Transcriptome analysis revealed the mechanism of skeletal muscle growth and development in different hybrid sheep.

Objective: To analyze the molecular mechanism of heterosis in East Friesian sheep × Hu sheep (EH) hybrid sheep and Suffolk × EH (SHE) hybrid sheep (Ovis aries). Methods: In this research, the growth performance data of Hu sheep (H), EH and SHE from birth to 8 months of age were analyzed. Three 8-month-old sheep of each of the three strains (9 sheep in total) were chosen and their longissimus dorsi muscles were collected for transcriptome sequencing. We verified the expression of seven differentially expressed genes (DEGs) by RT-qPCR. Results: The results showed: (1) body weight and chest circumference of EH were significantly greater than H (p<0.05), except at 4 months of age. Body weight and chest circumference of SHE was significantly higher than EH (p<0.05), except at 6 months of age. (2) 310 DEGs were screened in the EH and H, GO and KEGG showed DEGs were mainly concentrate on the categories of actin cytoskeleton, calcium binding, cGMP-PKG and MAPK signaling pathway, which correlating the development of skeletal muscle and energy metabolism. 329 DEGs were screened in the SHE and EH. DEGs were mainly enriched in ECM-receptor interactions and cell adhesion molecules. (3) PPI screening yielded five (MYL2, TNNI1, TNNI3, MYH11, TNNC1) and three (SOX10, COL2A1, MPZ) pivotal DEGs regulating muscle development in EH and SHE. (4) RT-qPCR test results were consistent with transcriptome sequencing. Conclusion: This study provides candidate genes for improving sheep growth traits. It provides a theoretical basis for analyzing the mechanism of muscle development in crossbred sheep.

Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning.

This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 24125) were obtained from the GEO database. Common differentially expressed genes (DEGs) in co-infection were identified by intersecting DEGs from monkeypox datasets with genes from key HIV modules screened using Weighted Gene Co-Expression Network Analysis (WGCNA). After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and construction of protein-protein interaction (PPI) network, candidate hub genes were further screened based on machine learning algorithms. Transcriptional factors (TFs) and miRNA-candidate hub gene networks were constructed to understand regulatory mechanisms and protein-drug interactions to identify potential therapeutic drugs. Seven candidate hub genes-MX2, ADAR, POLR2H, RPL5, IFI16, IFIT2, and RPS5-were identified. TFs and miRNAs associated with these hub genes, playing a key role in regulating viral infection and inflammation due to the activation of antiviral innate immunity, were also identified through network analysis. Potential therapeutic drugs were screened based on these hub genes: AZT, a nucleotide reverse transcriptase inhibitor, suppressed viral replication in HIV and monkeypox co-infection, while mefloquine inhibited inflammation due to the activation of antiviral innate immunity. In conclusion, the study identified candidate hub genes, their transcriptional regulation, signaling pathways, and small-molecule drugs in HIV and monkeypox co-infection, contributing to understanding the pathogenesis of HIV and monkeypox co-infection and informing precise therapeutic strategies.

Elucidating gastric cancer mechanisms and therapeutic potential of Adociaquinone A targeting EGFR: A genomic analysis and Computer Aided Drug Design (CADD) approach.

Gastric cancer predominantly adenocarcinoma, accounts for over 85% of gastric cancer diagnoses. Current therapeutic options are limited, necessitating the discovery of novel drug targets and effective treatments. The Affymetrix gene expression microarray dataset (GSE64951) was retrieved from NCBI-GEO data normalization and DEGs identification was done by using R-Bioconductor package. Gene Ontology (GO) analysis of DEGs was performed using DAVID. The protein-protein interaction network was constructed by STRING database plugin in Cytoscape. Subclusters/modules of important interacting genes in main network were extracted by using MCODE. The hub genes from in the network were identified by using Cytohubba. The miRNet tool built a hub gene/mRNA-miRNA network and Kaplan-Meier-Plotter conducted survival analysis. AutoDock Vina and GROMACS MD simulations were used for docking and stability analysis of marine compounds against the 5CNN protein. Total 734 DEGs (507 up-regulated and 228 down-regulated) were identified. Differentially expressed genes (DEGs) were enriched in processes like cell-cell adhesion and ATP binding. Eight hub genes (EGFR, HSPA90AA1, MAPK1, HSPA4, PPP2CA, CDKN2A, CDC20, and ATM) were selected for further analysis. A total of 23 miRNAs associated with hub genes were identified, with 12 of them targeting PPP2CA. EGFR displayed the highest expression and hazard rate in survival analyses. The kinase domain of EGFR (PDBID: 5CNN) was chosen as the drug target. Adociaquinone A from Petrosia alfiani, docked with 5CNN, showed the lowest binding energy with stable interactions across a 50 ns MD simulation, highlighting its potential as a lead molecule against EGFR. This study has identified crucial DEGs and hub genes in gastric cancer, proposing novel therapeutic targets. Specifically, Adociaquinone A demonstrates promising potential as a bioactive drug against EGFR in gastric cancer, warranting further investigation. The predicted miRNA against the hub gene/proteins can also be used as potential therapeutic targets.

