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The purpose of this study was to investigate biomarkers associated with poor clinical outcomes in colorectal cancer (CRC) by utilizing comprehensive datasets from the gene expression omnibus (GEO) databases GSE41258, GSE39582, and GSE44861. We initially identified differentially expressed genes (DEGs) and applied weighted gene co-expression network analysis (WGCNA) to the GSE41258 dataset to reveal key gene modules associated with CRC. Enrichment analyses were conducted to gain insights into the underlying biology of CRC, particularly focusing on pathways linked to the identified gene modules. Our analysis unveiled a distinct module strongly correlated with CRC carcinogenesis, with significant pathways related to extracellular matrix organization and vasculature development. Furthermore, we identified nine candidate genes (CDH11, COL1A1, COL1A2, COL5A1, COL5A2, FAP, SPARC, SULF1, and THY1) as potential crosstalk genes across various datasets. Notably, eight of these candidate genes exhibited a significant correlation with poor overall survival (OS) and recurrence-free survival (RFS) in CRC patients, suggesting their potential as prognostic biomarkers. Experimental validation using short hairpin RNA (shRNA)-mediated knockdown in HCT116 cells demonstrated that silencing of these candidate genes significantly impaired cancer cell proliferation, providing biological evidence supporting their functional roles in CRC progression. Our integrative approach offers a comprehensive understanding of the molecular landscape of CRC and identifies promising biomarkers for further exploration and validation. [ABSTRACT FROM AUTHOR]

Background: Hypertension is a critical public health issue worldwide. The identification of specific proteomic biomarkers in the Qatari population aims to advance personalized treatment strategies. Methods: We conducted proteomic profiling on 778 Qatari individuals using an aptamer-based SOMAscan platform to analyze 1,305 biomarkers. Statistical analysis involved two-way ANOVA and association analyses with FDR correction, alongside pathway and gene-set enrichment analyses using Reactome and DisGeNET databases. Results: The study identified 26 significant protein biomarkers associated with hypertension. Notably, QORL1 and BMP1 were identified as novel protein biomarkers. Enrichment analysis linked these biomarkers to critical pathways involved in vascular biology, immune system responses, and pathologies like arteriosclerosis and coronary artery disease. Correlation analyses highlighted robust interactions, particularly between QORL1 and various Apolipoprotein E isoforms, suggesting these biomarkers play pivotal roles in the molecular mechanisms underlying hypertension. Conclusions: This research enhances our understanding of the molecular basis of hypertension in the Qatari population and supports the development of precision medicine approaches for treatment. [ABSTRACT FROM AUTHOR]

Introduction: Endometriosis (EM) is a widely recognized disorder in gynecological endocrinology. Although hormonal therapies are frequently employed for EM, their side effects and outcome limitations underscore the need to explore the genetic basis and potential drug targets for developing innovative therapeutic approaches. This study aimed to identify both cerebrospinal fluid (CSF) and plasma protein markers as promising therapeutic targets for EM. Methods: We utilized Mendelian randomization (MR) analysis to explore potential disease-causing proteins, utilizing genetic datasets from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL) analyses. We applied a range of validation techniques, including reverse causality detection, phenotype scanning, Bayesian co-localization (BC) analysis, and external validations to substantiate our findings. Additionally, we conducted a protein-protein interaction (PPI) network as well as functional enrichment analyses to unveil potential associations among target proteins. Results: MR analysis revealed that a decrease of one standard deviation (SD) in plasma R-Spondin 3 (RSPO3) level had a protective effect on EM (OR = 1.0029; 95% confidence interval (95% CI): 1.0015–1.0043; P = 3.2567e-05; Bonferroni P < 5.63 × 10−5). BC analysis showed that RSPO3 shared the same genetic variant with EM (coloc.abf-PPH4 = 0.874). External validation further supported this causal association. Galectin-3 (LGALS3; OR = 0.9906; 95% CI: 0.9835–0.9977; P = 0.0101), carboxypeptidase E (CPE; OR = 1.0147; 95% CI: 1.0009–1.0287; P = 0.0366), and alpha-(1,3)-fucosyltransferase 5 (FUT5; OR = 1.0053; 95% CI: 1.0013–1.0093; P = 0.002) were detected as potential targets for EM in CSF. PPI analysis showed that fibronectin (FN1) had the highest combined score. Furthermore, several EM-linked proteins were involved in the glycan degradation pathway. Discussion: In conclusion, this comprehensive study offers valuable insights into potential drug targets for EM, with RSPO3 emerging as a promising candidate. Additionally, mechanistic roles of FN1, glycan degradation pathway, LGALS3, CPE, and FUT5 in EM warrant further investigation. [ABSTRACT FROM AUTHOR]

