Identification Of Hub Genes Research Articles

Identification of immune-related hub genes and potential molecular mechanisms involved in COVID-19 via integrated bioinformatics analysis

COVID-19, caused by the SARS-CoV-2 virus, poses significant health challenges worldwide, particularly due to severe immune-related complications. Understanding the molecular mechanisms and identifying key immune-related genes (IRGs) involved in COVID-19 pathogenesis is critical for developing effective prevention and treatment strategies. This study employed computational tools to analyze biological data (bioinformatics) and a method for inferring causal relationships based on genetic variations, known as Mendelian randomization (MR), to explore the roles of IRGs in COVID-19. We identified differentially expressed genes (DEGs) from datasets available in the Gene Expression Omnibus (GEO), comparing COVID-19 patients with healthy controls. IRGs were sourced from the ImmPort database. We conducted functional enrichment analysis, pathway analysis, and immune infiltration assessments to determine the biological significance of the identified IRGs. A total of 360 common differential IRGs were identified. Among these genes, CD1C, IL1B, and SLP1 have emerged as key IRGs with potential protective effects against COVID-19. Pathway enrichment analysis revealed that CD1C is involved in terpenoid backbone biosynthesis and Th17 cell differentiation, while IL1B is linked to B-cell receptor signaling and the NF-kappa B signaling pathway. Significant correlations were observed between key genes and various immune cells, suggesting that they influence immune cell modulation in COVID-19. This study provides new insights into the immune mechanisms underlying COVID-19, highlighting the crucial role of IRGs in disease progression. These findings suggest that CD1C and IL1B could be potential therapeutic targets. The integrated bioinformatics and MR analysis approach offers a robust framework for further exploring immune responses in COVID-19 patients, as well as for targeted therapy and vaccine development.

Unraveling the role of PBK in glioblastoma: from molecular mechanisms to therapeutic targets.

This study investigates the gene expression characteristics of glioma-initiating cells (GIC), an important subgroup of glioblastoma (GBM), after knockdown of PBK (PDZ-binding kinase). Differentially expressed genes (DEGs) between PBK knockdown GIC and control groups were screened through bioinformatics methods. The authors analyzed the mechanisms and roles of these DEGs in GBM tumorigenesis and patient prognosis. Microarray data (GSE53800) were obtained from the Gene Expression Omnibus (GEO) database, selecting 18 GIC cell line samples with or without PBK knockdown. Each control and knockdown group contained three samples. DEGs were screened using R software. GO enrichment analysis, KEGG pathway analysis, PPI network analysis, and hub gene identification were conducted to explore DEG mechanisms. Western blot analysis was also performed to detect EIF4E protein expression, one of the key hub genes, after PBK knockdown in the HS683 glioma cell line. A total of 175 upregulated and 145 downregulated genes were identified. GO analysis showed that DEGs were mainly enriched in the positive regulation of cell proliferation, cell adhesion, and angiogenesis. KEGG pathway analysis revealed that DEGs were mainly involved in neuroactive ligand-receptor interactions, calcium signaling, and HIF-1 signaling pathways. Western blot results indicated that EIF4E was downregulated after PBK knockdown. A group of genes, such as EIF4E, were closely associated with PBK expression and functions. These findings may provide insight into the molecular mechanism of PBK in GBM.

Identification and Validation of Oxidative Stress-Related Hub Genes in Parkinson’s Disease

Identification and Validation of Oxidative Stress-Related Hub Genes in Parkinson’s Disease

The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm.

