Experimental and in silico analysis of LINC01279 expression in tumor of patients with breast cancer.

Breast cancer (BC) is characterized by the increase of malignant cells in the breast. The malignant cells begin in the lining of the breast milk glands or ducts (ductal epithelium). BC is the most frequent cancer in women, but it may also occur in males. Long non-coding RNAs (lncRNA) have been demonstrated to control the development and incidence of cancer. However, some lncRNAs experience potential changes in BC, but their role has not been well studied. LINC01279 is known as a valuable biomarker in gastric cancer but has not yet been studied in BC. Changes in LINC01279 expression levels in BC samples were investigated by microarray. Q-PCR was also used to evaluate the expression of LINC01279 in the tumor and normal adjacent samples of 30 BC patients. The LINC01279 co-expressed gene module was discovered using weighted gene correlation network analysis (WGCNA) on the relevant dataset. The top ten hub genes were determined using gene ontology (GO) functional enrichments on the co-expressed gene module. The results of the bioinformatics study showed an increase in LINC01279 expression levels (log2FC = 3.228749561, adj.P.Val = 1.69E - 12) in tumor samples compared to normal marginal tissue. Q-PCR results also showed a significant increase in LINC01279 expression (P-value = 0.0005) in tumor samples. WGCNA analysis identified that the black module is the LINC01279 co-expressed module, and functional annotation analysis of black module genes enriched in significant cancer-related pathways and processes, including cell growth and/or maintenance, regulation of immune response, regulation of cell proliferation, and epithelial-to-mesenchymal transition (EMT). Regarding the real-time PCR results, the analysis of expression patterns has illuminated a distinct association between the heightened expression levels of LINC01279, and the stages of cancer progression as well as the metastatic potential of tumors. However, intriguingly, our observations have failed to reveal any statistically significant correlations between the relative expression of LINC01279 and tumor grade classification, or the presence of ER, PR, and HER2 biomarkers. The present study could provide a new perspective on the molecular regulatory. Processes associated with BC pathogenic mechanisms are linked to the LINC01279, although further research is needed on the possible role of this lncRNA in BC.

Circadian rhythm genes contribute to the prognosis prediction and potential therapeutic target in gastric cancer.

The role of circadian rhythm genes (CRGs) in gastric cancer (GC) is poorly understood. This study aimed to develop a CRG signature to improve understanding of prognosis and immunotherapy responses in GC patients. We integrated the The Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD) dataset with CRGs to develop a prognostic signature for GC. The signature's predictive ability was validated using Kaplan-Meier and ROC curves. The CIBERSORT algorithm evaluated immune cell proportions, and tumor immune dysfunction and exclusion score, as well as immune phenotype score, determined the response to immunotherapy for STAD patients. Finally, we assessed signature genes expression using real-time quantitative polymerase chain reaction. We developed a 4-CRG signature for STAD, demonstrating accurate prognostic ability. The low-risk group showed increased B cell memory and CD8 + T cells, and decreased M2 Macrophages compared to the high-risk group. Patients in the low-risk group had a higher likelihood of benefiting from immunotherapy. Additionally, gastric cancer tissues exhibited elevated expression of OPN3 and decreased expression of TP53 compared to adjacent tissue. This study successfully established a prognostic signature for CRGs, accurately predicting prognosis and immunotherapeutic response among STAD patients, providing insights for the development of personalized therapeutic strategies for these patients.

A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration analysis: AHR is a key gene in the cuproptosis process.

Background: Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis. Methods: We utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein-protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of AHR and FAS, and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation. Results: We identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes AHR, FAS, and FGF2 emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9. Conclusion: AHR, FAS, and FGF2 were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.

Exogenous interactome analysis of bovine viral diarrhea virus-host using network based-approach and identification of hub genes and important pathways involved in virus pathogenesis.

