Differential Gene Co-expression Research Articles

Identification of Hub Genes in Liver Hepatocellular Carcinoma Based on Weighted Gene Co-expression Network Analysis.

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with high incidence and poor prognosis. To investigate the correlation between hub genes and progression of LIHC and to provided potential prognostic markers and therapy targets for LIHC. Our study mainly used The Cancer Genome Atlas (TCGA) LIHC database and the gene expression profiles of GSE54236 from the Gene Expression Omnibus (GEO) to explore the differential co-expression genes between LIHC and normal tissues. The differential co-expression genes were extracted by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. The Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to annotate the function of differential genes. Then the hub genes were validated using protein-protein interaction (PPI) network. And the expression level and prognostic analysis were performed. The probable associations between the expression of hub genes and both tumor purity and infiltration of immune cells were explored by TIMER. A total of 68 differential co-expression genes were extracted. These genes were mainly enriched in complement activation (biological process), collagen trimer (cellular component), carbohydrate binding and receptor ligand activity (molecular function) and cytokine - cytokine receptor interaction. Then we demonstrated that the 10 hub genes (CFP, CLEC1B, CLEC4G, CLEC4M, FCN2, FCN3, PAMR1 and TIMD4) were weakly expressed in LIHC tissues, the qRT-PCR results of clinical samples showed that six genes were significantly downregulated in LIHC patients compared with adjacent tissues. Worse overall survival (OS) and disease-free survival (DFS) in LIHC patients were associated with the lower expression of CFP, CLEC1B, FCN3 and TIMD4. Ten hub genes had positive association with tumor purity. CFP, CLEC1B, FCN3 and TIMD4 could serve as novel potential molecular targets for prognosis prediction in LIHC.

In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma

BackgroundLung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.MethodsWe used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.ResultsWe found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.ConclusionWe provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

Genome-Wide Identification and Expression Profiling of Potato (Solanum tuberosum L.) Universal Stress Proteins Reveal Essential Roles in Mechanical Damage and Deoxynivalenol Stress.

Universal stress proteins (USPs) play an important regulatory role in responses to abiotic stress. Most of the research related to USPs so far has been conducted on plant models such as Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa L.), and cotton (Gossypium hirsutum L.). The potato (Solanum tuberosum L.) is one of the four major food crops in the world. The potato is susceptible to mechanical damage and infection by pathogenic fungi during transport and storage. Deoxynivalenol (DON) released by Fusarium can seriously degrade the quality of potatoes. As a result, it is of great significance to study the expression pattern of the potato StUSP gene family under abiotic stress conditions. In this study, a total of 108 USP genes were identified from the genome of the Atlantic potato, divided into four subgroups. Based on their genetic structure, the physical and chemical properties of their proteins and other aspects of their biological characteristics are comprehensively analyzed. Collinear analysis showed that the homologous genes of StUSPs and four other representative species (Solanum lycopersicum, Arabidopsis, Oryza sativa L., and Nicotiana attenuata) were highly conserved. The cis-regulatory elements of the StUSPs promoter are involved in plant hormones, environmental stress, mechanical damage, and light response. RNA-seq analysis showed that there are differences in the expression patterns of members of each subgroup under different abiotic stresses. A Weighted Gene Coexpression Network Analysis (WGCNA) of the central gene showed that the differential coexpression gene is mainly involved in the plant-pathogen response process, plant hormone signal transduction, and the biosynthesis process of secondary metabolites. Through qRT-PCR analysis, it was confirmed that StUSP13, StUSP14, StUSP15, and StUSP41 may be important candidate genes involved in the response to adversity stress in potatoes. The results of this study provide a basis for further research on the functional analysis of StUSPs in the response of potatoes to adversity stress.