Increasingly, the shared risk factors and pathological processes of atherosclerosis and abdominal aortic aneurysm (AAA) are being recognized. The aim of our study was to identify the hub genes involved in the pathogenesis of atherosclerosis and AAA. The analysis was based on 2 gene expression profiles for atherosclerosis (GSE28829) and AAA (GSE7084), downloaded from the Gene Expression Omnibus database. Common differential genes were identified and an enrichment analysis of differential genes was conducted, with construction of protein-protein interaction networks, and identification of common hub genes, and predicted transcription factors. The analysis identified 133 differentially expressed genes (116 upregulated and 17 downregulated), with the enrichment analysis identifying a potential important role of integrins and chemokines in the common immune and inflammatory responses of atherosclerosis and AAA. Regulation of the complement and coagulation cascades and regulation of the actin cytoskeleton were associated with both diseases, with 10 important hub genes identified: TYROBP, PTPRC, integrin subunit beta 2, ITGAM, PLEK, cathepsin S, lymphocyte antigen 86, ITGAX, CCL4, and FCER1G. Findings identified a common pathogenetic pathway between atherosclerosis and AAA, with integrin-related genes playing a significant role. The common pathways and hub genes identified provide new insights into the shared mechanisms of these 2 diseases and can contribute to identifying new therapeutic targets and predicting the therapeutic effect of biological agents.

Abstract B026: Identification of small RNAs that function as multi-target therapeutics and as tools for novel target discovery in prostate cancer

Abstract Introduction: A major contributor to prostate cancer (PCa) mortality is the development of resistance to single agents targeting androgen receptor (AR) signaling. We have developed an unbiased shRNA-based negative selection screen to identify small RNAs (shRNAs and siRNAs) that inhibit PCa cell growth and survival (toxic sRNAs). Computational analyses of the most toxic sRNAs identified revealed that each can simultaneously target multiple known AR-coregulatory mRNAs. AR-coregulators modulate AR transcriptional response and are essential to AR signaling. We have proposed that the identified toxic sRNAs inhibit androgen signaling through seed-mediated targeting of androgen regulatory gene networks, resulting in inhibition of cell growth and viability. The goal of the current studies was to begin to analyze the broad therapeutic potential of these toxic sRNAs for PCa. We focused on analyzing i) their potential to thwart cell growth and/or viability when delivered to preclinical models of PCa, and ii) the targetome of the toxic sRNAs as a mean to uncover novel AR-coregulators that represent a targetable axis within androgen signaling. Methods: i) cationic lipid nanoparticles (LNPs), that efficiently deliver the toxic siRNAs to PCa cell lines, were used to deliver toxic siRNAs to organoids grown from PCa patient derived xenografts (PDX) models. 3D image analysis using the Ominer® software package was used to determine organoid count and size, the numbers of nuclei per organoid, and the fraction of dead cells to discriminate between cytotoxic and cytostatic effects. ii) to identify novel AR-coregulators, toxic siRNA direct targets were identified by sequencing of transcripts captured by biotinylated toxic siRNAs. Directly bound targets, were subjected to pathway enrichment and network analysis followed by identification of hub genes (Cytoscape). The effect of individually silencing potential AR-coregulators on the expression of androgen responsive genes was analyzed using Nanostring. Results and Conclusions: i) The effect of the toxic siRNAs on growth and viability of the PCa PDX-derived organoids will be presented and discussed as compelling preliminary data for therapeutic testing in more complex pre-clinical models (PCa PDXs). ii) Based on bioinformatic and downstream analysis of the direct targets of two toxic siRNAs, we selected 12 potentially novel AR- coregulators, with varied known functions including chromatin modifiers, transcriptional regulators, etc. Silencing of several of these potential AR-coregulators significantly affect expression of numerous AR responsive genes. Future experiments will focus on analyzing their interaction and functional association with the AR (i.e. effect of AR binding to chromatin). In conclusion, we have developed an assay that allowed the identification of toxic sRNAs for PCa cells and demonstrated their potential as therapeutics and as a tool for discovery of potential novel targets for PCa. Citation Format: Joshua M Corbin, Hamilton Young, Stefan Wilhelm, Adam S Asch, Chao Xu, Maria J Ruiz Echevarria. Identification of small RNAs that function as multi-target therapeutics and as tools for novel target discovery in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B026.