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

BACKGROUND: It is known severe influenza infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other. Till now, no associated mechanism has been reported. METHOD: We 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. RESULTS: In 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). CONCLUSION: Herein, 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.

Exploring the hub Genes and Potential Mechanisms of Complement system-related Genes in Parkinson Disease: Based on Transcriptome Sequencing and Mendelian Randomization.

An accurate diagnosis of Parkinson's disease (PD) remains challenging and the exact cause of the disease is unclean. The aims are to identify hub genes associated with the complement system in PD and to explore their underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) and key module genes related to PD were mined through differential expression analysis and WGCNA. Then, differentially expressed CSRGs (DE-CSRGs) were obtained by intersecting the DEGs, key module genes and CSRGs. Subsequently, MR analysis was executed to identify genes causally associated with PD. Based on genes with significant MR results, the expression level and diagnostic performance verification were achieved to yield hub genes. Functional enrichment and immune infiltration analyses were accomplished to insight into the pathogenesis of PD. qRT-PCR was employed to evaluate the expression levels of hub genes. After MR analysis and related verification, CD93, CTSS, PRKCD and TLR2 were finally identified as hub genes. Enrichment analysis indicated that the main enriched pathways for hub genes. Immune infiltration analysis found that the hub genes showed significant correlation with a variety of immune cells (such as myeloid-derived suppressor cell and macrophage). In the qRT-PCR results, the expression levels of CTSS, PRKCD and TLR2 were consistent with those we obtained from public databases. Hence, we mined four hub genes associated with complement system in PD which provided novel perspectives for the diagnosis and treatment of PD.

Screening and identification of the hub genes in severe acute pancreatitis and sepsis.

Background: Severe acute pancreatitis (SAP) is accompanied with acute onset, rapid progression, and complicated condition. Sepsis is a common complication of SAP with a high mortality rate. This research aimed to identify the shared hub genes and key pathways of SAP and sepsis, and to explore their functions, molecular mechanism, and clinical value. Methods: We obtained SAP and sepsis datasets from the Gene Expression Omnibus (GEO) database and employed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify the shared differentially expressed genes (DEGs). Functional enrichment analysis and protein-protein interaction (PPI) was used on shared DEGs to reveal underlying mechanisms in SAP-associated sepsis. Machine learning methods including random forest (RF), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were adopted for screening hub genes. Then, receiver operating characteristic (ROC) curve and nomogram were applied to evaluate the diagnostic performance. Finally, immune cell infiltration analysis was conducted to go deeply into the immunological landscape of sepsis. Result: We obtained a total of 123 DEGs through cross analysis between Differential expression analysis and WGCNA important module. The Gene Ontology (GO) analysis uncovered the shared genes exhibited a significant enrichment in regulation of inflammatory response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the shared genes were primarily involved in immunoregulation by conducting NOD-like receptor (NLR) signaling pathway. Three machine learning results revealed that two overlapping genes (ARG1, HP) were identified as shared hub genes for SAP and sepsis. The immune infiltration results showed that immune cells played crucial part in the pathogenesis of sepsis and the two hub genes were substantially associated with immune cells, which may be a therapy target. Conclusion: ARG1 and HP may affect SAP and sepsis by regulating inflammation and immune responses, shedding light on potential future diagnostic and therapeutic approaches for SAP-associated sepsis.

Identification and validation of hub differential genes in pulmonary sarcoidosis.

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

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer.

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients' disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Identification and evaluation of candidate COVID-19 critical genes and medicinal drugs related to plasma cells.