Transcriptional dissection of symptomatic profiles across the brain of men and women with depression

Major depressive disorder (MDD) is one of the most important causes of disability worldwide. While recent work provides insights into the molecular alterations in the brain of patients with MDD, whether these molecular signatures can be associated with the expression of specific symptom domains remains unclear. Here, we identified sex-specific gene modules associated with the expression of MDD, combining differential gene expression and co-expression network analyses in six cortical and subcortical brain regions. Our results show varying levels of network homology between males and females across brain regions, although the associations between these structures and the expression of MDD remain highly sex specific. We refined these associations to several symptom domains and identified transcriptional signatures associated with distinct functional pathways, including GABAergic and glutamatergic neurotransmission, metabolic processes and intracellular signal transduction, across brain regions associated with distinct symptomatic profiles in a sex-specific fashion. In most cases, these associations were specific to males or to females with MDD, although a subset of gene modules associated with common symptomatic features in both sexes were also identified. Together, our findings suggest that the expression of distinct MDD symptom domains associates with sex-specific transcriptional structures across brain regions.

M6A reading protein RBMX as a biomarker for prognosis and tumor progression in esophageal cancer.

As a member of m6A methylated binding protein, RNA binding motif protein X-linked (RBMX) has been reported to be associated with tumor invasion, metastasis and prognosis. However, the prognostic significance of RBMX expression in esophageal cancer (ESCA) remains unclear. Based on the TIMER database, GEPIA database, cBioPortal database, CIBERSORT deconvolution algorithm, String-DB database, LinkedOmics database, etc., the RBMX expression level, mRNA expression level, prognostic relationship, genetic mutation, immune cell infiltration level, protein interaction network, differential co-expression genes and functional enrichment in esophageal carcinoma were analyzed. Immunohistochemistry was used to detect the expression of RBMX in 53 cases of esophageal carcinoma and adjacent esophageal tissues. The RBMX expression in ESCA tissue was significantly higher than that in the normal tissues. The overall survival (OS) of patients with high RBMX expression was significantly lower than that of patients with low expression (P=0.04). The protein encoded by the RBMX gene appeared to copy number amplification, mutation and deep deletion. The expression level of RBMX was positively correlated with the levels of follicular helper T cells, eosinophils and initial B cells (P<0.05). Genes significantly and positively correlated with RBMX expression included HNRNPA1L2, SFRS13A, HNRNPA1, etc., which were mainly enriched in biological processes (BPs) such as cell division, mRNA splicing, RNA binding and mRNA 3'-UTR binding. RBMX may be as a biomarker of poor prognosis of ESCA. RBMX is closely related to the survival and prognosis, genetic mutation and immune cell infiltration of patients with ESCA.

Identification of stromal cell proportion-related genes in the breast cancer tumor microenvironment using CorDelSFS feature selection: implications for tumor progression and prognosis.

Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.

Domestication over Speciation in Allopolyploid Cotton Species: A Stronger Transcriptomic Pull.

Cotton has been domesticated independently four times for its fiber, but the genomic targets of selection during each domestication event are mostly unknown. Comparative analysis of the transcriptome during cotton fiber development in wild and cultivated materials holds promise for revealing how independent domestications led to the superficially similar modern cotton fiber phenotype in upland (G. hirsutum) and Pima (G. barbadense) cotton cultivars. Here we examined the fiber transcriptomes of both wild and domesticated G. hirsutum and G. barbadense to compare the effects of speciation versus domestication, performing differential gene expression analysis and coexpression network analysis at four developmental timepoints (5, 10, 15, or 20 days after flowering) spanning primary and secondary wall synthesis. These analyses revealed extensive differential expression between species, timepoints, domestication states, and particularly the intersection of domestication and species. Differential expression was higher when comparing domesticated accessions of the two species than between the wild, indicating that domestication had a greater impact on the transcriptome than speciation. Network analysis showed significant interspecific differences in coexpression network topology, module membership, and connectivity. Despite these differences, some modules or module functions were subject to parallel domestication in both species. Taken together, these results indicate that independent domestication led G. hirsutum and G. barbadense down unique pathways but that it also leveraged similar modules of coexpression to arrive at similar domesticated phenotypes.

Transcriptomics reveal the genetic coordination of early defense to Armillaria root rot (ARR) in Prunus spp.