Effect of acute exercise on gene expression in peripheral blood mononuclear cells of puberty children

Studies have shown that acute exercise alters the expression of circulating neutrophil and peripheral blood mononuclear cell (PBMC) genes, affecting a wide range of processes from cellular growth to the promotion of inflammatory and anti-inflammatory responses. In this study, we identified differential gene expression associated with acute exercise and PBMC in puberty children through the GEO database. We utilized bioinformatics technology to analyze the impact of acute exercise on PBMC and identified key genes involving the puberty children. Combining two datasets of gene expression, we identified 197 shared differentially expressed genes (DEGs) in PBMC before and after exercise. We conducted pathway analysis of these DEGs and built a gene co-expression network. The identification of hub genes was achieved through the utilization of protein-protein interaction (PPI) networks. 268 shared DEGs were identified in the GSE11761 dataset in pre- and post-exercise groups, while 352 differential genes were identified between the pre- and post-exercise groups of females in the GSE14642 dataset. A total of 197 shared genes were identified between the two datasets. Seven core genes were identified by MCODE and CytoHubba, namely IL2RB, KLRD1, GZMB, TBX21, PRF1, HAVCR2, and GZMA. Bioinformatics techniques are effective in screening and analyzing PBMC-associated DEGs in children undergoing acute pre- and post-exercise puberty. Microarray analysis of PBMCs in children undergoing puberty can identify molecules that may predict acute exercise responses. This study provides insights for further exploration of the mechanisms and targets of acute exercise on the organism.

Prognostic significance and identification of m6A regulator genes and hub genes associated with m6A in breast cancer

This research endeavors to investigate the functions of N6-methyladenosine (m6A) regulatory genes and key genes linked to m6A modifications within the context of breast cancer (BC). The objective is to identify a promising predictive biomarker related to m6A modifications and validate its significance in BC through experimental methodologies. Utilizing data from The Cancer Genome Atlas (TCGA) database, a model for predicting prognosis was developed. Key genes connected to m6A modifications were discerned using weighted gene co-expression network analysis (WGCNA) coupled with LASSO and Cox regression analyses, which were then utilized to construct a predictive model. The influence of ZNF260 within BC was probed experimentally. The predictive model formulated using m6A regulatory genes and key m6A-associated genes demonstrated the capability to categorize BC patients into distinct risk groups effectively (all P < 0.001). Clinical sample analyses revealed notably elevated expression levels of ZNF260 in hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR + /HER2−) BC tissues compared to adjacent non-tumor tissues (all P < 0.001). Reduction in ZNF260 expression was shown to inhibit the proliferation, clonogenicity, migration, and invasiveness of MCF-7 cells while concomitantly enhancing apoptosis (all P < 0.001).This investigation uniquely uncovered ZNF260 as a novel key gene, suggesting its potential utility as a predictive biomarker associated with m6A modifications specifically in HR + /HER2− BC.

Identification of hub genes in the crosstalk between type 2 diabetic nephropathy and obesity according to bioinformatics analysis

ABSTRACT Diabetic nephropathy (DN) and obesity bring a huge burden to society. Obesity plays a crucial role in the progression of type 2 DN, but the pathophysiology remains unclear. Thus, we aimed the explore the association between type 2 DN and obesity using bioinformatics method. The gene expression profiles of type 2 DN (GSE96804) and obesity (GSE94752) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened with the thresholds defined as |log2FC| ≥1 and P<0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. Subsequently, a protein-protein interaction network was constructed based on the STRING database. Hub genes were identified, and the co-expression network was constructed. Finally, the hub genes were verified in clinical samples of 24 patients by immunohistochemistry. A total of 17 common DEGs were identified. Finally, two overlapping hub genes were identified (CCL18, C1QC). C1QC has been verified in clinical specimens. Using bioinformatics methods, the present study analyzed the common DEGs and the potential pathogenic mechanisms involved in type 2 DN and obesity. C1QC was the hub gene. Further studies are needed to clarify the specific relationships among C1QC, type 2 DN and obesity.

Identification of oxidative stress-related hub genes for predicting prognosis in diffuse large B-cell lymphoma

BackgroundOxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL). MethodsThe DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups. ResultsThrough website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability. ConclusionsThe key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.