Bovine viral diarrhea (BVD) is one of the most important diseases in livestock, caused by BVD virus (BVDV). During the pathogenesis of the virus, many interactions occur between host and viral proteins. Studying these interactions can help better understand the pathogenesis of the virus, identify putative functional proteins, and find new treatment and prevention strategies. To this aim, a BVDV-host protein-protein interaction (PPI) network map was constructed using Cytoscape and analyzed with cytoHubba, Kyoto Encyclopedia of Genes and Genomics (KEGG), Gene Ontology (GO), and Protein Analysis Through Evolutionary Relationships (PANTHER). Npro with 125 connections had the greatest number of interactions with host proteins. CD46, EEF-2, and TXN genes were detected as hub genes using different ranking algorithms in cytoHubba. BVDV interactions with its host mainly focus on targeting translation, protein synthesis, and cellular metabolism pathways. Different classes of proteins including translational proteins, nucleic acid metabolism proteins, metabolite interconversion enzymes, and protein-modifying enzymes are affected by BVDV. These findings improve our understanding of the effects of the virus on the cell. Hub genes and key pathways identified in the present study can serve as targets for novel BVDV prevention or treatment strategies.

Identifying Potential Diagnostic and Therapeutic Targets for Infantile Hemangioma Using WGCNA and Machine Learning Algorithms.

Infantile hemangioma (IH) is the most common benign vascular tumor during infancy and childhood and is characterized by abnormal vascular development. It is the most common vascular tumor and its related mechanisms and treatments remain a problem. IH-related biomarkers have been identified using transcriptome analysis and can be used to predict clinical outcomes. This study aimed to identify the key target genes for IH treatment and explore their possible roles in the IH pathophysiology. Gene records were acquired from the Gene Expression Omnibus database. Utilizing integrated weighted gene co-expression network examination, gene clusters were determined. Single-sample gene set enrichment analysis was performed to gauge immune infiltration. Essential genes were identified via Random Forest and Least Absolute Selection and Shrinkage Operator analyses. Ultimately, a set of five pivotal genes associated with the ailment was identified (NETO2, IDO1, KDR, MEG3, and TMSB15A). A nomogram for predicting IH diagnosis was constructed based on hub genes. The calibration curve showed valid agreement between the prediction and conclusion that the key genes in the model were clinically significant. Neuropilin and Tolloid-like 2 (NETO2) are closely associated with tumor development. The role value of NETO2 expression levels increased in hemangioma-derived endothelial cells (HemECs). After silencing NETO2, the growth and migration of cancer cells were significantly restrained. This study revealed the critical role of NETO2 in IH development, suggesting that targeting NETO2 may be effective in improving the therapeutic outcome of IH.

Discovering common pathogenetic processes between periodontitis and Alzheimer's disease by bioinformatics and system biology approach.

BACKGROUND: There is increasing evidence that inflammation plays a key role in the pathophysiology of periodontitis (PT) and Alzheimer's disease (AD), but the roles of inflammation in linking PT and AD are not clear. Our aim is to analyze the potential molecular mechanisms between these two diseases using bioinformatics and systems biology approaches. METHODS: To elucidate the link between PT and AD, we selected shared genes (SGs) with gene-disease-association scores of ≥ 0.1 from the Disease Gene Network (DisGeNET) database, followed by extracting the hub genes. Based on these genes, we constructed gene ontology (GO) enrichment analysis, pathway enrichment analysis, protein-protein interaction (PPI) networks, transcription factors (TFs)-gene networks, microRNAs (miRNAs)-gene regulatory networks, and gene-disease association analyses. Finally, the Drug Signatures database (DSigDB) was utilized to predict candidate molecular drugs related to hub genes. RESULTS: A total of 21 common SGs between PT and AD were obtained. Cell cytokine activity, inflammatory response, and extracellular membrane were the most important enriched items in GO analysis. Interleukin-10 Signaling, LTF Danger Signal Response Pathway, and RAGE Pathway were identified as important shared pathways. IL6, IL10, IL1B, TNF, IFNG, CXCL8, CCL2, MMP9, TLR4 were identified as hub genes. Both shared pathways and hub genes are closely related to endoplasmic reticulum (ER) stress and mitochondrial dysfunction. Importantly, glutathione, simvastatin, and dexamethasone were identified as important candidate drugs for the treatment of PT and AD. CONCLUSIONS: There is a close link between PT and AD pathogenesis, which may involve in the inflammation, ER and mitochondrial function.