Long unique genomic regions have been reported to be highly enriched for developmental genes in mice and humans. In this paper, we identify unique genomic regions using an efficient method based on fast string matching. We quantify the resource consumption and accuracy of this method before applying it to the genomes of 18 mammals. We annotate their unique regions (URs) of at least 10 kb and find that they are strongly enriched for developmental genes across the board. We then investigated the subset of URs that lack annotations, which we call "anonymous." The longest anonymous UR in the Tasmanian devil spanned 83 kb and contained the gene encoding inositol polyphosphate-5-phosphatase A, which is an essential part of intracellular signaling. This discovery of an essential gene in a UR implies that URs might be given priority when annotating mammalian genomes. Our documented pipeline for annotating URs in any mammalian genome is available from the repository github.com/evolbioinf/auger ; the additional data for this study are available from the dataverse at doi.org/10.17617/3.4IKQAG. [ABSTRACT FROM AUTHOR]

Northern corn leaf blight (NCLB), caused by Setosphaeria turcica (S. turcica), is one of the devastating foliar diseases of maize (Zea mays) in maize-producing regions globally. Previous research has predominantly centered on elucidating the infection strategy and process of the pathogen, but the molecular mechanism of maize response to the pathogen is still largely unknown. In this study, we employed transcriptomics technology to comprehensively analyze alterations in RNA expression profiles within maize leaves at critical time points (hours post-infestation, 24 hpi, and 72 hpi) during S. turcica infection. Our study identified 7196 differentially expressed genes (DEGs) involved in the maize leaf response to S. turcica infection compared to the control (CK at 0 hpi). Functional analysis revealed that these DEGs were enriched in multiple metabolic pathways. Notably, genes associated with "benzoxazinone biosynthesis", "tetracyclic pyrrole biosynthesis", and "photosynthesis" were all down-regulated. In contrast, DEGs related to "phenol metabolism" and "phenylpropanoid metabolism" were significantly upregulated. Moreover, the genes belonging to the NAC, MYB-related, HB, and WRKY transcription factor families were also significantly enriched among the DEGs. The expression levels of six randomly selected DEGs were validated using qRT-PCR, confirming the accuracy of the RNA-Seq findings. This study delves into the functional genes and metabolic pathways closely associated with maize's response to S. turcica infection, providing foundational data for a deeper understanding of the molecular mechanisms underlying the interaction between S. turcica and maize. [ABSTRACT FROM AUTHOR]

Background: Research links arthropathies with adverse pregnancy outcomes. This study aims to explore its connection to postpartum hemorrhage (PPH) through Mendelian randomization (MR) analysis. Methods: The study used GWAS data from the IEU OpenGWAS database for PPH and arthropathies. After selecting instrumental variables, bidirectional MR analysis was conducted using MR-Egger, Weighted median, Simple mode, Weighted mode, and IVW methods. Sensitivity analysis was then performed to assess MR results reliability. Finally, enrichment analysis of genes corresponding to arthropathies SNPs in forward MR was conducted to explore their biological function and signaling pathways. Results: The forward MR results revealed that arthropathies was causally related to PPH, and arthropathies was a risk factor for PPH. Whereas, there was not a causal relationship between PPH and arthropathies by reverse MR analysis. It illustrated the reliability of the MR analysis results by the sensitivity analysis without heterogeneity, horizontal pleiotropy, and SNPs of severe bias by LOO analysis. Furthermore, a total of 33 genes corresponding to SNPs of arthropathies were obtained, which were mainly enriched in regulation of response to biotic stimulus, spliceosomal snRNP complex and ligase activity in GO terms, and natural killer cell-mediated cytotoxicity in KEGG pathways. Conclusion: This study supported that arthropathies was a risk factor for PPH, and the pathways involved the genes corresponding to SNPs were analyzed, which could provide important reference and evidence for further exploring the molecular mechanism between arthropathies and PPH. [ABSTRACT FROM AUTHOR]

Background: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets. Results: We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package's predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites. Conclusions: The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets. [ABSTRACT FROM AUTHOR]

Ziziphus jujuba is one of the oldest cultivated and economically important nut trees and its development and ripening processes involve numerous physiological and biochemical changes. This study conducted a fruit transcriptomic analysis during the fruit expanding (FE) stage, white-mature (WM) stage, early red (ER) stage and full red (FR) stage. The expression of mRNAs was then compared at four different stages. Subsequently, functional enrichment analysis was performed on the differentially expressed genes (DEGs) identified in each group. The relationships among DEGs within each group were assessed and hub genes were identified using the degree algorithm of Cytohubba. Finally, the expression levels of these hub genes were compared across the four stages. Based on the results, a total of 3448 unannotated novel genes were identified. The number of DEGs in the four group comparisons WM vs FE, ER vs WM, FR vs ER and FR vs FE groups were 1576, 8561, 1073 and 7884 DEGs, respectively, and mainly involved in biological processes such as stimulation, defence, immunity, ADP binding, DNA-binding transcription factor activity, secondary active transmembrane transporter activity, etc. In total, 20 hub genes were gained. The expression of 4 hub genes was not significantly different among four stages, namely LOC107409707, LOC107416546, LOC107415777 and LOC107414679, and the expression of the remaining hub genes was markedly different. Our study provides a transcriptional level reference to reveal further the dynamic developmental process of winter jujube fruits and a theoretical basis for improving the quality of winter jujube fruits. [ABSTRACT FROM AUTHOR]