Identification of immune-related hub genes in chronic obstructive pulmonary disease

ObjectiveAs a prevalent persistent respiratory disease, chronic obstructive pulmonary disease (COPD) is featured by airflow limitation and chronic inflammation. This study focused on the identification of immune-related hub genes in COPD. MethodsWe employed the GSE38974 dataset to analyze differentially expressed genes (DEGs) of COPD. Then, we obtained COPD immune-related DEGs (COPD-IMDEGs) based on the intersection of DEGs and immune-related genes. Subsequently, we carried out Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses on COPD-IMDEGs. We established a protein-protein interaction network based on COPD-IMDEGs. The hub genes were determined by utilizing the Maximal Clique Centrality method. We utilized receiver operating characteristic (ROC) curves to analyze the clinical significance of hub genes in COPD. In addition, potential drugs targeting hub genes were predicted based on interactions between hub gene-corresponding proteins and drugs. ResultsA total of 45 COPD-IMDEGs were obtained through differential analysis. Enrichment analyses showed that COPD-IMDEGs were associated with cytokines, growth factors, and receptor ligands. Ten COPD-IMDEGs were identified as hub genes. As shown by ROC curves, these genes had potential value in identifying COPD patients. Drug prediction results showed that simvastatin and other drugs targeted hub genes. ConclusionThis study analyzed the potential biological functions enriched by COPD-IMDEGs, identified ten genes as biological markers for diagnosing COPD, and predicted potential drugs for treating COPD.

Identification of Hub Genes and Immune Infiltration in Coronary Artery Disease: A Risk Prediction Model.

Our study aimed to establish a prediction model for coronary artery disease (CAD) that integrates immune infiltration and a gene expression signature. 613 differentially expressed genes (DEGs) and 12hub genes were screened via the GSE113079 dataset. The pathway enrichment analysis indicated that these genes (613 DEGs and 12hub genes) were closely associated with the inflammatory and immune responses. Based on the differentially expressed miRNA (DEmiRNA)-DEG regulatory network and immune cell infiltration, the Lasso algorithm constructed a CAD risk prediction model containing the risk score and immune score. Then, ROC-AUC and polymerase chain reaction (PCR) were performed for validation. Six hub genes (PTGER1, PIK3R1, ADRA2A, CORT, CXCL12, and S1PR5) had a high distinguishing capability (AUC > 0.90). In addition, the miRNAs targeting 12hub genes were predicted and intersected with the DEmiRNAs, and the DEmiRNA-DEG regulatory network was then constructed. Two LASSO models and a novel CAD risk prediction model were constructed through LASSO regression analysis, and they both accurately obtained the risk of CAD. The CAD risk prediction model shows good performance (AUC = 0.988). We also constructed a valid nomogram, and PCR results verified three downregulation hub genes and one upregulation gene in the CAD risk model. We demonstrated the molecular mechanism of the hub genes in CAD and provided a valuable tool for predicting the risk of CAD.

Identification of hub genes and pathways in Uterine corpus endometrial carcinoma (UCEC): A comprehensive in silico study

BackgroundUterine corpus endometrial carcinoma (UCEC), derived from the endometrium, is the most common type of endometrial malignasis. This gynecological malignancy is very common all over the world, especially in developed countries and shows a potentially rising trend correlated with the increase in obese women. MethodsDifferentially Expressed Genes (DEGs) analysis was conducted on GSE7305 and GSE25628 datasets from the Gene Expression Omnibus (GEO). DEGs were identified using GEO2R (adjusted p-value <0.05, |logFC| > 1). Pathway analysis employed KEGG and Gene Ontology databases, while protein-protein interactions were analyzed using Cytoscape and Gephi. GEPIA was used for target gene validation. ResultsWe have identified 304 common DEGs and 78 hub genes using GEO and PPI analysis, respectively. The GO and KEGG pathways analysis revealed enrichment of DEGs in extracellular matrix structural constituent, extracellular space, cell adhesion, and ECM-receptor interaction. GEPIA analysis identified three genes, ENG, GNG4, and ECT2, whose expression significantly differed between normal and tumor samples. ConclusionThis analysis study identified the hub genes and associated pathways involved in the pathogenesis of UCEC. The identified hub genes exhibit remarkable potential as diagnostic biomarkers, providing a significant opportunity for early diagnosis and more effective therapeutic approaches for UCEC.