Integration of bioinformatics and multi-layered experimental validation reveals novel functions of acetylation-related genes in intervertebral disc degeneration.

BACKGROUND: The molecular mechanisms underlying intervertebral disc degeneration (IDD) remain poorly understood. The purpose of this work is to elucidate key molecules and investigate the roles of acetylation-related RNAs and their associated pathways in IDD. METHOD: Datasets GSE70362 and GSE124272 were obtained from the Gene Expression Omnibus (GEO) and combined to investigate differentially expressed genes (DEGs) associated with acetylation in IDD patients compared to healthy controls. Critical genes were pinpointed by integrating GO, KEGG and PPI networks. Furthermore, CIBERSORTx analysis was used to investigate the differences in immune cell infiltration between different groups and the biological processes (BP), cellular components (CC) and molecular functions (MF) were calculated by GSEA and GSVA. In addition, The single-cell database GSE165722 was incorporated to validate the specific expression patterns of hub genes in cells and identify distinct cell subtypes. This provides a theoretical basis for a more in-depth understanding of the roles played by critical cell subtypes in the process of IDD. Subsequently, tissues from IVD with varying degrees of degeneration were collected to corroborate the key DEGs using western blot, RT-qPCR, and immunofluorescence staining. RESULTS: By integrating various datasets and references, we identified a total of 1620 acetylation-related genes. These genes were subjected to a combined analysis with the DEGs from the databases included in this study, resulting in the discovery of 358 acetylation-related differentially expressed genes (ARDEGs). A comparative analysis with differentially expressed genes obtained from three databases yielded 19 ARDEGs. The PPI network highlighted the top 10 genes (IL1B, LAMP1, PPIA, SOD2, LAMP2, FBL, MBP, SELL, IRF1 and KHDRBS1) based on their protein interaction relationships. CIBERSORTx immune infiltration analysis revealed a moderate positive correlation between the gene IL1ß and Mast.cells.activated, as well as a similar correlation between the gene IRF1 and Mast.cells.activated. Single-cell dataset was used to identify cell types and illustrate the distribution of hub genes in different cell types. The two cell types with the highest AUCell scores (Neutrophils and Monocytes) were further explored, leading to the subdivision of Neutrophils into two new cell subtypes: S100A9-type Neutrophils and MARCKS-type Neutrophils. Monocytes were labeled as HLA-DRA9-type Monocytes and IGHG3-type Monocytes. Finally, molecular biology techniques were employed to validate the expression of the top 10 hub genes. Among them, four genes (IL1ß, SOD2, LAMP2, and IRF1) were confirmed at the gene level, while two (IL1ß and SOD2) were validated at the protein level. CONCLUSION: In this study, we carried out a thorough analysis across three databases to identify and compare ARDEGs between IDD patients and healthy individuals. Furthermore, we validated a subset of these genes using molecular biology techniques on clinical samples. The identification of these differently expressed genes has the potential to offer new insights for diagnosing and treating IDD.

Identification of Potential Therapeutics for Infantile Hemangioma via in silico Investigation and in vitro Validation.

Introduction: Infantile Hemangioma (IH) is a prevalent benign vascular tumor affecting approximately 5-10% of infants. Its underlying pathogenesis remains enigmatic, and current therapeutic approaches show limited effectiveness. Our study aimed to discover potential IH-associated therapeutics through a transcriptomic, computational drug repurposing methodology. Methods: Utilizing the IH-specific dataset GSE127487 from the Gene Expression Omnibus, we identified differentially expressed genes (DEGs) and conducted weighted gene coexpression network analysis (WGCNA). Subsequently, a protein-protein interaction (PPI) network was constructed to obtain the top 100 hub genes. Drug candidates were sourced from the Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD). Results: Our analysis revealed 1203 DEGs and a significant module of 1780 mRNAs strongly correlated with IH. These genes were primarily enriched in the PI3K/AKT/MTOR, RAS/MAPK, and CGMP/PKG signaling pathway. After creating a PPI network of overlapping genes, we filtered out the top 100 hub genes. Ultimately, 44 non-toxic drugs were identified through the CMap and CTD databases. Twelve molecular-targeting agents (belinostat, chir 99021, dasatinib, entinostat, panobinostat, sirolimus, sorafenib, sunitinib, thalidomide, U 0126, vorinostat, and wortmannin) may be potential candidates for IH therapy. Moreover, in vitro experiments demonstrated that entinostat, sorafenib, dasatinib, and sirolimus restricted the proliferation and migration and initiated apoptosis in HemEC cells, thereby underscoring their potential therapeutic value. Conclusion: Our investigation revealed that the pathogenic mechanism underlying IH might be closely associated with the PI3K/AKT/MTOR, RAS/MAPK, and CGMP/PKG signaling pathways. Furthermore, we identified twelve molecular-targeting agents among the predicted drugs that show promise as therapeutic candidates for IH.