Introduction: The gut microbiome (GM) has been implicated in cancer pathogenesis and treatment, including head and neck cancers (HNC). However, the specific microbial compositions influencing HNC and the underlying mechanisms remain largely unknown. Methods: This study utilized published genome-wide association studies (GWAS) summary data-based two-sample Mendelian randomization (MR) to uncover the GM compositions that exert significant causal effects on HNC. Functional annotation and enrichment analysis were conducted to better understand the significant genetic variables and their connection with HNC. The HNC dataset included 2,281 cases and 314,193 controls. The GM GWAS data of 211 gut taxa (35 families, 20 orders, 16 classes, 9 phyla, and 131 genera) were obtained from the MibioGen consortium, involving 18,340 participants. Results: MR analysis revealed four GM compositions exerting causal effects on HNC. Specifically, family Peptococcaceae.id.2024 was significantly associated with a 35% reduced risk of HNC (OR=0.65; 95%CI=0.48-0.90; P=0.0080). In contrast, genus DefluviitaleaceaeUCG-011.id.11287 (OR=1.54; 95%CI=1.13-2.09; P=0.0060), genus Gordonibacter.id.821 (OR=1.23; 95%CI=1.05-1.45; P=0.012), and genus Methanobrevibacter.id.123 (OR=1.28; 95%CI=1.01-1.62; P=0.040) showed a significant association with an increased risk of HNC. These GMs interact with genes and genetic variants involved in signaling pathways, such as GTPase regulation, influencing tumor progression and disease prognosis. Conclusions: Our study demonstrates, for the first time, the causal influence of specific gut microbiome compositions on HNC, offering significant insights for advancing clinical research and personalized treatments. The identified GMs may serve as potential biomarkers or therapeutic targets, paving the way for innovative approaches in HNC diagnosis, prevention, and therapy. [ABSTRACT FROM AUTHOR]

Bridging the gap between cortical morphometric remodeling and gene expression can help to clarify the effects of the selective brain accumulation of Amyloid- β (A β) and tau proteins occurring in the Alzheimer's disease (AD). To this aim, we derived morphometric similarity (MS) networks from 126 A β - and tau-positive (A β +/tau+) and 172 A β −/tau− subjects, and we investigated the association between group-wise regional MS differences and transcriptional correlates thanks to an imaging transcriptomics approach grounded in the Allen Human Brain Atlas (AHBA). The expressed gene with the highest correlation with MS alterations was BCHE, a gene related to A β homeostasis. In addition, notably, among the most promising results derived from the enrichment analysis, we found the immune response to be a biological process and astrocytes, microglia, and oligodendrocyte precursors for the cell types. In summary, by relating cortical MS and AHBA-derived transcriptomics, we were able to retrieve findings suggesting the biological mechanisms underlying the A β - and tau- induced cortical MS alterations in the AD continuum. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a critical neurological disorder characterized by loss of voluntary motor control and substantial slowing of movement. While traditionally attributed to environmental factors, recent studies underscore the significant role of genetics in the onset and progression of PD. This study aimed to identify differentially expressed genes (DEGs) and relevant pathways in PD by analyzing gene expression data from four datasets (83 PD and 53 control substantia nigra samples) sourced from the Gene Expression Omnibus (GEO) database. Using GEO2R, we identified common DEGs and performed functional annotation and KEGG pathway enrichment analysis through Enrichr. We constructed a protein-protein interaction (PPI) network using StringDB and identified hub genes via CytoHubba. Results revealed 18 critical DEGs enriched in pathways such as dopaminergic synapse and cocaine addiction. Key hub genes included Tyrosine Hydroxylase (TH), Solute Carrier Family 18 Member A2 (SLC18A2), and Potassium Inwardly Rectifying Channel Subfamily J Member 6 (KCNJ6). These findings provide insights into the molecular mechanisms of PD, highlighting potential biomarkers and therapeutic targets. This study offers a robust framework for future research and the development of effective treatment strategies for Parkinson's disease. [ABSTRACT FROM AUTHOR]