HLA is a potent immunoinflammatory target in asymptomatic Alzheimer’s disease

Alzheimer’s disease (AD) is a common neurodegenerative disease, neuroinflammation is an early pathological feature of AD. However, the alteration of the immune microenvironment in asymptomatic AD was not fully explained. In this study, we aimed to utilize the transcriptome data of AD patients in public databases to reveal the change of immune microenvironment in asymptomatic AD and screen the potential anti-AD drug. Through a series of bioinformatics analyses, differentially expressed genes (DEGs) screening, enrichment analysis, PPI network construction, and hub gene identification were done. Meanwhile, the hub genes were validated in APP/PS-1(AD) mice. Importantly, seven enrichment pathways and eight hub genes associated with inflammation were identified in asymptomatic AD. Early AD patients presented infiltration of immunoinflammatory cells to varying degrees in the four representative brain regions. Correspondingly, more hub genes changed in the hippocampus in AD mice compared to the other four brain regions. Accompanied by the activation of microglia and astrocytes, the inflammation cytokines were increased in the hippocampus of AD mice. Moreover, HLA-C was correlated with the activation of microglia, HLA-DRB1 with IL-6 and OAS2 with TGF-β1 in the hippocampus. Five FDA-approved drugs (Itrazole, Dfo, Syrosingopine, Cefoperazone and Pradaxa) were predicted as the common drug-targeted HLA-C and HLA-DRB1 by molecular docking. Taken together, the changes in the immune microenvironment of asymptomatic AD, and provided a new perspective for the development of anti-inflammatory drugs for AD early treatment.

Hub genes identification for diagnosing Alzheimer's disease in patients with Crohn's disease

Hub genes identification for diagnosing Alzheimer's disease in patients with Crohn's disease

Identification of hub genes through integrated single-cell and microarray transcriptome analysis in osteoarthritic meniscus

BackgroundOsteoarthritis (OA) is marked by the progressive degradation of joint cartilage and subchondral bone. The precise molecular mechanisms driving meniscus deterioration in OA, especially at the single-cell level, remain poorly understood.MethodWe analyzed two datasets from the GEO database, GSE220243 and GSE98918, focusing on meniscus tissue sequencing data from OA and non-OA patients. The standard Seurat procedure was employed to process single-cell data and identify differentially expressed genes (DEGs). Immune cell infiltration was assessed using the Microenvironment Cell Populations (MCP) counter and CIBERSORT algorithms. For the microarray data, DEGs were identified with the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The overlapping DEGs from both datasets were imported into Cytoscape to generate protein-protein interaction (PPI) networks and identify hub genes. Transcription factor (TF) and miRNA interaction networks were analyzed using NetworkAnalyst, and gene-related predictive drugs were enriched through the DSigDB platform.ResultAfter quality control, 34,763 cells from the OA patients and 34,145 cells from the healthy controls were analyzed. UMAP identified and SingleR annotated 14 cell clusters. The 10 largest cell clusters were selected for further analysis. The OA group exhibited a notable increase in macrophages and a reduction in cytotoxic lymphocytes and endothelial cells in the meniscus. In GSE98918, 220 DEGs were identified, and the MCODE plug-in in Cytoscape pinpointed a key module containing 12 candidate genes. The MCC methodfiltered the top 20 DEGs in each GSE220243 cluster. Overlapping DEGs from GSE220243 and GSE98918 identified COL1A1, COL3A1, COL5A2, COL6A3, LOX, and VEGFA as significantly decreased in OA, with COL3A1, COL5A2, LOX, and VEGFA upregulated in meniscal chondrocytes. The interaction network highlighted 3 key miRNAs and 13 shared TFs. Ten gene-related predictive drug molecules were identified.ConclusionThis research highlights crucial genes in the OA meniscus and uncovers their differing regulatory patterns between chondrocytes and non-chondrocytes. These findings enhance our understanding of the molecular mechanisms driving OA pathogenesis and aid in identifying potential drug targets.