Transcriptomic analysis used to discover potential therapeutics for Infantile Hemangioma (IH). Key IH-related pathways: PI3K/AKT/MTOR, RAS/MAPK, and CGMP/PKG signaling identified. Identified 44 non-toxic drugs as potential IH therapies via CMap and CTD. Twelve molecular agents show potential as IH therapy candidates. In vitro studies confirmed entinostat, sorafenib, dasatinib, and sirolimus inhibit HemEC cell proliferation and induce apoptosis.

Bioinformatics analysis-based mining of potential markers for inflammatory bowel disease and their immune relevance.

Background: The incidence of inflammatory bowel disease (IBD) is increasing every year and is characterized by a prolonged course, frequent relapses, difficulty in curing, and a lack of more efficacious therapeutic biomarkers. The aim of this study was to find key core genes as therapeutic biomarkers for IBD. Methods: GSE75214 in Gene Expression Omnibus (GEO) was used as the experimental set. The genes in the top 25% of standard deviation of all samples in the experimental set were subjected to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, least absolute shrinkage and selection operator (LASSO) logistic regression was used to further screen the central genes. Finally, the validity of hub genes was verified on GEO dataset GSE179285 using "BiocManager" R package. Results: Twelve well-preserved modules were identified in the experimental set using the WGCNA method. Among them, five modules significantly associated with IBD were screened as clinically significant modules, and four candidate genes were screened from these five modules. Then TIMP1, GUCA2B, and HIF1A were screened as hub genes. These hub genes successfully distinguished tumor samples from healthy tissues by artificial neural network algorithm in an independent test set with an area under the working characteristic curve of 0.946 for the subjects. Conclusions: IBD differentially expressed gene (DEGs) are involved in immunoregulatory processes. TIMP1, GUCA2B, and HIF1A, as core genes of IBD, have the potential to be therapeutic targets for patients with IBD, and our findings may provide a new outlook on the future treatment of IBD.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning.

Background: Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF. Methods: We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS. Results: Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4. Conclusion: In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Combining bioinformatics, network pharmacology and artificial intelligence to predict the target genes of S-ketamine for treating major depressive disorder.

BACKGROUND: Ketamine has received attention owing to its rapid and long-lasting antidepressant effects; however, its clinical application is restricted by its addictiveness and adverse effects. S-ketamine, which is the S-enantiomer of ketamine, is considered safer and better tolerated by patients than ketamine. AIMS: This study aimed to identify the key gene targets and potential signalling pathways associated with the mechanism of S-ketamine in major depressive disorder (MDD) treatment. METHODS: The GSE98793 dataset was extracted from the Gene Expression Omnibus database, and differentially expressed genes were identified in blood samples from patients with MDD and healthy individuals. The hub genes among the differentially expressed genes were identified and enrichment analysis was performed. The therapeutic targets and related signalling pathways of S-ketamine in MDD treatment were analysed. The 3D structures of the target proteins were predicted using AlphaFold2, and molecular docking was performed to verify whether S-ketamine could be successfully docked to the predicted targets. A quantitative polymerase chain reaction was performed to determine the effect of ketamine on the screened targets. Among 228 target genes annotated using pharmacophore target gene analysis, 3 genes were identified and 2 therapeutic signalling pathways were discovered. RESULTS: S-ketamine exerts downregulatory effects on TGM2 and HSP90AB1 expression but exerts an up-regulatory effect on ADORA3 expression. The protein structures of the therapeutic targets were successfully predicted using AlphaFold2. CONCLUSIONS: S-ketamine may alleviate depression by targeting specific genes, including TGM2, HSP90AB1 and ADORA3, as well as signalling pathways, including the gonadotropin-releasing hormone and relaxin signalling pathways.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes: a bioinformatics analysis.