Armillaria root rot (ARR) poses a significant threat to the long-term productivity of stone-fruit and nut crops in the predominant production area of the United States. To mitigate this issue, the development of ARR-resistant and horticulturally-acceptable rootstocks is a crucial step towards the maintenance of production sustainability. To date, genetic resistance to ARR has been found in exotic plum germplasm and a peach/plum hybrid rootstock, 'MP-29'. However, the widely-used peach rootstock Guardian® is susceptible to the pathogen. To understand the molecular defense mechanisms involved in ARR resistance in Prunus rootstocks, transcriptomic analyses of one susceptible and two resistant Prunus spp. were performed using two causal agents of ARR, including Armillaria mellea and Desarmillaria tabescens. The results of in vitro co-culture experiments revealed that the two resistant genotypes showed different temporal response dynamics and fungus-specific responses, as seen in the genetic response. Gene expression analysis over time indicated an enrichment of defense-related ontologies, including glucosyltransferase activity, monooxygenase activity, glutathione transferase activity, and peroxidase activity. Differential gene expression and co-expression network analysis highlighted key hub genes involved in the sensing and enzymatic degradation of chitin, GSTs, oxidoreductases, transcription factors, and biochemical pathways likely involved in Armillaria resistance. These data provide valuable resources for the improvement of ARR resistance in Prunus rootstocks through breeding.

Tissues and mechanisms associated with Verticillium wilt resistance in tomato using bi-grafted near-isogenic lines.

Host resistance is the primary means to control Verticillium dahliae, a soil-borne pathogen causing major losses on a broad range of plants, including tomato. The tissues and mechanisms responsible for resistance remain obscure. In the field, resistant tomato used as rootstocks does not confer resistance. Here, we created bi-grafted plants with near-isogenic lines (NILs) exhibiting (Ve1) or lacking (ve1) resistance to V. dahliae race 1. Ten days after inoculation, scion and rootstock tissues were subjected to differential gene expression and co-expression network analyses. Symptoms only developed in susceptible scions regardless of the rootstock. Infection caused more dramatic alteration of tomato gene expression in susceptible compared to resistant tissues, including pathogen receptor, signaling pathway, PR protein, and cell wall modification genes. Differences were observed between scions and rootstocks, primarily related to physiological processes in these tissues. Gene expression in scions was influenced by the rootstock genotype. A few genes were associated with the Ve1 genotype, which was independent of infection or tissue type. Several were physically clustered, some near the Ve1 locus on chromosome 9. Transcripts mapped to V. dahliae were dominated by secreted candidate effector proteins. These findings advance knowledge of molecular mechanisms underlying the tomato-V. dahliae interaction.

Host immune responses to enzootic and invasive pathogen lineages vary in magnitude, timing, and efficacy.

Infectious diseases of wildlife continue to pose a threat to biodiversity worldwide, yet pathogens are far from uniform in virulence or host disease outcome. Within the same pathogen species, virulence can vary considerably depending on strain or lineage, in turn eliciting variable host responses. One pathogen that has caused extensive biodiversity loss is the amphibian-killing fungus, Batrachochytrium dendrobatidis (Bd), which is comprised of a globally widespread hypervirulent lineage (Bd-GPL), and multiple geographically restricted, enzootic lineages. Whereas host immunogenomic responses to Bd-GPL have been characterized in a number of amphibian species, immunogenomic responses to geographically restricted, enzootic Bd lineages are less clear. To examine lineage-specific host immune responses to Bd, we exposed a species of pumpkin toadlet, Brachycephalus pitanga, which is endemic to Brazil's Southern Atlantic Forest, to either the Bd-GPL or the enzootic Bd-Asia-2/Brazil (hereafter Bd-Brazil) lineage. Using temporal samples from early, mid, and late infection stages, we quantified functional immunogenomic responses over the course of infection using differential gene expression tests and coexpression network analyses. Host immune responses varied significantly with Bd lineage. Relative to controls, toadlet responses to Bd-Brazil were weak at early infection (25 genes significantly differentially expressed), peaked by mid-stage infection (414 genes), and were nearly fully resolved by late-stage infection (nine genes). In contrast, responses to Bd-GPL were magnified and delayed; toadlets significantly differentially expressed 111 genes early, 87 genes at mid-stage infection, and 726 genes by late-stage infection relative to controls. Given that infection intensity did not vary between mid- and late-stage disease in either Bd-Brazil or Bd-GPL treatments, this suggests that pumpkin toadlets may be at least partially tolerant to the enzootic Bd-Brazil lineage. In contrast, late-stage immune activation against Bd-GPL was consistent with immune dysregulation previously observed in other species. Our results demonstrate that both the timing of immune response and the particular immune pathways activated are specific to Bd lineage. Within regions where multiple Bd lineages co-occur, and given continued global Bd movement, these differential host responses may influence not only individual disease outcome, but transmission dynamics at the population and community levels.