The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents one of the most significant global health crises in recent history. Despite extensive research into the immune mechanisms and therapeutic options for COVID-19, there remains a paucity of studies focusing on plasma cells. In this study, we utilized the DESeq2 package to identify differentially expressed genes (DEGs) between COVID-19 patients and controls using datasets GSE157103 and GSE152641. We employed the xCell algorithm to perform immune infiltration analyses, revealing notably elevated levels of plasma cells in COVID-19 patients compared to healthy individuals. Subsequently, we applied the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify COVID-19 related plasma cell module genes. Further, positive cluster biomarker genes for plasma cells were extracted from single-cell RNA sequencing data (GSE171524), leading to the identification of 122 shared genes implicated in critical biological processes such as cell cycle regulation and viral infection pathways. We constructed a robust protein-protein interaction (PPI) network comprising 89 genes using Cytoscape, and identified 20 hub genes through cytoHubba. These genes were validated in external datasets (GSE152418 and GSE179627). Additionally, we identified three potential small molecules (GSK-1070916, BRD-K89997465, and idarubicin) that target key hub genes in the network, suggesting a novel therapeutic approach. These compounds were characterized by their ability to down-regulate AURKB, KIF11, and TOP2A effectively, as evidenced by their low free binding energies determined through computational analyses using cMAP and AutoDock. This study marks the first comprehensive exploration of plasma cells' role in COVID-19, offering new insights and potential therapeutic targets. It underscores the importance of a systematic approach to understanding and treating COVID-19, expanding the current body of knowledge and providing a foundation for future research.

Identification of novel hub genes and immune infiltration in atopic dermatitis using integrated bioinformatics analysis.

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

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes.

BACKGROUND: The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis (PBC) and finding relevant biomarkers for diagnosis and therapeutic evaluation. AIM: To determine PBC-associated hub genes and assess their clinical utility for disease prediction. METHODS: PBC expression data were obtained from the Gene Expression Omnibus database. Overlapping genes from differential expression analysis and weighted gene co-expression network analysis (WGCNA) were identified as key genes for PBC. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes. Hub genes were identified in protein-protein interaction (PPI) networks using the Degree algorithm in Cytoscape software. The relationship between hub genes and immune cells was investigated. Finally, a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC. RESULTS: We identified 71 overlapping key genes using differential expression analysis and WGCNA. These genes were primarily enriched in pathways related to cytokine-cytokine receptor interaction, and Th1, Th2, and Th17 cell differentiation. We utilized Cytoscape software and identified five hub genes (CD247, IL10, CCL5, CCL3, and STAT3) in PPI networks. These hub genes showed a strong correlation with immune cell infiltration in PBC. However, inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk. CONCLUSION: Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment, thereby offering significant clinical utility.

Identification of driving genes of recurrent miscarriage based on transcriptome sequencing and immunoinfiltration analysis.

AIMS: Recurrent miscarriage (RM) plagues 1 %-5 % women of childbearing age. Facing the limitations of clinical treatment, its pathological mechanism remains to be clarified. METHODS: Decidual tissues of three induced abortions and three RM were collected for transcriptome sequencing. The pathological features of RM were identified by differential expression genes (DEGs) analysis, GSEA, GO and KEGG analysis, and a protein-protein interaction network was constructed for DEGs, and six algorithms were used to identify hub genes. In addition, the immune characteristics of RM patients were identified by CIBERSORT, and the correlation between them and hub genes was analyzed. Furthermore, in single-cell level, different cells were grouped according to the expression level of hub genes, and the expression ratio and abundance of hub genes in different cells and their regulation on cell function were explored. RESULTS: Transcriptome sequencing of patients with RM showed that a large number of genes were down-regulated, which was related to fibroblast proliferation, epithelial cell migration, female pregnancy and cell chemotaxis. Fifteen hub genes were identified by constructing a protein-protein interaction network, among which DUSP1, NR4A1 and THBS1 were involved in cell migration and chemotaxis. Immune cell infiltration analysis showed that the infiltration of T cells, macrophages and NK cells was abnormal, and there was a significant correlation with hub genes. Moreover, we found that compared with the expression of DUSP1, the non-expression of DUSP1 will reduce the extracellular matrix formation of fibroblasts and the chemotaxis of macrophages. At the same time, it is worth noting that the expression ratio and abundance of hub genes are decreased in epithelial cells, fibroblasts, macrophages and NK cells. Furthermore, single-cell analysis and in vitro and in vivo experiments show that DUSP1 and NR4A1 are low-expressed in different cells of RM patients, which is accompanied by the inhibition of fibroblast proliferation and macrophage chemotaxis. Drug prediction and screening based on hub genes show that Cinobufagin and calmidazolium are expected to be candidate drugs for RM. CONCLUSION: Hub genes such as DUSP1, NR4A1 and THBS1 participate in RM by regulating epithelial cell migration, fibroblast proliferation and macrophage chemotaxis, which will provide new insight for the diagnosis and targeted therapy of RM.