In-depth investigation of the complex pathophysiological mechanisms between diabetes and ischemic stroke through gene expression and regulatory network analysis

This study explores the intricate relationship between diabetes and ischemic stroke (IS) through gene expression analysis and regulatory network investigation to identify potential biomarkers and therapeutic targets. Using datasets from the Gene Expression Omnibus (GEO) database, differential gene analysis was conducted on GSE43950 (diabetes) and GSE16561 (IS), revealing overlapping differentially expressed genes (DEGs). Functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, and hub gene identification were performed, followed by validation in independent datasets (GSE156035 and GSE58294). The analysis identified 307 upregulated and 156 downregulated overlapping DEGs with significant enrichment in GO and KEGG pathways. Key hub genes (TLR2, TLR4, HDAC1, ITGAM) were identified through a PPI network (257 nodes, 456 interactions), with their roles in immune and inflammatory responses highlighted through GeneMANIA analysis. TRRUST-based transcription factor enrichment analysis revealed regulatory links involving RELA, SPI1, STAT3, and SP1. Differential expression analysis confirmed that RELA and SPI1 were upregulated in diabetes, while SPI1, STAT3, and SP1 were linked to IS. These transcription factors are involved in regulating immunity and inflammation, providing insights into the molecular mechanisms underlying diabetes-IS comorbidity. This bioinformatics-driven approach offers new understanding of the gene interactions and pathways involved, paving the way for potential therapeutic targets.

Identification of potential hub genes associated with recurrent miscarriage through combined transcriptomic and proteomic analysis.

Recurrent miscarriage (RM) is currently difficult to prevent and treat due to a lack of comprehensive understanding of its molecular mechanisms. The aim of this study was to identify genes potentially involved in the pathogenesis of RM and to observe their expression in the decidual tissues of RM patients. A total of 1823 differentially expressed genes (DEGs) and 148 differentially expressed proteins (DEPs) in decidual tissues between RM and control groups were identified. Subsequently, DCN, DPT, LUM, MFAP4, and ISG15 were identified from the DEGs/DEPs as RM-related hub genes through systematic bioinformatics analysis. Bioinformatics analysis of the single-cell dataset GSE214607 revealed that the expression of these five hub genes in the decidual stromal cells of RM patients appeared to be upregulated, while the RT-qPCR assay showed that their decidual expression levels were significantly increased in RM patients. Uterine Isg15expression was significantly increased, whereas the uterine expression of Dcn, Dpt, Lum, and Mfap4 was decreased in LPS-induced early pregnancy loss mice. MiR-16-5p, -21-3p, -27a-3p, and -941 were identified as potentially involved in the regulation of these five hub genes, and their decidual expression levels were significantly decreased in RM patients. The abnormally increased ISG15 expression in the decidual tissues of RM patients and uterine tissues of LPS-induced mice was validated by WB analysis. ISG15 expression was significantly reduced during the in vitro decidualization of human endometrial stromal cells (hESCs). Collectively, DCN, DPT, LUM, MFAP4, and ISG15 were identified as RM-related hub genes, and their expression in the decidual tissues of RM patients was significantly increased. The decidualization of hESCs was accompanied by reduced ISG15 expression, suggesting that increased decidual ISG15 expression might lead to early pregnancy loss by disrupting the decidualization process.