Bioinformatics-based diagnosis and evaluation of several pivotal genes and pathways associated with immune infiltration at different time points in spinal cord injury

ABSTRACT Spinal Cord Injury (SCI) is a devastating neurological event. To assess the degree of spinal cord damage and classify the injury, it is recommended to use the 2019 version of the AIS standard. The severity of trauma was evaluated using the Trauma Severity Score, and various classification systems have been proposed for injuries at different parts and segments of the spine. Understanding the regulated signaling pathways and immune processes following SCI can lead to a better understanding of SCI-induced biomarkers and their underlying mechanisms. In this study, two gene expression datasets (GSE464 and GSE45006) from the Gene Expression Omnibus database were utilized. Differential gene expression and co-expression network analysis were performed, revealing 370 shared genes in the 3-day group and 111 shared genes in the 14-day group after SCI. The study used functional enrichment analysis methods such as Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. The ssGSEA method was used to assess the levels and composition of immune infiltration in both the sham (control) and SCI groups. The single-cell transcriptomics dataset GSE182803 was analyzed to identify genes associated with immune marker cells. Four key genes (Ptgs2, Fn1, Ccl2, and Icam1) were identified in the 3-day group, while only one gene (Cyp51) was identified in the 14-day group after SCI. The findings offer significant insights into the immune-related genes and signaling pathways involved in secondary SCI at different time points and hold potential for the development of intervention strategies for acute and chronic post-SCI.

Identification of potential immune-related ceRNA Regulatory Network in UVB-irradiated human skin

ABSTRACT For a better understanding the molecular biomarkers in UVB-induced skin damage, and its potential mechanism, we downloaded two microarray data sets on skin UVB damage from the Gene Expression Omnibus (GEO): GSE21429, GSE56754. By using the Limma package to analyze differential gene expression and co-expression network analysis to screen module genes, 16 common genes were identified (16 up-regulated). Gene Ontology analysis to explore the functional roles of these genes indicated that the common genes were associated mainly with melanin biosynthetic process and metabolic process. Gene Set Enrichment Analysis provided evidence that the most gene sets enriched in immune and inflammation-related signaling pathways in the UVB-treated subjects, as compared with the untreated subjects. The PPI network genes were ranked according to the degree of connectivity, the top three ranked genes: ”MLANA”, ”GPR143” and ”SFTPC” were identified as potential biomarkers using the area under the receiver operating characteristic curve. The relative proportion of 22 immune cell types was then calculated by using the CIBERSORT algorithm. A higher follicular helper T cell ratio in UVB-treated samples compared to untreated samples was observed. Moreover, three hub genes have also been shown to be associated with immune cells. Finally, through multiple online miRNA databases, we propose MLANA-miR-573-MALAT1/NEAT1, GPR143-miR-138-5p-MALAT1/ KCNQ1OT1 might be potential RNA regulatory pathways that control disease progression in UVB-induced skin damage. In summary, the present results provide novel insights into the UVB-radiation related biological process changes, and further offer a new clinical application for prognosis and diagnostic prediction of UVB radiation-mediated skin damage.

WMDS.net: a network control framework for identifying key players in transcriptome programs.