Genes related to microglia polarization and immune infiltration in Alzheimer's Disease.

Alzheimer's Disease (AD) remains a significant challenge due to its complex etiology and socio-economic burden. In this study, we investigated the roles of macrophage polarization-related hub genes in AD pathology, focusing on their impact on immune infiltration and gene regulation in distinct brain regions. Using Gene Expression Omnibus (GEO) datasets GSE110226 (choroid plexus) and GSE1297 (hippocampal CA1), we identified key genes-EDN1, HHLA2, KL, TREM2, and WWTR1-associated with AD mechanisms and immune responses. Based on these findings, we developed a diagnostic model demonstrating favorable calibration and clinical applicability. Furthermore, we explored molecular interactions within mRNA-transcription factor and mRNA-miRNA regulatory networks, providing deeper insights into AD progression and identifying potential therapeutic targets. The novel identification of WWTR1 and HHLA2 as biomarkers expands the diagnostic toolkit for AD, offering new perspectives on the disease's underlying immune dynamics. However, external dataset validation and further in vitro and in vivo studies are required to confirm these results and their clinical relevance.

Identification of Potential Hub Genes in Alopecia Areata.

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

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis.

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Exploration of the link between COVID-19 and gastric cancer from the perspective of bioinformatics and systems biology.

Background: Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has caused a global pandemic. Gastric cancer (GC) poses a great threat to people's health, which is a high-risk factor for COVID-19. Previous studies have found some associations between GC and COVID-19, whereas the underlying molecular mechanisms are not well understood. Methods: We employed bioinformatics and systems biology to explore these links between GC and COVID-19. Gene expression profiles of COVID-19 (GSE196822) and GC (GSE179252) were obtained from the Gene Expression Omnibus (GEO) database. After identifying the shared differentially expressed genes (DEGs) for GC and COVID-19, functional annotation, protein-protein interaction (PPI) network, hub genes, transcriptional regulatory networks and candidate drugs were analyzed. Results: We identified 209 shared DEGs between COVID-19 and GC. Functional analyses highlighted immune-related pathways as key players in both diseases. Ten hub genes (CDK1, KIF20A, TPX2, UBE2C, HJURP, CENPA, PLK1, MKI67, IFI6, IFIT2) were identified. The transcription factor/gene and miRNA/gene interaction networks identified 38 transcription factors (TFs) and 234 miRNAs. More importantly, we identified ten potential therapeutic agents, including ciclopirox, resveratrol, etoposide, methotrexate, trifluridine, enterolactone, troglitazone, calcitriol, dasatinib and deferoxamine, some of which have been reported to improve and treat GC and COVID-19. Conclusion: This research offer valuable insights into the molecular interplay between COVID-19 and GC, potentially guiding future therapeutic strategies.

The Effect of Vitamin D Deficiency on Immune-Related Hub Genes: A Network Analysis Associated With Type 1 Diabetes.

Background Type 1 diabetes (T1D) is an autoimmune disorder that results in the destruction of pancreatic beta cells, causing a shortage of insulin secretion. The development of T1D is influenced by both genetic predisposition and environmental factors, such as vitamin D. This vitamin is known for its ability to regulate the immune system and has been associated with a decreased risk of T1D. However, the specific ways in which vitamin D affects immune regulation and the preservation of beta cells in T1D are not yet fully understood. Gaining a better understanding of these interactions is essential for identifying potential targets for preventing and treating T1D. Methods The analysis focused on two Gene Expression Omnibus (GEO) datasets, namely, GSE55098 and GSE50012, to detect differentially expressed genes (DEGs). Enrichr (Ma'ayan Laboratory, New York, NY) was used to perform enrichment analysis for the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The Search Tool for the Retrieval of Interacting Genes 12.0 (STRING) database was used to generate a protein-protein interaction (PPI) network. The Cytoscape 3.10.1 (Cytoscape Team, San Diego, CA) was used to analyze the PPI network and discover the hub genes. Results The DEGs in both datasets were identified using the GEO2R tool, with a particular focus on genes exhibiting contrasting regulations. Enrichment analysis unveiled the participation of these oppositely regulated DEGs in processes relevant to the immune system. Cytoscape analysis of the PPI network revealed five hub genes, MNDA, LILRB2, FPR2, HCK, and FCGR2A, suggesting their potential role in the pathogenesis of T1D and the response to vitamin D. Conclusion The study elucidates the complex interaction between vitamin D metabolism and immune regulation in T1D. The identified hub genes provide important knowledge on the molecular pathways that underlie T1D and have the potential to be targeted for therapeutic intervention. This research underscores the importance of vitamin D in the immune system's modulation and its impact on T1D development.