Background: Quantitative trait loci (QTL) are genomic regions that influence essential traits in livestock. Understanding QTL distribution and density across species' genomes is crucial for animal genetics research. Objectives: This study explored the QTLome of cattle, pigs, sheep, and chickens by analyzing QTL distribution and evaluating the correlation between QTL, gene density, and chromosome size with the aim to identify QTL-enriched genomic regions. Methods: Data from 211,715 QTL (1994–2021) were retrieved from the AnimalQTLdb and analyzed using R software v4.2.1. Unique QTL annotations were identified, and redundant or inconsistent data were removed. Statistical analyses included Pearson correlations and binomial, hypergeometric, and bootstrap-based enrichment tests. Results: QTL densities per Mbp were 10 for bovine, 4 for pig, 1 for sheep, and 3 for chicken genomes. Analysis of QTL distribution across chromosomes revealed uneven patterns, with certain regions enriched for QTL. Correlation analysis revealed a strong positive relationship between QTL and gene density/chromosome size across all species (p < 0.05). Enrichment analysis identified pleiotropic regions, where QTL affect multiple traits, often aligning with known candidate and major genes. Significant QTL-enriched windows (p < 0.05) were detected, with 699 (187), 355 (68), 50 (15), and 38 (17) genomic windows for cattle, pigs, sheep, and chickens, respectively, associated with overall traits (and specific phenotypic categories). Conclusions: This study provides critical insights into QTL distribution and its correlation with gene density, offering valuable data for advancing genetic research in livestock species. The identification of QTL-enriched regions also highlights key areas for future exploration in trait improvement programs. [ABSTRACT FROM AUTHOR]

Background and objective Lung cancer represents the main cause of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) is the most main subtype. More than half of NSCLC patients have already developed distant metastasis (DM) at the time of diagnosis and have a poor prognosis. Therefore, it is necessary to find new biomarkers for predicting NSCLC DM in order to guide subsequent treatment and thus improve the prognosis of NSCLC patients. Numerous studies have shown that microRNAs (miRNAs) are abnormally expressed in lung cancer tissues and play an important role in tumorigenesis and progression. The aim of this study is to identify differentially expressed miRNAs in lung adenocarcinoma tissues with DM group compared to those with non-distant metastasis (NDM) group, and to construct a miRNA signature for predicting DM of lung adenocarcinoma. Methods We first obtained miRNA and clinical data for patients with lung adenocarcinoma from The Cancer Genome Atlas (TCGA) database. Subsequently, bioinformatics analysis, which included different R packages, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, and a range of online analysis tools, was performed to analyze the data. Results A total of 12 differentially expressed miRNAs were identified between the DM and NDM groups, and 8 miRNAs (miR-377-5p, miR-381-5p, miR-490-5p, miR-519d-5p, miR-3136-5p, miR-320e, miR-2355-5p, miR-6784-5p) were screened for constructing a miRNA signature. The efficacy of this miRNA signature in predicting DM was good with an area under the curve (AUC) of 0.831. Logistic regression analysis showed that this miRNA signature was an independent risk factor for DM of lung adenocarcinoma. Next, target genes of the eight miRNAs were predicted, and enrichment analysis showed that these target genes were enriched in a variety of pathways, including pathways in cancer, herpes simplex virus I infection, PI3K-Akt pathway, MAPK pathway, Ras pathway, etc. Conclusion This miRNA signature has good efficacy in predicting DM of lung adenocarcinoma and has the potential to be a predictor of DM of lung adenocarcinoma.

Abstract Background The corticotropin-releasing hormone-binding protein (CRHBP) plays a crucial role in regulating corticotropin release. Little is known about the role of CRHBP, a major regulator of neuroendocrine, autonomic, and stress adaptation, in tumors. In this study, we aimed to investigate the clinical and molecular landscapes of CRHBP in various types of tumors. Methods We investigated the role of CRHBP in different types of tumors using publicly available databases and performed a comparative expression analysis of CRHBP-related genes in pan-cancer prognosis using methylation profiling, tumor-infiltrating immune cell expression analysis, gene enrichment analysis, and protein-protein interaction analysis, identified common pathways, and in vitro evaluation. Results We evaluated CRHBP expression across tumor and corresponding normal tissues using the data from The Cancer Genome Atlas and the Genotype-Tissue Expression database. CRHBP was downregulated in most tumors and was identified as an important factor for predicting the prognosis of patients with cancer. Intracellular metabolic pathways and hormone-related processes were involved in the functional mechanisms of CRHBP. Mechanistically, the downregulation of CRHBP was attributed to the upregulation of four miRNAs in most tumors, and CRHBP expression was related to tumor-infiltrating immune cells in tumors. Overexpression of CRHBP significantly inhibited cell proliferation of LUAD, LIHC, and KIRC cell lines, while inhibition of cell mobility was found only in KIRC and HCC cells. Conclusions This study provides a comprehensive summary of the systemic role of CRHBP expression in various types of tumors, highlighting the prognostic importance and clinical significance of tumors. Furthermore, CRHBP decreases cell proliferation and mobility in cancer cell lines associated with OS and DFS, further research is needed to understand the underlying mechanisms and explore clinical applications.

Abstract Background Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data. Results The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility. Conclusions Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

Abstract Aims The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. Methods In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). Results We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. Conclusions Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Abstract Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

Abstract Background Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood. Methods Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network. Results Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results. Conclusion This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future.

