction of a coding-non-coding network. Lastly, serum peripheral blood mononuclear cells (PBMCs) from patients with RA and OP, as well as healthy controls, were obtained for TRAP staining and qRT-PCR to validate the findings obtained from the online dataset assessments. Results: A total of 28 DEmRNAs and 2 DElncRNAs were identified in individuals with both RA and OP. Chromosomal distribution analysis of the consensus DEmRNAs revealed that chromosome 1 had the highest number of differential expression genes. GO and KEGG analyses indicated that these DEmRNAs were primarily associated with " platelets (PLTs) degranulation", "platelet alpha granules", "platelet activation", "tight junctions" and "leukocyte transendothelial migration", with many genes functionally related to PLTs. In the PPI network, MT-ATP6 and PTGS1 emerged as potential hub genes, with MT-ATP6 originating from mitochondrial DNA. Co-expression analysis identified two key lncRNA-mRNA pairs: RP11 − 815J21.2 with MT − ATP6 and RP11 − 815J21.2 with PTGS1. Experimental validation confirmed significant differential expression of RP11-815J21.2, MT-ATP6 and PTGS1 between the healthy controls and the RA + OP groups. Notably, knockdown of RP11-815J21.2 attenuated TNF + IL-6-induced osteoclastogenesis. Conclusions: This study successfully identified shared dysregulated genes and potential therapeutic targets in individuals with RA and OP, highlighting their molecular similarities. These findings provide new insights into the pathogenesis of RA and OP and suggest potential avenues for further research and targeted therapies. [ABSTRACT FROM AUTHOR]

Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical diagnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets available in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two datasets. The identified DEGs were subjected to functional assessment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close association of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prognosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment. [ABSTRACT FROM AUTHOR]

With the rapid development of oil, energy, power and other industries, CO2 emissions rise sharply, which will cause a large amount of CO2 in the air be absorbed by the ocean and lead to ocean acidification. The growth and development of organisms can be seriously affected by acidified seawater. Sepia esculenta is a mollusk with high nutritional and economic value and is widely cultured in offshore waters of China. Larvae are the early life forms of the organism and are more vulnerable to changes in the external environment. Too low pH will lead to some adverse reactions in larvae, which will affect metabolism, immune response and other life activities. In this study, we sequenced the transcriptome of S. esculenta subjected to acidified seawater stress and identified 1072 differentially expressed genes (DEGs). The detected atypical expression of DEGs substantiates cellular malformation and translocation in S. esculenta under low pH stimulation. Simultaneously, this also substantiates the notable impact of ocean acidification on mollusks. These DEGs were used for functional enrichment analysis of GO and KEGG, and the top twenty items of the biological process classification in GO terms and 11 KEGG signaling pathways were significantly enriched. Finally, the constructed protein-protein interaction network (PPI) was used to analyze protein-protein interactions, and 12 key DEGs and 3 hub genes were identified. The reliability of 12 genes was verified by quantitative RT-PCR. A comprehensive analysis of the KEGG signaling pathway and PPI revealed that ocean acidification leads to abnormalities in lipid metabolism in S. esculenta larvae, which can lead to cancer development and metastasis, accompanied by some degree of inflammation. The results of the study will help to further investigate the physiological processes of S. esculenta when stimulated by ocean acidification, and provide a reference to cope with the captive breeding of S. esculenta affected by acidification. [ABSTRACT FROM AUTHOR]

Simple Summary: Androgenetic alopecia (AGA) represents the most common form of hair loss experienced by both men and women. Curcuma aeruginosa Roxb., a plant known for its medicinal properties, has shown promise in reversing this hair loss disorder for its hair growth effects and anti-androgenic effects. Despite its promising potential, the mechanism of action by which it acts remains unknown. As such, this study unveiled how this plant works against hair loss by identifying its bioactive compounds, the gene its targets, and the potential mechanism involved in the therapy of AGA using network pharmacology and molecular docking. The findings revealed insights into how C. aeruginosa can potentially prevent AGA, highlighting its potential for developing new, safe therapies for AGA, benefiting those affected by this condition. Androgenetic alopecia (AGA) is the most prevalent hair loss disorder worldwide, driven by excessive sensitivity or response to androgen. Herbal extracts, such as Curcuma aeruginosa Roxb., have shown promise in AGA treatment due to their anti-androgenic activities and hair growth effects. However, the precise mechanism of action remains unclear. Hence, this study aims to elucidate the active compounds, putative targets, and underlying mechanisms of C. aeruginosa for the therapy of AGA using network pharmacology and molecular docking. This study identified 66 bioactive compounds from C. aeruginosa, targeting 59 proteins associated with AGA. Eight hub genes were identified from the protein–protein interaction network, namely, CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3. Topological analysis of components–targets network revealed trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide as the core components with potential significance in AGA treatment. The molecular docking verified the binding affinity between the hub genes and core compounds. Moreover, the enrichment analyses showed that C. aeruginosa is involved in hormone response and participates in HIF-1 and MAPK pathways to treat AGA. Overall, this study contributes to understanding the potential anti-AGA mechanism of C. aeruginosa by highlighting its multi-component interactions with several targets involved in AGA pathogenesis. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer, which is one of the most common cancers with high mortality in women, has always been the focus of researchers, and every day, scientists are trying to identify mechanisms, genes, and medicines related to this disease. Nowadays, bioinformatics methods are used to identify and repurpose drugs for the treatment of diseases, especially cancer. Material & Methods: In this study, bioinformatics and biological network analysis were used to identify candidate drugs for breast cancer treatment. In this regard, analysis of the protein interaction network and drug-gene network were employed. The needed data were collected from the GEO database with the access code GSE54002. For the selected data set, genes with significant expression changes between two groups of healthy people and people with breast cancer cases were selected and considered primary genes. Thereafter, the protein-protein interaction network was constructed using the STRING database, and a significant gene module was obtained from the network. Following that, gene ontology studies and biological pathways were conducted. Next, the drug-gene network was constructed to identify drugs that target module genes and were introduced as essential drugs for the treatment of breast cancer. Cytoscape software and STRING and OncoDB databases were used to reconstruct and analyze the networks. Results: After analyzing the protein-protein interaction network and the drug-gene network, three important drugs that target the genes of the modules were identified and introduced as candidate drugs for the treatment of breast cancer. These drugs were RG1530, R-406, and GW441756x. Discussion & Conclusion: The obtained results demonstrated that the introduced drugs (RG-1530, R-406, and GW441756x) can be effective in the treatment of breast cancer. [ABSTRACT FROM AUTHOR]

ObjectiveAortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD.MethodsFour AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein–protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model.ResultsA total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice.ConclusionBy integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.