Mammalian cells can be transcriptionally reprogramed to other cellular phenotypes. Controllability of such complex transitions in transcriptional networks underlying cellular phenotypes is an inherent biological characteristic. This network controllability can be interpreted by operating a few key regulators to guide the transcriptional program from one state to another. Finding the key regulators in the transcriptional program can provide key insights into the network state transition underlying cellular phenotypes. To address this challenge, here, we proposed to identify the key regulators in the transcriptional co-expression network as a minimum dominating set (MDS) of driver nodes that can fully control the network state transition. Based on the theory of structural controllability, we developed a weighted MDS network model (WMDS.net) to find the driver nodes of differential gene co-expression networks. The weight of WMDS.net integrates the degree of nodes in the network and the significance of gene co-expression difference between two physiological states into the measurement of node controllability of the transcriptional network. To confirm its validity, we applied WMDS.net to the discovery of cancer driver genes in RNA-seq datasets from The Cancer Genome Atlas. WMDS.net is powerful among various cancer datasets and outperformed the other top-tier tools with a better balance between precision and recall. https://github.com/chaofen123/WMDS.net. Supplementary data are available at Bioinformatics online.

Gene differential co-expression analysis of male infertility patients based on statistical and machine learning methods.

Male infertility has always been one of the important factors affecting the infertility of couples of gestational age. The reasons that affect male infertility includes living habits, hereditary factors, etc. Identifying the genetic causes of male infertility can help us understand the biology of male infertility, as well as the diagnosis of genetic testing and the determination of clinical treatment options. While current research has made significant progress in the genes that cause sperm defects in men, genetic studies of sperm content defects are still lacking. This article is based on a dataset of gene expression data on the X chromosome in patients with azoospermia, mild and severe oligospermia. Due to the difference in the degree of disease between patients and the possible difference in genetic causes, common classical clustering methods such as k-means, hierarchical clustering, etc. cannot effectively identify samples (realize simultaneous clustering of samples and features). In this paper, we use machine learning and various statistical methods such as hypergeometric distribution, Gibbs sampling, Fisher test, etc. and genes the interaction network for cluster analysis of gene expression data of male infertility patients has certain advantages compared with existing methods. The cluster results were identified by differential co-expression analysis of gene expression data in male infertility patients, and the model recognition clusters were analyzed by multiple gene enrichment methods, showing different degrees of enrichment in various enzyme activities, cancer, virus-related, ATP and ADP production, and other pathways. At the same time, as this paper is an unsupervised analysis of genetic factors of male infertility patients, we constructed a simulated data set, in which the clustering results have been determined, which can be used to measure the effect of discriminant model recognition. Through comparison, it finds that the proposed model has a better identification effect.

Integration of meta-analysis, machine learning and systems biology approach for investigating the transcriptomic response to drought stress in Populus species

In Populus, drought is a major problem affecting plant growth and development which can be closely reflected by corresponding transcriptomic changes. Nevertheless, how these changes in Populus are not fully understood. Here, we first used meta-analysis and machine learning methods to identify water stress-responsive genes and then performed a systematic approach to discover important gene networks. Our analysis revealed that large transcriptional variations occur during drought stress. These changes were more associated with the response to stress, cellular catabolic process, metabolic pathways, and hormone-related genes. The differential gene coexpression analysis highlighted two acetyltransferase NATA1-like and putative cytochrome P450 genes that have a special contribution in response to drought stress. In particular, the findings showed that MYBs and MAPKs have a prominent role in the drought stress response that could be considered to improve the drought tolerance of Populus. We also suggest ARF2-like and PYL4-like genes as potential markers for use in breeding programs. This study provides a better understanding of how Populus responses to drought that could be useful for improving tolerance to stress in Populus.

Comprehensive Analysis of PPMs in Pancreatic Adenocarcinoma Indicates the Value of PPM1K in the Tumor Microenvironment.