Background: The corticotropin-releasing hormone-binding protein (CRHBP) plays a crucial role in regulating corticotropin release. Little is known about the role of CRHBP, a major regulator of neuroendocrine, autonomic, and stress adaptation, in tumors. In this study, we aimed to investigate the clinical and molecular landscapes of CRHBP in various types of tumors. Methods: We investigated the role of CRHBP in different types of tumors using publicly available databases and performed a comparative expression analysis of CRHBP-related genes in pan-cancer prognosis using methylation profiling, tumor-infiltrating immune cell expression analysis, gene enrichment analysis, and protein-protein interaction analysis, identified common pathways, and in vitro evaluation. Results: We evaluated CRHBP expression across tumor and corresponding normal tissues using the data from The Cancer Genome Atlas and the Genotype-Tissue Expression database. CRHBP was downregulated in most tumors and was identified as an important factor for predicting the prognosis of patients with cancer. Intracellular metabolic pathways and hormone-related processes were involved in the functional mechanisms of CRHBP. Mechanistically, the downregulation of CRHBP was attributed to the upregulation of four miRNAs in most tumors, and CRHBP expression was related to tumor-infiltrating immune cells in tumors. Overexpression of CRHBP significantly inhibited cell proliferation of LUAD, LIHC, and KIRC cell lines, while inhibition of cell mobility was found only in KIRC and HCC cells. Conclusions: This study provides a comprehensive summary of the systemic role of CRHBP expression in various types of tumors, highlighting the prognostic importance and clinical significance of tumors. Furthermore, CRHBP decreases cell proliferation and mobility in cancer cell lines associated with OS and DFS, further research is needed to understand the underlying mechanisms and explore clinical applications. [ABSTRACT FROM AUTHOR]

Introduction: The preservation of locally endangered breeds is essential for maintaining ecosystem services that benefit both society and the environment. Reproductive fitness becomes a crucial consideration in this context. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a key role in post-transcriptional regulation. Typically, they function within the tissues where they are produced. However, when they are released into extracellular fluid, they are referred to as circulating miRNAs (c-miRNAs). C-miRNAs may serve as potential biomarkers, whose profile changes under different physiological states. The purpose of this study is to establish a connection between distinctive variations in the expression of c-miRNAs and specific estrus cycle phases in Frabosana-Roaschina sheep, an endangered Piedmontese breed. Methods: Two trials, each involving 20 ewes with different reproductive efficiencies (nulliparous in the first trial and pluriparous in the second trial), were sampled on alternate days after synchronization for blood, saliva, and feces. Ultrasound scans were performed during the induced estrus cycle. The animals' behaviors were assessed through video recordings. Results: In the first trial, play behaviors were detected without sexual behaviors, whereas in the second trial, sexual behaviors were observed without play behaviors. Based on plasma trends of 17β-estradiol and progesterone and ultrasound images, two moments were identified for miRNAs analyses: the beginning of the follicular phase (day 2) and the beginning of the luteal phase (day 11). C-miRNAs of six representative animals from the second trial were sequenced. Analyses of the sequencing data have identified 12 c-miRNAs that were differentially expressed (DE) when comparing day 11 with day 2: five miRNAs were found to be upregulated, whereas seven miRNAs were downregulated. An enrichment analysis, based on predicted targets, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases was performed. Many of these genes regulate reproductive pathways with the possible involvement of miRNAs. Finally, qRT-PCR was conducted to validate the DE miRNAs in all ewes. Differences in gene expression between the two sampling points and the two trials were observed, in line with existing literature. Discussion: Investigating the role of these miRNAs in regulating estrus could improve the reproductive performance and welfare of Frabosana-Roaschina ewes. [ABSTRACT FROM AUTHOR]

Background: Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data. Results: The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility. Conclusions: Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research. [ABSTRACT FROM AUTHOR]

Background: Previous studies have demonstrated widespread brain neurodegeneration in Alzheimer's disease (AD). However, the neurobiological and pathogenic substrates underlying this structural atrophy across the AD spectrum remain largely understood. Methods: In this study, we obtained structural MRI data from ADNI datasets, including 83 participants with early-stage cognitive impairments (EMCI), 83 with late-stage mild cognitive impairments (LMCI), 83 with AD, and 83 with normal controls (NC). Our goal was to explore structural atrophy across the full clinical AD spectrum and investigate the genetic mechanism using gene expression data from the Allen Human Brain Atlas. Results: As a result, we identified significant volume atrophy in the left thalamus, left cerebellum, and bilateral middle frontal gyrus across the AD spectrum. These structural changes were positively associated with the expression levels of genes such as ABCA7, SORCS1, SORL1, PILRA, PFDN1, PLXNA4, TRIP4, and CD2AP, while they were negatively associated with the expression levels of genes such as CD33, PLCG2, APOE, and ECHDC3 across the clinical AD spectrum. Further gene enrichment analyses revealed that the positively associated genes were mainly involved in the positive regulation of cellular protein localization and the negative regulation of cellular component organization, whereas the negatively associated genes were mainly involved in the positive regulation of iron transport. Conclusion: Overall, these results provide a deeper understanding of the biological mechanisms underlying structural changes in prodromal and clinical AD. [ABSTRACT FROM AUTHOR]