AIM: To prevent neovascularization in diabetic retinopathy (DR) patients and partially control disease progression. METHODS: Hypoxia-related differentially expressed genes (DEGs) were identified from the GSE60436 and GSE102485 datasets, followed by gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Potential candidate drugs were screened using the CMap database. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hypoxia-related hub genes. A nomogram was generated using the rms R package, and the correlation of hub genes was analyzed using the Hmisc R package. The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve (ROC) curves. Finally, a hypoxia-related miRNA-transcription factor (TF)-Hub gene network was constructed using the NetworkAnalyst online tool. RESULTS: Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified, such as ruxolitinib, meprylcaine, and deferiprone. In addition, 8 hub genes were also identified: glycogen phosphorylase muscle associated (PYGM), glyceraldehyde-3-phosphate dehydrogenase spermatogenic (GAPDHS), enolase 3 (ENO3), aldolase fructose-bisphosphate C (ALDOC), phosphoglucomutase 2 (PGM2), enolase 2 (ENO2), phosphoglycerate mutase 2 (PGAM2), and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3). Based on hub gene predictions, the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs, 77 TFs, and hub genes. The results of ROC showed that the except for GAPDHS, the area under curve (AUC) values of the other 7 hub genes were greater than 0.758, indicating their favorable diagnostic performance. CONCLUSION: PYGM, GAPDHS, ENO3, ALDOC, PGM2, ENO2, PGAM2, and PFKFB3 are hub genes in DR, and hypoxia-related hub genes exhibited favorable diagnostic performance.

Unraveling the molecular pathogenesis of Type 2 Diabetes and its impact on female infertility: A bioinformatics and systems biology approach.

Type 2 diabetes mellitus (T2D) has been linked with female infertility (FI). Nevertheless, our understanding of the molecular hallmarks and underlying mechanisms remains elusive. This research article aimed to find the hub genes, pathways, transcription factors, and miRNA involved. For this study, softwares like cytoscape, string, Enrichr, FFL loop, etc., were utilized. This research article employed differentially expressed genes (DEGs) to identify multiple biological targets to understand the association between T2D and female infertility (FI). Between T2D and FI, we found 3869 differentially expressed genes. We have also analyzed different pathways like thyroid hormone signaling pathways, AGE-RAGE signaling pathways in diabetic complications and ubiquitin-mediated proteolysis through pathway analysis. Moreover, hub genes MED17, PRKCG, THRA, FOXO1, NCOA2, PLCG2, COL1A1, CXCL8, PRPF19, ANAPC5, UBE2I, XIAP and KEAP1 have been identified. Additionally, these hub genes were subjected to identify the miRNA-mRNA regulation network specific to T2D-associated female infertility. In the FFL study (Feed Forward Loop), transcription factor (SP1, NFKB1, RELA and FOX01), miRNA (has-mir-7-5p, has-let-7a-5p, hsa-mir-16-5p, hsa-mir-155-5p, has-mir-122-5p, has-let-7b-5p, has-mir-124-3p, has-mir-34a-5p, has-mir-130a-3p, has-let-7i-5p, and hsa-mir-27a-3p) and six genes (XIAP, THRA, NCOA2, MED17, FOXO1, and COL1A1) among the thirteen key genes were recognized as regulator and inhibitor. Our analysis reveals that these genes can serve as a significant biomarker for female infertility linked with Type 2 Diabetes, through the prioritization of candidate genes. This study gives us insight into the molecular and cellular mechanism of T2D-associated FI. This finding helps in developing novel therapeutic approaches and will improve efficacy and reduce side effects of the treatment. This research requires further experimental investigation of the principal targets.