Gene expression analysis identifies hub genes and pathways distinguishing fatal from survivor outcomes of Ebola virus disease.

The Ebola virus poses a severe public health threat, yet understanding factors influencing disease outcomes remains incomplete. Our study aimed to identify critical pathways and hub genes associated with fatal and survivor Ebola disease outcomes. We analyzed differentially expressed hub genes (DEGs) between groups with fatal and survival outcomes, as well as a healthy control group. We conducted additional analysis to determine the functions and pathways associated with these DEGs. We found 13,198 DEGs in the fatal and 12,039 DEGs in the survival group compared to healthy controls, and 1873 DEGs in the acute fatal and survivor groups comparison. Upregulated DEGs in the comparison between the acute fatal and survivor groups were linked to ECM receptor interaction, complement and coagulation cascades, and PI3K-Akt signaling. Upregulated hub genes identified from the acute fatal and survivor comparison (FGB, C1QA, SERPINF2, PLAT, C9, SERPINE1, F3, VWF) were enriched in complement and coagulation cascades; the downregulated hub genes (IL1B, 1L17RE, XCL1, CXCL6, CCL4, CD8A, CD8B, CD3D) were associated with immune cell processes. Hub genes CCL2 and F2 were unique to fatal outcomes, while CXCL1, HIST1H4F, and IL1A were upregulated hub genes unique to survival outcomes compared to healthy controls. Our results demonstrate for the first time the association of EVD outcomes to specific hub genes and their associated pathways and biological processes. The identified hub genes and pathways could help better elucidate Ebola disease pathogenesis and contribute to the development of targeted interventions and personalized treatment for distinct EVD outcomes.

Integrated Analysis of Multi-Omic Data Reveals Regulatory Mechanisms and Network Characteristics in Breast Cancer.

Background: Breast cancer is a complex and heterogeneous disease, and understanding its regulatory mechanisms and network characteristics is essential for identifying therapeutic targets and developing effective treatment strategies. This study aimed to unravel the intricate network of interactions involving differentially expressed genes, microribonucleic acid (miRNAs), and proteins in breast cancer through an integrative analysis of multi-omic data from Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset. Methods: The TCGA-BRCA dataset was used for data acquisition, which included RNA sequencing data for gene expression, miRNA sequencing data for miRNA expression, and protein expression quantification data. Various R packages, such as TCGAbiolinks, limma, and RPPA, were employed for data preprocessing and integration. Differential expression analysis, network construction, miRNA regulation exploration, pathway enrichment analysis, and independent dataset validation were performed. Results: Eight consistently upregulated hub genes-including ACTB, HSP90AA1, FN1, HSPA8, CDC42, CDH1, UBC, and EP300-were identified in breast cancer, indicating their potential significance in driving the disease. Pathway enrichment analysis revealed highly enriched pathways in breast cancer, including proteoglycans in cancer, PI3K-Akt, and mitogen-activated protein kinase signaling. Conclusion: This integrated multi-omic data analysis provides valuable insights into the regulatory mechanisms, network characteristics, and functional roles of genes, miRNAs, and proteins in breast cancer. The findings contribute to our understanding of the molecular landscape of breast cancer, facilitate the identification of potential therapeutic targets, and inform strategies for effective treatment.

Transcriptome analysis of high- and low-selenium genotypes identifies genes responsible for selenium absorption, translocation, and accumulation.

Introduction: Selenium is an essential micronutrient the human body requires, which is closely linked to health. Rice, a primary staple food globally, is a major source of human selenium intake. To develop selenium-enriched rice varieties, it is imperative to understand the mechanisms behind selenium's absorption and transport within rice, alongside identifying the key genes involved in selenium uptake, transport, and transformation within the plant. Methods: This study conducted transcriptome sequencing on four types of rice materials (two with low-selenium and two with high-selenium contents) across roots, stems, leaves, and panicles to analyze the gene expression differences. Results and discussion: Differential gene expression was observed in the various tissues, identifying 5,815, 6,169, 7,609, and 10,223 distinct genes in roots, stems, leaves, and panicles, respectively. To delve into these differentially expressed genes and identify the hub genes linked to selenium contents, weighted gene co-expression network analysis (WGCNA) was performed. Ultimately, 10, 8, 7, and 6 hub genes in the roots, stems, leaves, and panicles, respectively, were identified. The identification of these hub genes substantially aids in advancing our understanding of the molecular mechanisms involved in selenium absorption and transport during the growth of rice.