Background: Intervertebral disc degeneration (IDD) is a major cause for low back pain. Studies showed the association between senescence and degenerative diseases. Cell senescence can promote the occurrence and development of degenerative diseases through multiple mechanisms including inflammatory stress, oxidative stress and nutritional deprivation. The roles of senescence and senescence‐associated genes (SAGs) remains unknown in IDD. Methods: Four differently expressed SAGs were identified as hub SAGs using "limma" package in R. We then calculated the immune infiltration of IDD patients, and investigated the relation between hub SAGs and immune infiltration. Enrichment analysis was performed to explore the functions of hub SAGs in IDD. Nomogram and LASSO model based on hub SAGs was constructed to predict the risk of severe degeneration (SD) for IDD patients. Subsequently, single cell analysis was conducted to describe the expression pattern of hub SAGs in intervertebral disc tissue. Results: We identified ASPH, CCND1, IGFBP3 and SGK1 as hub SAGs. Further analysis demonstrated that the hub SAGs might mediate the development of IDD by regulating immune infiltration and multiple pathways. The LASSO model based on the four hub SAGs showed good performance in predicting the risk of SD. Single cell analysis revealed that ASPH, CCND1 and SGK1 mainly expressed in nucleus pulposus cells, while IGFBP3 mainly expressed in epithelial cells. Eleven candidate drugs targeting hub SAGS were predicted for IDD patients through Comparative Toxicogenomics Database (CDT). PCR and immunohistochemical analysis showed that the levels of four hub SAGs were higher in SD than MD (mild degeneration) patients. Conclusions: We performed a comprehensive analysis of SAGs in IDD, which revealed their functions and expression pattern in intervertebral disc tissue. Based on hub SAGs, we established a predictive model and explored the potential drugs. These findings provide new understandings of SAG mechanism and promising therapeutic strategies for IDD. [ABSTRACT FROM AUTHOR]

This study highlights the differences in the metabolomes of cow milk yoghurt (CY) and goat milk yoghurt (GY) using a nuclear magnetic resonance (NMR)-based metabolomic approach. The 1H HRMAS-NMR spectrum displayed 21 metabolites comprising organic acids, sugars, amino acids, amino acid derivatives and phospholipids. The orthogonal partial least squares discriminant analysis model clearly separated CY and GY groups, implying differences in metabolite composition. The corresponding Variable Importance in Projection (VIP) plot revealed that choline, sn-glycero-3-phosphocholine, O-phosphocholine, fucose, citrate, sucrose, glucose and lactose mainly contributed to the group separation (VIP > 1). Hierarchical cluster analysis further confirmed the metabolome similarities and differences between CY and GY. Additionally, 12 significantly differential metabolites (with a fold change > 1.5 and p-value < 0.05) were identified, with 1 downregulated and 11 upregulated. Pathway impact analysis revealed the correlation of significant metabolites with starch and sucrose metabolism, galactose metabolism, and the citrate cycle. Furthermore, receiver operating characteristic curve analysis identified eight metabolites (choline, sn-glycero-3-phosphocholine, fucose, O-phosphocholine, glucose, citrate, 2-oxoglutarate, lactose and sucrose) as candidate biomarkers. This study represents the first utilization of HRMAS-NMR to analyze the metabolomic profiles of yoghurt made from cow and goat milk. In conclusion, these findings provide preliminary information on how NMR-based metabolomics can discriminate the metabolomes of yoghurt prepared from the milk of two different animals, which may be valuable for authenticity and adulteration assessments. [ABSTRACT FROM AUTHOR]

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to find the lipid metabolism-associated biomarkers in geriatric patients with sepsis. Methodology: The gene expression profiles of specimens from geriatric patients with sepsis were retrieved from the Gene Expression Omnibus database. Differentially expressed genes were obtained via "limma" R package, and modules and genes highly associated with geriatric patients with sepsis were screened via "WGCNA" R package. The study also involved conducting enrichment analyses using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, as well as analyzing protein-protein interaction networks. The receiver operating characteristic curves were employed to determine the diagnostic values of hub genes. Results: A total of 73 differentially expressed lipid metabolism-related genes (DELRGs) were retained from the 1,317 differentially expressed genes, 8,335 module genes, and 1,045 lipid metabolism-related genes. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes results showed that DELRGs were mostly related to lipid metabolism. We identified ten hub genes from the protein-protein interaction network of DELRGs. The result of receiver operating characteristic validation indicated that seven hub genes (PPARG, ACSL1, IRS2, PLA2G4A, ALOX5, SPTLC1, and JAK2) worked as the biomarkers of geriatric patients with sepsis. The prognostic nomogram suggested that the set of seven hub genes can be utilized to evaluate the mortality risk. Conclusions: We screened seven lipid metabolism-related hub genes with diagnostic values. These molecules may exert a pivotal influence on the progression of sepsis in geriatric patients, as potential biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

Objective: The incidence of Hashimoto's thyroiditis (HT) in patients with metabolically unhealthy obesity (MUO) is generally higher than that in normal-weight individuals. However, the relationship among obesity, HT, and hypothyroidism remains unclear. Subjects and Methods: We searched the National Center for Biotechnology Information database and analyzed the abnormal expression of miRNAs in patients with MUO. The datasets GSE169290 and GSE138198 were selected as the objects of this data analysis. Using the MirPath tool on the DIANA TOOLS website, the KEGG pathway enrichment results were used for further analysis and explored the differential expression of pathways in patients with HT. Results: Four KEGG pathways were identified: "prion diseases (hsa05020)," "ECM-receptor interaction (hsa04512)," "mucin-type O-glycan biosynthesis (hsa00512)," and "cell adhesion molecules (hsa04514)." Sixteen differential genes were obtained, among which GALNT15 ranked the first, GALNT12 ranked the eighth, and GALNT8 ranked the 13th. GALNT15 , GALNT12 , and GALNT8 in the "mucin-type O-glycan biosynthesis" pathway are significantly lower in HT patients, which may be a key factor in the pathogenesis of HT. Conclusions: Decreased expression of O-GalNAc glycosylation in patients with MUO may increase the incidence of HT, which may become an important mechanism of HT in patients with obesity and is worthy of further exploration in future. [ABSTRACT FROM AUTHOR]

Objective: Asthma and gastroesophageal reflux disease (GERD) often occur simultaneously, with GERD being a comorbidity of asthma. This study aimed to explore the biological markers related to asthma and GERD by bioinformatics analysis. Methods: Initially, gene expression datasets for asthma and GERD were obtained from the Gene Expression Omnibus database, and subsequent differential expression analysis yielded 620 differentially expressed genes (DEGs) for asthma and 2367 DEGs for GERD. The intersection of these two gene sets yielded a total of 84 DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these genes may be involved in steroid hormone secretion and cellular stress response. Five hub genes (PTGDR2, CPA3, FCER1A, TPSAB1, and IL1RL1) were identified by a protein–protein interaction (PPI) network analysis and topological algorithm. Results: Enrichment analysis results indicated that hub genes may be involved in hormone secretion and disease development, particularly in regulating the renin–angiotensin system and systemic arterial blood pressure. PTGDR2, CPA3, TPSAB1, and IL1RL1 were upregulated in both asthma and GERD patient groups, while FCER1A was upregulated in asthma patients but downregulated in GERD patients. Through drug prediction, 22 drugs targeting hub genes PTGDR2, FCER1A, and TPSAB1 were identified. By constructing a transcription factor (TF)-target gene network, we found that eight TFs may regulate the expression of PTGDR2, FCER1A, and IL1RL1. Conclusion: Hence, Asthma and GERD were related to steroid hormone secretion and the renin–angiotensin system. [ABSTRACT FROM AUTHOR]

Understanding the impact of various culturing strategies on the secretome composition of adipose-derived stromal cells (ASC) enhances their therapeutic potential. This study investigated changes in the secretome of perirenal ASC (prASC) under different conditions: normoxia, cytokine exposure, high glucose, hypoxia, and hypoxia with high glucose. Using mass spectrometry and enrichment clustering analysis, we found that normoxia enriched pathways related to extracellular matrix (ECM) organization, platelet degranulation, and insulin-like growth factor (IGF) transport and uptake. Cytokine exposure influenced metabolism, vascular development, and protein processing pathways. High glucose affected the immune system, metabolic processes, and IGF transport and uptake. Hypoxia impacted immune and metabolic processes and protein processing. Combined hypoxia and high glucose influenced the immune system, IGF transport and uptake, and ECM organization. Our findings highlight the potential of manipulating culturing conditions to produce secretomes with distinct protein and functional profiles, tailoring therapeutic strategies accordingly. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. Methods: In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). Results: We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. Conclusions: Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers. [ABSTRACT FROM AUTHOR]

Objective: The present study was performed to identify key biomarkers associated with immune cell infiltration in peri‐implantitis through bioinformatic analyses. Methods: Six peri‐implantitis soft tissue samples and six healthy gingiva samples were obtained from GSE106090, and were used to identify immune‐associated differentially expressed genes (DEGs) in peri‐implantitis. The candidate biomarkers associated with immune cell infiltration were examined by immunohistochemical staining. Results: We identified 2089 upregulated and 2173 downregulated genes. Upregulated DEGs were significantly associated with immune response. Ten key candidate biomarkers were identified in the PPI network, including IL1B, TLR2, TLR4, CCL4, CXCL8, IL10, IL6, CD4, CCL3, and PTPRC. The expression level of the 10 genes increased in peri‐implantitis soft tissue samples compared with healthy gingiva samples. The proportion of CD4+ T cells, iTreg, and Tfh in infiltration immune cells increased in peri‐implantitis soft tissue samples and were positively correlated with the expression level of candidate biomarkers TLR4, CCL3, CXCL8, and IL1B. Immunohistochemistry showed that there were more lymphocytes in peri‐implantitis soft tissue samples, with an increased expression level of TLR4, CCL3, CXCL8, and IL1B. Conclusion: Identification of four novel diagnostic biomarkers was helpful for revealing the molecular mechanisms and could serve as a risk predictor for the immune microenvironment in peri‐implantitis. [ABSTRACT FROM AUTHOR]

In silico toxicogenomics methods are resource- and time-efficient approaches for inferring chemical–protein–disease associations with potential mechanism information for exploring toxicological effects. However, current in silico toxicogenomics systems make inferences based on only chemical–protein interactions without considering tissue-specific gene/protein expressions. As a result, inferred diseases could be overpredicted with false positives. In this work, six tissue-specific expression datasets of genes and proteins were collected from the Expression Atlas. Genes were then categorized into high, medium, and low expression levels in a tissue- and dataset-specific manner. Subsequently, the tissue-specific expression datasets were incorporated into the chemical–protein–disease inference process of our ChemDIS system by filtering out relatively low-expressed genes. By incorporating tissue-specific gene/protein expression data, the enrichment rate for chemical–disease inference was largely improved with up to 62.26% improvement. A case study of melamine showed the ability of the proposed method to identify more specific disease terms that are consistent with the literature. A user-friendly user interface was implemented in the ChemDIS system. The methodology is expected to be useful for chemical–disease inference and can be implemented for other in silico toxicogenomics tools. [ABSTRACT FROM AUTHOR]

Objective: To analyze the causal relationship between gut microbiota and gestational diabetes, and to clarify its mechanism. Methods: Two-sample Mendelian randomization (MR) analysis was conducted by using summary data from genome-wide association study (GWAS) for gut microbiota and gestational diabetes. The GWAS data of gut microbiota were obtained from a GWAS study from the MiBioGen consortium; the GWAS data on gestational diabetes were sourced from the FinnGen consortium’s publicly available R8 dataset; inverse variance weighted (IVW) method was used as the primary method to detect the causal association between the gut microbiota and the gestational diabetes. Sensitivity analysis was performed by Weighted Median and MR Egger methods; heterogeneity and pleiotropy were detected by Cochran’s Q, MR-PRESSO, Egger intercept tests and Leave-One-Out analysis; multivariable MR was used to adjust for the effect of body mass index (BMI); reverse MR was used to explore the presence of reverse causal associations; Gene Ontology (GO) fuctional and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling enrichment analyses were used to explore the potential pathways through which gut microbiota may have impact on gestational diabetes. Results: Four gut microbes were found to be causally associated with gestational diabetes: the genus Methanobrevibacter and the phylum Euryarchaeota displayed negative causal relationships with the risk of gestational diabetes, while the genus Olsenella and genus Lachnoclostridium exhibited positive causal associations. No significant heterogeneity or horizontal pleiotropy was detected in the analysis. The reverse MR analysis did not reveal any causal relationship. After adjusting for BMI, the multivariable MR analysis results showed there were the causal associations between the genus Olsenella and the phylum Euryarchaeota with the risk of gestational diabetes. The GO fuctional and KEGG signaling pathway enrichment analyses results showed that axon development, axon production, insulin secretion and other pathways were significantly enriched. Conclusion: There are causal associations between four gut microbes and gestational diabetes. Among them, the significant correlations with gestational diabetes are still observed in phylum Euryarchaeota and genus Olsenella after adjusting for BMI. [ABSTRACT FROM AUTHOR]

Background: Increased hemoglobin concentrations may increase the risk of varicose veins. However, the underlying relationship between them was not yet understood. Methods: Mendelian randomization (MR) analysis was performed to investigate causal effect between mean corpuscular hemoglobin concentration (MCHC, exposure factor) and varicose veins (outcome). Afterward, sensitivity analysis was used to ensure the reliability of MR analysis results. Then Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of SNPs were performed. A search tool for recurring instances of neighbouring genes (STRING) database was used to construct a protein-protein interaction (PPI) network. Results: Therefore, the inverse-variance weighted (IVW) results showed there existed a causal relationship between MCHC and varicose veins (p = 0.0026), with MCHC serving as a significant risk factor. (odd ratio [OR] = 1.2321). In addition, the validity of the results of the forward MR analysis was verified by sensitivity analysis. Further, a PPI network of 92 single-nucleotide polymorphisms (SNPs) which used for forward MR analysis related genes was constructed. And they were found to be closely associated with the peroxisome proliferator-activated receptor (PPAR) signalling pathway and cellular response to external stimulus by enrichment analysis. In addition, we clarified that the effect of varicose veins on MCHC was minimal by reverse MR analysis, suggesting that the results of forward MR analysis were not disturbed by reverse results. Conclusion: This study found a causal relationship between varicose veins and MCHC, which provided strong evidence for the effect of hemoglobin on varicose veins, and a new thought for the diagnosis and prevention of varicose veins in the future. [ABSTRACT FROM AUTHOR]

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