Identification of hub modules and therapeutic targets associated with CD8+T-cells in HF and their pan-cancer analysis.

Heart failure (HF) is a terminal condition of multiple cardiovascular disorders. Cancer is a deadly disease worldwide. The relationship between HF and cancer remains poorly understood. The Gene Expression Omnibus database was used to download the RNA sequencing data of 356 patients with hypertrophic cardiomyopathy-induced HF and non-HF. A co-expression network was established through the weighted correlation network analysis (WGCNA) to identify hub genes of HF and cancer. Cox risk analysis was performed to predict the prognostic risks of HF hub genes in pan-cancer. HF was linked to immune response pathway by the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A positive correlation was observed between the expression levels of 4 hub genes and the infiltration of CD8+T-cells in pan-cancer. 4 hub genes were identified as beneficial prognostic factors in several cancers. Western blotting and real-time polymerase chain reaction validated the high expression of GZMM, NKG7, and ZAP70 in both mice and patients with HF compared to control groups. Our study highlights the shared immune pathogenesis of HF and cancer and provides valuable insights for developing novel therapeutic strategies, offering new opportunities for improving the management and treatment outcomes of both HF and cancer.

Weighted Gene Co-expression Network Analysis (WGCNA) of Wnt Signaling Related to Periodontal Ligament Formation: A Bioinformatics-Based Analysis.

Introduction The Wnt signaling pathway is crucial for tooth development, odontoblast differentiation, and dentin formation. It interacts with epithelial cadherin (E-cadherin) and beta-catenin in tooth development and periodontal ligament (PDL) formation. Dysregulation of Wnt signaling is linked to periodontal diseases, requiring an understanding of therapeutic interventions. Weighted gene co-expression network analysis (WGCNA) can identify co-expressed gene modules. Our study aims to identify hub genes in WGCNA analysis of Wnt signaling-based PDL formation. Methods The study used a microarray dataset GSE201313 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus to analyze the impact of DMP1 expression on XLH dental pulp cell differentiation and PDL formation. The standardized dataset was used for WGCNA analysis, which generated a co-expression network by calculating pairwise correlations between genes and constructing an adjacency matrix. The topological overlap matrix (TOM) was transformed into a hierarchical clustering tree and then cut into modules or clusters of highly interconnected genes. The module eigengene (ME) was calculated for each module, and the genes within this module were identified as hub genes. Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) was used for WGCNA analysis. Results The study used the WGCNA package to analyze 1,000 differentially expressed genes, constructing a gene co-expression network and generating a hierarchical clustering tree and TOM. The analysis reveals a scale-free topology fitting index R2 and mean connectivity for various soft threshold powers, with an R2 value of 5. COL6A1, MMP3, BGN, COL1A2, and FBN2 are hub genes implicated in PDL development. Conclusion The study identified key hub genes, including COL6A1, MMP3, BGN, and FBN2, crucial for PDL formation, tissue remodeling, and cell-matrix interactions, guiding future therapeutic strategies.

Bioinformatics Identification and Experimental Verification of Disulfidptosis-Related Genes in the Progression of Osteoarthritis.

BACKGROUND: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. METHODS: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. RESULTS: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. CONCLUSIONS: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA.

Joint GWAS and WGCNA Identify Genes Regulating the Isoflavone Content in Soybean Seeds.

Isoflavone is a secondary metabolite of the soybean phenylpropyl biosynthesis pathway with physiological activity and is beneficial to human health. In this study, the isoflavone content of 205 soybean germplasm resources from 3 locations in 2020 showed wide phenotypic variation. A joint genome-wide association study (GWAS) and weighted gene coexpression network analysis (WGCNA) identified 33 single-nucleotide polymorphisms and 11 key genes associated with soybean isoflavone content. Gene ontology enrichment analysis, gene coexpression, and haplotype analysis revealed natural variations in the Glyma.12G109800 (GmOMT7) gene and promoter region, with Hap1 being the elite haplotype. Transient overexpression and knockout of GmOMT7 increased and decreased the isoflavone content, respectively, in hairy roots. The combination of GWAS and WGCNA effectively revealed the genetic basis of soybean isoflavone and identified potential genes affecting isoflavone synthesis and accumulation in soybean, providing a valuable basis for the functional study of soybean isoflavone.