Gene expression profile analysis of severe influenza-based modulation of idiopathic pulmonary fibrosis

BackgroundIt is known severe influenza infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other. Till now, no associated mechanism has been reported.MethodWe collected the genetic pattern of expression of severe influenza (GSE111368) and IPF (GSE70866) from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (C-DEGs) were identified from the two datasets, and using this data, we conducted three forms of analyses, functional annotation, protein–protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis.ResultsIn all, 174 C-DEGs were selected for additional analyses. Based on our functional analysis, these C-DEGs mediated inflammatory response and cell differentiation. Furthermore, using cytoHubba, we identified 15 genes, namely, MELK, HJURP, BIRC5, TPX2, TK1, CDT1, UBE2C, UHRF1, CCNA2, TYMS, CDCA5, CDCA8, RAD54L, CCNB2, and ITGAM, which served as hub genes to possibly contribute to severe influenza patients with IPF disease as comorbidity. The hub gene expressions were further confirmed using two stand-alone datasets (GSE101702 for severe influenza and GSE10667 for IPF).ConclusionHerein, we demonstrated the significance of common pathways and critical genes in severe influenza and IPF etiologies. The identified pathways and genes may be employed as possible therapeutic targets for future therapy against severe influenza patients with IPF.

Identification and validation of matrix metalloproteinase hub genes as potential biomarkers for Skin Cutaneous Melanoma.

The role of matrix metalloproteinases (MMPs) in Skin Cutaneous Melanoma (SKCM) development and progression is unclear so far. This comprehensive study delved into the intricate role of MMPs in SKCM development and progression. RT-qPCR, bisulfite sequencing, and WES analyzed MMP gene expression, promoter methylation, and mutations in SKCM cell lines. TCGA datasets validated findings. DrugBank and molecular docking identified potential regulatory drugs, and cell line experiments confirmed the role of key MMP genes in tumorigenesis. Our findings unveiled significant up-regulation of MMP9, MMP12, MMP14, and MMP16, coupled with hypomethylation of their promoters in SKCM cell lines, implicating their involvement in disease progression. Mutational analysis highlighted a low frequency of mutations in these genes, indicating less involvement of mutations in the expression regulatory mechanisms. Prognostic assessments showcased a significant correlation between elevated expression of these genes and poor overall survival (OS) in SKCM patients. Additionally, functional experiments involving gene silencing revealed a potential impact on cellular proliferation, further emphasizing the significance of MMP9, MMP12, MMP14, and MMP16 in SKCM pathobiology. This study identifies Estradiol and Calcitriol as potential drugs for modulating MMP expression in SKCM, highlighting MMP9, MMP12, MMP14, and MMP16 as key diagnostic and prognostic biomarkers.

Bioinformatics analysis of immune infiltration in human diabetic retinopathy and identification of immune-related hub genes and their ceRNA networks

Diabetic retinopathy (DR) is the most common microvascular complication in diabetic patients, and recent studies have shown that immune regulatory mechanisms are closely associated with retinal damage in DR. Therefore, this study focused on exploring immune cells and immune-related genes (IRGs) in DR and gaining insight into the ceRNA mechanisms by which IRGs regulate DR progression. Four datasets from human DR model retinal tissues were obtained from the Gene Expression Omnibus (GEO) database. R software was first used to identify differentially expressed mRNAs (DE-mRNAs) in the dataset GSE160306-mRNAs, then the distribution of immune cells in the gene matrix was analyzed by xCell and ImmuCellAI, ImmPort and InnateDB database were used to obtain immune-related hub genes (IRHGs) in the DR, and finally the STRING online tool and Cytoscape to construct the immune-related ceRNA network. The datasets GSE102485, GSE160308 and GSE160306-lncRNAs were used to validate the results of the ceRNA network further. The results of immune cell infiltration analysis showed that macrophages are important immune cells in DR; immune-related gene screening showed that FCGR2B is an IRHG in DR, and 2 immune-related ceRNA networks of IRHG were obtained: DDN-AS1/miR-10a-5p/FCGR2B and LINC01515/miR-10a-5p/FCGR2B. Our study suggests that infiltration of immune cells, especially the immune role of macrophages, is an important component of DR progression; the immune-related hub gene FCGR2B and its ceRNA network may be a key regulatory network for DR progression. The discovery of key immune cells, IRHG and ceRNA networks in this study may provide new prospects for early intervention and targeted treatment of DR.