Early metastasis and resistance to traditional therapy are responsible for the poor prognosis of pancreatic adenocarcinoma patients. Metal-dependent protein phosphatases (PPMs) have been proven to play a crucial role in the initiation and progression of various tumors. Nevertheless, the expression and function of distinct PPMs in pancreatic adenocarcinoma have not been fully elucidated. In this study, we investigated the mRNA expression level, prognostic value, and the relationship between the expression of PPMs and the tumor microenvironment in pancreatic adenocarcinoma using Oncomine, TCGA and GTEx, GEO, Kaplan-Meier plotter, STRING, GeneMANIA, and HPA databases and R packages. GO and KEGG analysis revealed that PPMs and their differential co-expression genes are attributed to cell-cell adhesion and immune cell infiltration. Among these, PPM1K was downregulated in the tissue and peripheral blood of PAAD patients, whose expression level was negatively related to poor prognosis. Further to this, PPM1K was found to play a role in the epithelial-mesenchymal transition and immune infiltration. ROC curves showed that PPM1K had a good predictive value for pancreatic adenocarcinoma. The knockdown of PPM1K markedly promoted the proliferation and migration of pancreatic cancer cells, confirming its role in tumor suppressor activity in PAAD. This study demonstrates the potential clinical utility of PPM1K in tumor immunotherapy and brings about novel insights into the prognostic value of PPM1K in pancreatic adenocarcinoma.

Transcriptomic analysis of mid-secretory endometrium reveals essential immune factors associated with pregnancy after single euploid blastocyst transfer.

Implantation is a limiting factor for treatment success in assisted reproduction. Both embryonic and endometrial factors contribute to implantation. Embryonic factors have often been ignored in previous studies about the role of endometrium in implantation. In this study, we sought to identify the endometrial genes associated with negative pregnancy outcomes following the transfer of a single euploid blastocyst. Computational analyses of the transcriptomes of mid-secretory endometria from nine pregnant and seven non-pregnant patients in a cycle preceding the transfer of a single euploid blastocyst in a vitrified-warmed cycle were performed. Principal component analysis of two reported endometrial receptivity gene sets showed close clustering of the pregnant and non-pregnant samples. Differential gene expression analysis and co-expression module analysis identified 131 genes associated with the pregnancy status. The endometrial signatures identified highlight the importance of immune and metabolic regulation in pregnancy outcome. Network analysis identified 20 hub genes that could predict pregnancy outcomes with88.9% sensitivity and 85.7% specificity. Single-cell gene expression analysis highlighted the regulation of endometrial natural killer cells, T cells, and macrophages during embryo implantation. Immune cell abundance analysis supported the dysregulation of cytotoxic immune cells in the endometria of non-pregnant women. We reported the first endometrial gene signature associated with pregnancy after elimination of embryo aneuploidy and highlighted the importance of the endometrial immune microenvironment and metabolic status in pregnancy outcomes. This article is protected by copyright. All rights reserved.

Identification of PFKFB2 as a key gene for the transition from acute to old myocardial infarction in peripheral blood.

This study aims to analyze the gene expression profile of peripheral blood in different stages of myocardial infarction (MI) by transcriptome sequencing, and to study the gene expression characteristics of peripheral blood after MI. Differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify genes and modules associated with old myocardial infarction (OMI). Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation were applied to analyze the potential functions of genes. Hub genes were identified by Random Forest Classifier. CIBERSORT was used to provide an estimate of the abundance of 22 immune cells in peripheral blood. Quantitative polymerase chain reaction (qPCR) was used to detect gene expression levels in clinical samples. The cellular components (CC) of peripheral blood were counted by an automatic hematology analyzer. Through differential gene analysis and co-expression network analysis, 11 candidate genes were obtained. A random forest classifier identified 10 hub genes. Immune cell distribution of peripheral blood was found that T cell CD4 memory resting, NK cells resting, Dendritic cells activated, Mast cells resting, Monocytes and Neutrophils were correlated with OMI. Spearman correlation analysis found that PFKFB2 is related to the above immune cells. Low expression of PFKFB2 in peripheral blood of OMI was detected in clinical samples, and the relationship between PFKFB2 and peripheral blood immune cell counts was analyzed, which showed monocytes were associated with PFKFB2 in our study. PFKFB2 was low expressed in OMI, and related to the distribution of immune cells. PFKFB2 may play a key role in reflecting the transition from AMI to OMI, and predicting the distribution of immune cells, which provided a new perspective for improving myocardial fibrosis and adverse remodeling.

Exploring the pharmacological mechanism of Naoxueshu oral liquid in the treatment of intracerebral hemorrhage through weighted gene co-expression network analysis, network pharmacological and experimental validation

BackgroundIntracerebral hemorrhage (ICH) is a life-threatening stroke subtype with high rates of disability and mortality. Naoxueshu oral liquid is a proprietary Chinese medicine that absorbs hematoma and exhibits neuroprotective effects in patients with ICH. However, the underlying mechanisms remain obscure. PurposeExploring and elucidating the pharmacological mechanism of Naoxueshu oral liquid in the treatment of ICH. Study design and methodsThe Gene Expression Omnibus (GEO) database was used to download the gene expression data on ICH. ICH-related hub modules were obtained by weighted gene co-expression network analysis (WGCNA) of differentially co-expressed genes (DEGs). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the obtained key modules to identify the ICH-related signaling pathways. Network pharmacology technology was applied to forecast the targets of Naoxueshu oral liquid and to establish a protein-protein interaction (PPI) network of overlapping targets between Naoxueshu oral liquid and ICH. Functional annotation and enrichment pathway analyses of the intersectional targets were performed using the omicsbean database. Finally, we verified the therapeutic role and mechanism of Naoxueshu oral liquid in ICH through molecular docking and experiments. ResultsThrough the WGCNA analysis, combined with network pharmacology, it was found that immune inflammation was closely related to the early pathological mechanism of ICH. Naoxueshu oral liquid suppressed the inflammatory response; hence, it could be a potential drug for ICH treatment. Molecular docking further confirmed that the effective components of Naoxueshu oral liquid docked well with CD163. Finally, the experimental results showed that Naoxueshu oral liquid treatment in the ICH rat model attenuated neurological deficits and neuronal injury, decreased hematoma volume, and promoted hematoma absorption. In addition, Naoxueshu oral liquid treatment also significantly increased the levels of Arg-1, CD163, Nrf2, and HO-1 around hematoma after ICH. ConclusionThis study demonstrated that Naoxueshu oral liquid attenuated neurological deficits and accelerated hematoma absorption, possibly by suppressing inflammatory responses, which might be related to the regulation of Nrf2/CD163/HO-1 that interfered with the activation of M2 microglia, thus accelerating the clearance and decomposition of hemoglobin in the hematoma.

Differential gene co-expression network analyses reveal novel molecules associated with transcriptional dysregulation of key biological processes in osteoarthritis knee cartilage

ObjectivesTo compare co-expression networks of normal and osteoarthritis knee cartilage to uncover molecules associated with the transcriptional misregulation compromising biological processes (BPs) critical for cartilage homeostasis. DesignNormal and osteoarthritis human knee cartilage RNA-seq GSE114007 dataset was obtained from the Gene Expression Omnibus database. Partial Correlation and Information Theory (PCIT) algorithm was used to build co-expression networks containing all nodes connecting to at least one differentially expressed gene (DEG) in normal and osteoarthritis networks. Hub and hub centrality genes were used to perform functional enrichment analysis. Enriched BPs known to be associated with both healthy and diseased cartilage were compared in depth. ResultsDifferential co-expression network analyses allowed the identification of DDX43 and USP42 as exclusively co-expressed with DEGs in normal and osteoarthritis networks, respectively. The top hub and hub centrality genes of these networks were HIST1H3A and SNHG12 (normal) and TAF9B and OTUD1 (osteoarthritis). Enrichment analysis revealed several shared BPs between the contrasting groups, which are well-known in osteoarthritis pathogenesis. Protein-protein interaction network analysis for these BPs showed a global down-regulation of transcription factors in osteoarthritis. Specific transcription factors were identified as pleiotropic mediators in articular cartilage maintenance since they take part in several BPs. In addition, chromatin organisation and modification proteins were found relevant for osteoarthritis development. ConclusionDifferential gene co-expression analysis allowed the identification of novel and high priority therapeutic candidate genes that may drive modifications in the transcriptional “status” of cartilage in osteoarthritis.