Identification of key module and hub genes affecting broiler body weight through weighted gene co-expression network analysis.

Body weight (BW) is an important economic trait in chickens. The hypothalamus serves as a central regulator of appetite and energy balance, and extensive research has demonstrated its pivotal role in regulating BW. However, the molecular network of the hypothalamus regulating BW traits in chickens needs to be further illuminated. In the present study, 200 1-day-old male 817 broilers were reared to 50 d of age, and BW were recorded. 20 birds with the lowest BW were classified as the low body weight group (L-BWG), and 20 birds with the highest BW were classified as the high body weight group (H-BWG). 18 hypothalamic tissue samples were collected, including 5 from the L-BWG, 5 from the H-BWG, and 8 from the middle weight range, and were analyzed using RNA-seq and weighted gene co-expression network analysis (WGCNA). Among the 18 RNA-seq samples, 5 samples from the L-BWG and 5 from the H-BWG were selected for differential expression gene analysis. Compared with the L-BWG, 195 and 1,241 genes were upregulated and downregulated in the H-BWG, respectively. The WGCNA analysis classified all co-expressed genes in the hypothalamus of 817 broilers into 20 modules. Among these modules, the pink module was identified as significantly negatively (r = -0.81, P = 4×10-5) associated with BW. Furthermore, several genes, including Wnt family member 6 (WNT6), growth differentiation factor 11 (GDF11), bone morphogenetic protein 4 (BMP4), and erb-b2 receptor tyrosine kinase 4 (ERBB4), involved in "regulation of developmental process" and "response to growth factor," were identified as hub genes that contribute to the regulation of BW. These results provide valuable information for further understanding of the gene expression and regulation affecting BW traits and will contribute to the molecular breeding of chickens in the future.

DUSP6 protein action and related hub genes prevention of sepsis-induced lung injury were screened by WGCNA and Venn.

During a sepsis infection, the lung is extremely susceptible to damage. A condition known as acute respiratory distress syndrome (ARDS) may develop in extreme circumstances. The primary objective of this research is to identify important genes that are related with both sepsis and lung injury. These genes have the potential to act as novel biomarkers in the investigation of sepsis-induced lung injury prevention strategies. It was possible to download from GEO data both the sepsis-related dataset (GSE64457) and the lung injury-related dataset (GSE40839). In the GSE64457 dataset, using the "limma" package in R revealed 429 differentially expressed genes (DEGs) with logFC values more than or equal to -1 and p values <0.05. There were 266 genes that were up-regulated and 163 genes that were down-regulated. Through the use of Gene Ontology (GO), it was discovered that the majority of the DEGs were associated with the inflammatory response (BP terms), a particular granule lumen (CC terms), and protein binding (MF terms). By doing a pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), researchers were able to identify DEGs that were mostly associated with the NOD-like receptor signalling pathway, the TNF signalling pathway, and Epstein-Barr virus infection. Within the GSE40839 dataset, Weighted Gene Co-Expression Network Analysis (WGCNA) yielded a total of 7 modules, from which it was possible to screen out 2 critical modules and 693 key genes. The important genes and DEGs were both subjected to a Venn analysis. Finally, 14 genes that overlapped (ARL4A, LAIR1, MTHFD2, TSPAN13, DUSP6, PECR, CBS, TES, ASNS, SYNE1, FGF13, LCN2, KLF10, BCAT1) were closely associated to the incidence and development of sepsis-induced lung injury. This indicates that these genes are the essential genes to avoid the occurrence of sepsis-induced lung injury. This study provides novel strategies for preventing lung harm brought on by sepsis.

Transcriptomic Hallmarks of Hypoxic-Ischemic Brain Injury: Insights from an in Vitro Model.

BACKGROUND: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets. METHODS: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression. RESULTS: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends. CONCLUSIONS: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia.