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Klinefelter syndrome (KS) is the most frequent genetic anomaly in infertile men. Given its unclear mechanism, we aim to investigate critical genes and pathways in the pathogenesis of KS based on three bulk and one single-cell transcriptome data sets from Gene Expression Omnibus. We merged two data sets (GSE42331 and GSE47584) with human KS whole blood samples. When comparing the control and KS samples, five hub genes, including defensin alpha 4 (DEFA4), bactericidal permeability increasing protein (BPI), myeloperoxidase (MPO), intelectin 1 (ITLN1), and Xg Glycoprotein (XG), were identified. Besides, infiltrated degree of certain immune cells such as CD56bright NK cell were positively associated with the expression of ITLN1 and XG. Kyoto Encyclopedia of Genes and Genomes analysis identified upregulated phosphatidylinositol 3-kinase (PI3K)/AKT pathway in KS. Gene set enrichment analysis followed by gene set variation analysis confirmed the upregulation of G2M checkpoint and heme metabolism in KS. Thereafter, the GSE200680 data set was used for external validation of the expression variation of hub genes from healthy to KS testicular samples, and each hub gene yielded excellent discriminatory capability for KS without exception. At the single-cell level, the GSE136353 data set was utilized to evaluate intercellular communication between different cell types in KS patient, and strong correlations were detected between macrophages/ dendritic cells/ NK cells and the other cell types. Collectively, we provided hub genes, pathways, immune cell infiltration degree, and cell-cell communication in KS, warranting novel insights into the pathogenesis of this disease. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) significantly increases stroke risk, but the risk factors and predictors of AF-stroke are rarely discovered. Hence, it is necessary to identify novel biomarker and therapeutic targets. Our study aimed to find effective pathogenic targets and elucidate the underlying molecular mechanism. First, we conducted weighted co-expression network analysis (WGCNA) in stroke-related dataset and AF-related dataset. The pink module that was highly associated with stroke (r = 0.78, p = 2 × 10–20) and the red module that was correlated with AF (r = 0.71, p = 4 × 10–5) was identified. The male-specific lethal homolog 3 (MSL3) was selected by taking the intersection between top 5 genes in stroke-related dataset and top 5 genes in AF-related dataset. Next, the expression of key gene and the receiver operating characteristic curve (ROC) analysis were also validated in other two datasets. Single sample gene set enrichment analysis (ssGSEA), single sample gene set variation analysis (ssGSVA) and correlation between genes in the key modules were exploited to investigate the function of MSL3. In addition, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune cells infiltration were conducted in key modules. Eventually, 10 small molecular drugs that have the potential to treat AF-stroke were filtered. In conclusion, our research find MSL3 can be as a novel biomarker and several candidate molecular drugs for treating AF-stroke. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alfalfa (Medicago sativa L.), an important forage crop with high nutritional value and good palatability, plays a vital role in the development of animal husbandry in China. In Northeast China, there are vast areas of saline–alkali land that remain undeveloped. Given that alfalfa is a highly adaptable forage crop, exploring its salt tolerance at the molecular transcriptional level and identifying salt-tolerant genes has great significance for breeding salt-resistant alfalfa varieties. This also provides valuable genetic resources for better utilization of saline–alkali land. In this study, we conducted two rounds of screening on 41 alfalfa varieties and identified WL168 as a salt-sensitive variety and Longmu801 as a salt-tolerant variety. After 7 days of 300 mM salt stress, both varieties showed a decreasing trend in plant height, fresh weight, and dry weight over time, but Longmu801 demonstrated better water retention ability compared to WL168. Chlorophyll content also declined, but chlorophyll a and total chlorophyll levels in Longmu801 were higher than in WL168. Hydrogen peroxide and malondialdehyde levels increased overall, but Longmu801 had significantly lower levels than WL168 under prolonged stress. Both varieties showed increasing trends in soluble sugars, proline, and antioxidant enzymes (SOD, POD, CAT), with Longmu801 significantly outperforming WL168. This suggests that the two varieties share similar growth and physiological response mechanisms, with their differences primarily arising from variations in indicator levels. In the above, comparisons between varieties were conducted based on the relative values of the indicators in relation to their controls. Transcriptomic analysis revealed that under salt stress, Longmu801 had 16,485 differentially expressed genes (DEGs) relative to its control, while WL168 had 18,726 DEGs compared to its control. Among these, 2164 DEGs shared the same expression trend, with GO functions enriched in response to oxidative stress, nucleus, plasma membrane, and others. The KEGG pathways were enriched in phenylpropanoid biosynthesis, protein processing in the endoplasmic reticulum, starch and sucrose metabolism, and others. This suggests that alfalfa's transcriptional response mechanism to salt stress involves these pathways. Additionally, the variety-specific DEGs were also enriched in the same KEGG pathways and GO functions, indicating that the differences between the two varieties stem from their unique stress-responsive DEGs, while their overall mechanisms for coping with stress remain similar. To further identify salt stress-related genes, this study conducted WGCNA analysis using 32,683 genes and physiological indicators. Six modules closely related to physiological traits were identified, and the top five genes ranked by degree in each module were selected as hub genes. Further analysis of these hub genes identified five genes directly related to salt stress: Msa085011, Msa0605650, Msa0397400, Msa1258740, and Msa0958830. Mantel test analysis revealed that these genes showed strong correlations with physiological indicators. This study will provide important insights for breeding salt-tolerant alfalfa varieties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alternative splicing (AS) is a prevalent post-transcriptional regulatory mechanism in eukaryotes and plays a crucial role in plant disease resistance. Here, we used the Illumina Novaseq sequencing platform to conduct transcriptome sequencing on canola (Brassica napus) leaves infected with the blackleg pathogen (Leptosphaeria biglobosa strain nm−1) at 0 h, 72 h, 120 h, and 168 h post-inoculation to investigate the mechanism of AS coordination with transcriptional regulation in canola's response to blackleg disease. The rMATS software (4.1.0) was employed to analyze different AS events in samples taken at 72 h, 120 h, and 168 h. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to elucidate the biological functions of differentially spliced genes at various time points, while Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify key modules and hub genes. As a result, our analysis reveals 16908 AS events across three time points, with 221 being differently spliced. Intron retention (RI) was the most common AS event, accounting for approximately 55% of all events, while alternative 5′ splice site events were least common, comprising only 2%. Furthermore, a total of 213 significantly differentially spliced genes were identified, which were enriched in functions related to protein kinase activity, transferase activity, and pathways such as MAPK signaling pathway—plant and plant hormone signal transduction. WGCNA identified three key modules and ten hub genes, including calcium-binding transcription activator 1, LRR class receptor serine/threonine protein kinase FEI 2, PLATZ transcription factor family proteins, serine/threonine protein kinase PRP4, and E3 ubiquitin ligase SUD1, all of which are associated with canola resistance to L. biglobosa. Thus, this study provides a theoretical basis for identifying disease-resistance genes involved in AS and for exploring the functions of AS gene isoforms in canola. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Coronary artery disease (CAD) is a predominant cardiovascular condition influenced by risk factors, with an emphasis on major depressive disorder (MDD). However, the shared mechanisms and therapeutic targets for CAD and MDD remain incompletely comprehended. Functional enrichment analyses were conducted to investigate the pathways associated with the differentially expressed genes (DEGs) in the CAD and MDD datasets. Hub genes were identified utilizing the Protein-Protein Interaction network and Cytoscape software. The single sample gene set variation analysis was applied to assess immune cell infiltration in the CAD and MDD datasets. Weighted gene co-expression network analysis and molecular biological experiments were executed to evaluate these hub genes. Molecular docking was conducted to identify drug candidates targeting these hub genes. The overlapping DEGs between the CAD and MDD datasets were mainly enriched in the Herpes simplex virus 1 infection and the NF-kappa B signaling pathways. CDC42, NDUFB3, and TXN were validated within the eigengenes of the blue module, which exhibited a significant association with the CAD phenotype. The drug candidate GS-9620 was identified as a potential protective agent against both disorders. In conclusion, CDC42, NDUFB3, and TXN held potential as molecular biomarkers and therapeutic targets for the simultaneous treatment of CAD and MDD.

Abstract 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

Background Hepatocellular carcinoma (HCC) is the third leading cause of common cancer-related mortality globally, accounting for approximately 90% of all primary liver cancer cases. Its recurrence and mortality rates are high, with the underlying molecular mechanisms remaining unclear. Objective To explore potential molecular mechanisms of HCC and explore novel biomarkers. Methods RNA-seq expression data and clinical information were retrieved from TCGA database, differential gene expression analysis was conducted between normal liver tissue and HCC tissue. Enrichment analysis on the differentially expressed genes was performed. Based on the gene expression data profiles of HCC in TCGA, a co-expression network was established using the WGCNA R package, and weighted gene co-expression network analysis (WGCNA) was performed to select clinically significant modules and screen candidate Hub genes; the candidate Hub genes were further analyzed for significant differential expression in HCC tissues and normal liver tissues, and whether they were significantly correlated with the overall survival and disease-free survival of HCC patients. The Hub genes were conclusively identified, and their protein expression was validated through the Human Protein Atlas database. Results The genetic expression data in this study were obtained from 50 normal liver tissue samples and 373 HCC tissue samples. Through differential gene expression analysis, a total of 7 230 genes differential expression between HCC and normal hepatic tissue, comprising 3 691 up-regulated genes and 3 539 down-regulated genes in HCC were identified. Enrichment analysis showed that the up-regulated differentially expressed genes were mainly involved in cell cycle regulation and mitotic processes; the down-regulated differentially expressed genes were mainly involved in processes such as small molecule metabolism and organic acid metabolism. WGCNA identified 19 gene modules related to the clinical features of HCC patients, the cyan and purple modules were screened by analyzing the relationship between the modules and the clinical features. The first two genes in the cyan module genes that were strongly associated with both overall survival and disease-free survival of patients were VPS45 and FAM189B. In the purple module genes, first two genes that were strongly associated with both overall survival and disease-free survival of patients were CLEC1B and FCN3, respectively; therefore, VPS45, FAM189B, CLEC1B and FCN3 were identified as the final Hub genes. Immunohistochemical staining in the Human Protein Atlas database showed that VPS45 and FAM189B were expressed higher in HCC tissues than in normal liver tissues. FCN3 was expressed in HCC tissues lower than in normal liver tissues, the difference in the expression of CLEC1B between HCC tissues and normal liver tissues was not obvious. Conclusion VPS45, FAM189B, CLEC1B and FCN3 have been preliminary identified as possible novel potential biomarkers for HCC, which may provide a theoretical basis for targeted therapy of HCC.

Abstract 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.

BACKGROUND: Iron overload refers to excessive accumulation of iron in the body, which can cause pathological changes in various tissues. At present, the molecular mechanism and potential gene targets related to iron overload in osteoarthritis still need to be further studied and explored. OBJECTIVE: To analyze the key genes of iron overload in osteoarthritis by means of bioinformatics and verify them in animal experiments, so as to provide a new idea for preventing and treating osteoarthritis from the perspective of iron overload. METHODS: GEO database and GeneCards database were used to screen out genes associated with osteoarthritis and genes associated with iron overload. Then, the intersection of the two was taken to obtain a collection of genes commonly associated with osteoarthritis and iron overload. GO and KEGG enrichment analyses were used to screen the functions and pathways of these genes. To further investigate the interactions between these genes, a proteinprotein interaction network was constructed and five computational methods of Cytoscape software were utilized to identify the Hub genes for iron overload in osteoarthritis. Finally, 12 male Sprague-Dawley rats were divided into osteoarthritis group and normal group, with 6 rats in each group. A knee osteoarthritis model was established by the modified Hulth method in the osteoarthritis group. The expression of Hub genes in the knee joint of rats in each group was detected by PCR. RESULTS AND CONCLUSION: (1) A total of 51 genes associated with iron overload were identified in osteoarthritis. GO enrichment analysis showed that these genes were mainly involved in cytokine receptor binding, chemokine receptor binding, cytokine activity, growth factor receptor binding and oligosaccharide binding. (2) KEGG enrichment analysis showed that genes associated with iron overload in osteoarthritis was mainly involved in tumor necrosis factor signaling pathway and lipid and atherosclerosis signaling pathway. (3) The protein-protein interaction network was constructed, and five Hub genes of iron overload, intercellular cell adhesion molecule-1, tumor necrosis factor superfamily member 11, myelocytomatosis oncogene, janus kinase 2, and interleukin 6, were obtained by further analysis. Animal experiments verified that there were significant differences in the expression of the above Hub genes in the rat knee joint between the control group and the experimental group (P < 0.05). (4) All these findings show that intercellular cell adhesion molecule-1, tumor necrosis factor superfamily member 11, myelocytomatosis oncogene, janus kinase 2, and interleukin 6 can be used as the Hub genes of iron overload in osteoarthritis, which are expected to become new targets for the prevention and treatment of osteoarthritis. [ABSTRACT FROM AUTHOR]

Abstract 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.

Wei Lu,&ast; Zhenyu Yang,&ast; Mengjie Wang, Ye Zhang, Zuoliang Qi, Xiaonan Yang Department of Hemangioma and Vascular Malformation, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100144, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaonan Yang, Department of Hemangioma and Vascular Malformation, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100144, People’s Republic of China, Email yxnan@aliyun.comIntroduction: 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.Plain Language Summary: 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. Keywords: infantile hemangioma, entinostat, sirolimus, hub gene, transcriptome, bioinformatics

Abstract Background Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive oral cancer with a poor prognosis. Its polygenic risk is likely influenced by complex transcriptional disorders involving networks of co-expressed and functionally related genes, though such investigations are limited in OSCC. Methods We analyzed the GSE37991 dataset, comprising 40 OSCC and 40 normal oral tissue samples from the Gene Expression Omnibus. Tumor-specific modules were identified using weighted correlation network analysis (WGCNA), leading to the selection of hub mRNAs and lncRNAs. These lncRNAs were used to construct lncRNA–mRNA and competing endogenous RNA networks. We further examined the expression profiles and survival data of these genes from the Cancer Genome Atlas. Prognostic markers were identified and validated through 5-year survival analysis and Cox proportional hazards modeling. RT-qPCR was used to validate the expression levels in clinical OSCC tissues. Results We identified 1847 differentially expressed genes in OSCC tissues. WGCNA revealed four OSCC-specific modules, screening 120 hub mRNAs and five hub lncRNAs. Two prognostic markers (AQP5, IL-26) from hub mRNAs and three (FRMD5, INHBB, GUCY1A3) from the lncRNA–mRNA network were associated with survival. Validation showed lower expression of AQP5 and GUCY1A3, and higher expression of FRMD5 and INHBB in OSCC compared to normal tissues. Conclusion This study enhances our understanding of transcriptional dysregulation in OSCC and may highlights AQP5, IL-26, FRMD5, INHBB, and GUCY1A3 as promising prognostic biomarkers.

Abstract 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.

Hypopharyngeal cancer is a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

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. [ABSTRACT FROM AUTHOR]

Background: Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive oral cancer with a poor prognosis. Its polygenic risk is likely influenced by complex transcriptional disorders involving networks of co-expressed and functionally related genes, though such investigations are limited in OSCC. Methods: We analyzed the GSE37991 dataset, comprising 40 OSCC and 40 normal oral tissue samples from the Gene Expression Omnibus. Tumor-specific modules were identified using weighted correlation network analysis (WGCNA), leading to the selection of hub mRNAs and lncRNAs. These lncRNAs were used to construct lncRNA–mRNA and competing endogenous RNA networks. We further examined the expression profiles and survival data of these genes from the Cancer Genome Atlas. Prognostic markers were identified and validated through 5-year survival analysis and Cox proportional hazards modeling. RT-qPCR was used to validate the expression levels in clinical OSCC tissues. Results: We identified 1847 differentially expressed genes in OSCC tissues. WGCNA revealed four OSCC-specific modules, screening 120 hub mRNAs and five hub lncRNAs. Two prognostic markers (AQP5, IL-26) from hub mRNAs and three (FRMD5, INHBB, GUCY1A3) from the lncRNA–mRNA network were associated with survival. Validation showed lower expression of AQP5 and GUCY1A3, and higher expression of FRMD5 and INHBB in OSCC compared to normal tissues. Conclusion: This study enhances our understanding of transcriptional dysregulation in OSCC and may highlights AQP5, IL-26, FRMD5, INHBB, and GUCY1A3 as promising prognostic biomarkers. [ABSTRACT FROM AUTHOR]

【Objective】The aim of this study is to identify the co-expression modules of male sterile Welsh onion (Allium fistulosum L.) and to screen key hub genes, so as to provide deeper insights into the molecular mechanisms of male sterile in the Welsh onion. 【Method】Twenty-four cDNA libraries were constructed and sequenced for different development stages of flower buds from the male-sterile (MS) plants and male-fertile (MF) plants. Then weighted gene co-expression network analysis (WGCNA) was performed to reveal the key modules and hub genes. 【Result】A total of eighteen co-expression modules were identified based on 8,320 genes, among which three modules (blue, midnightblue, and black) were significantly correlated with pollen abortion of the MS plant. Functional enrichment analysis suggested that several pathways associated with the “phenylpropanoid biosynthesis” “cutin, suberine, and wax biosynthesis” “starch and sucrose metabolism” “alpha-linolenic acid metabolism” and “fatty acid elongation” were found to be involved in pollen abortion of the MS plant. By considering gene significance and intramodular connectivity, several hub genes including atpB, TPS9, TPD1, and BHLH35, were identified, which might play key roles in pollen development of the Welsh onion. Then hub genes related to pollen development response were verified for quantitative real-time PCR (RT-qPCR) analysis, and the expression patterns of RNA-Seq and RT-qPCR were very consistent. 【Conclusion】Three key modules of male sterile Welsh Onion are obtained, and hub genes like atpB, TPS9, and TPD1 are identified. It is found that the pollen abortion of the MS plant mainly is involved the processes of phenylpropanoid biosynthesis, cutin, suberine, and wax biosynthesis, starch and sucrose metabolism, alpha-Linolenic acid metabolism, and fatty acid elongation. [ABSTRACT FROM AUTHOR]

Objective: Candida albicans is a common fungal pathogen that triggers complex host defense mechanisms, including coordinated innate and adaptive immune responses, to neutralize invading fungi effectively. Exploring the immune microenvironment has the potential to inform the development of therapeutic strategies for fungal infections. Methods: The study analyzed individual immune cell profiles in peripheral blood mononuclear cells from Candida albicans -infected mice and healthy control mice using single-cell transcriptomics, fluorescence quantitative PCR, and Western blotting. We investigated intergroup differences in the dynamics of immune cell subpopulation infiltration, pathway enrichment, and differentiation during Candida albicans infection. Results: Our findings indicate that infiltration of CD4 + naive cells, regulatory T (Treg) cells, and Microtubules-associated (MT)-associated cells increased after infection, along with impaired T cell activity. Notably, CD4+T cells and plasma cells were enhanced after infection, suggesting that antibody production is dependent on T cells. In addition, we screened 6 hub genes, transcription factor forkhead box protein 3(Foxp3), cytotoxic T-lymphocyte associated protein 4 (CTLA4), Interleukin 2 Receptor Subunit Beta (Il2rb), Cd28, C-C Motif Chemokine Ligand 5 (Ccl5), and Cd27 for alterations associated with CD4+T cell differentiation. Conclusions: These results provide a comprehensive immunological landscape of the mechanisms of Candida albicans infection and greatly advance our understanding of adaptive immunity in fungal infections. [ABSTRACT FROM AUTHOR]

To explore underlying mechanisms related to the progression of colon cancer and identify hub genes associated with the prognosis of patients with colon cancer. GSE10950 and GSE62932 were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to screen out the differentially expressed genes (DEGs). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on DEGs. Moreover, STRING and Cytoscape software were utilized for establishing the network of protein–protein interaction (PPI) and identifying hub genes. Afterward, data from The Cancer Genome Atlas (TCGA) was utilized for identifying prognosis-related hub genes by Kaplan-Meier survival analysis. Colon cancer cell line LOVO and human normal intestinal epithelial cell line NCM-460 were exploited to demonstrate the differential expression of selected hub genes through RT-qPCR and western blot. The LOVO cells were transfected to regulate expressions of prognosis-associated genes, followed by exploring the effects of those genes on prognosis by Cell Counting Kit-8 assay and colony-forming assay for cancer cell proliferation, cell scratch test and transwell migration assay for cancer cell migration and Annexin V-PE/7-AAD double staining as well as flow cytometry for cancer cell apoptosis. In this study, 266 common DEGs were obtained from the intersection of two datasets. The GO analysis suggested the common DEGs mainly participated in the one-carbon metabolic process, cell cycle G2/M phase transition, organelle fission, cell cycle phase transition regulation, and regulation of mitotic cell cycle phase transition. The KEGG analysis demonstrated the common DEGs were related to the p53 signaling pathway, nitrogen metabolism, mineral absorption, and cell cycle. 10 hub genes including CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified and CCNB1, CLCA1, and PLK4 were prognosis-related. Increased expression of CCNB1, CLCA1, and PLK4 restrained proliferation as well as migration of cancer cells and induced apoptosis of cancer cells. CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified as hub genes and CCNB1, CLCA1, and PLK4 could inhibit the progression of colon cancer through inhibiting proliferation as well as migration of the cancer cell and promoting apoptosis of cancer cell. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The number of patients with COVID-19 caused by severe acute respiratory syndrome coronavirus 2 is still increasing. In the case of COVID-19 and tuberculosis (TB), the presence of one disease affects the infectious status of the other. Meanwhile, coinfection may result in complications that make treatment more difficult. However, the molecular mechanisms underpinning the interaction between TB and COVID-19 are unclear. Accordingly, transcriptome analysis was used to detect the shared pathways and molecular biomarkers in TB and COVID-19, allowing us to determine the complex relationship between COVID-19 and TB. Two RNA-seq datasets (GSE114192 and GSE163151) from the Gene Expression Omnibus were used to find concerted differentially expressed genes (DEGs) between TB and COVID-19 to identify the common pathogenic mechanisms. A total of 124 common DEGs were detected and used to find shared pathways and drug targets. Several enterprising bioinformatics tools were applied to perform pathway analysis, enrichment analysis and networks analysis. Protein–protein interaction analysis and machine learning was used to identify hub genes (GAS6, OAS3 and PDCD1LG2) and datasets GSE171110, GSE54992 and GSE79362 were used for verification. The mechanism of protein-drug interactions may have reference value in the treatment of coinfection of COVID-19 and TB. [ABSTRACT FROM AUTHOR]

Copyright of West China Journal of Stomatology is the property of Sichuan University, West China College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Objectives: Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored. Methods: GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi‐array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver‐operating characteristic curve analysis was used to verify the validity and agility of the hub genes‐based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real‐time polymerase chain reaction and western blotting. Results: Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue. Conclusion: We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis. [ABSTRACT FROM AUTHOR]

Hypopharyngeal cancer is a disease that is associated with EGFR‐mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Using genetic enhancement techniques has led to the development of poultry capable of fulfilling significant portions of human dietary requirements through meat and egg production. Maximizing genetic potential necessitates the provision of conducive environmental conditions. Heat stress poses a substantial challenge within the poultry sector, detrimentally impacting poultry production and growth, there by diminishing breeders' economic returns. Adaptation to heat stress is contingent upon a multitude of factors. The capacity to establish homeostasis through alterations in gene expression serves to mitigate extracellular damage and restore physiological equilibrium upon reversion to favorable temperature ranges. Domestic birds thrive within a temperature spectrum of 18-24 °C, with any deviation beyond this range precipitating elevated mortality rates. Elevated mortality is attributed to heat-induced suppression of the immune system and compromised resilience. Moreover, heat stress amplifies the secretion of the corticotropin-releasing hormone, subsequently elevating blood corticosterone levels, thereby instigating molecular alterations under stress conditions. Hence, a comprehensive examination of this challenge from diverse perspectives holds significant promise. Consequently, the present study endeavors to delineate protein complexes and pivotal signaling pathways regulated by distinct genes expressed in response to heat stress within the hypothalamus of broiler chickens. Methods: The systemic response of chickens' hypothalamus transcripts to heat stress was comprehensively examined in this investigation. Transcript expression profiles from eight chicken hypothalamus tissues, including four subjected to thermal stress and four under non-stress conditions, were retrieved from the GEO database (accession number GSE37400). The raw microarray data underwent rigorous quality control measures and normalization using GEO2R software to ensure reliability and consistency. Genes with discernible expression changes were identified based on a stringent criterion of P-value > 0.05 and (LogFC < 0.5-0.5). Subsequently, a gene expression regulatory network was constructed to elucidate the interplay among genes implicated in broiler heat stress, employing Cytoscape software in conjunction with the STRING database. The network visualization facilitated the identification of protein clusters, representing areas of high density, which were further analyzed using the CytoCluster plugin renowned for its efficacy in detecting such densely interconnected regions. Following the delineation and characterization of genes within these clusters, the DAVID database was utilized to elucidate the relevant biological pathways associated with the identified genes. This comprehensive approach enabled a detailed exploration of the molecular mechanisms underlying the hypothalamic response to heat stress in chickens. Results: The analysis of microarray data unveiled a comprehensive portrait of the molecular response to heat stress in chickens. Among the 43,607 initially examined probes, 593 differentially expressed genes were identified after rigorous curation involving the elimination of duplicate genes and application of a significance threshold. A network encompassing 149 genes with a minimum interaction score of 0.4 was constructed utilizing the STRING software, shedding light on the intricate interplay among these genes. Subsequent investigations honed in on genes exerting significant regulatory effects in response to heat stress, elucidating their pivotal roles in orchestrating physiological processes. Functional assessment of the networked genes revealed their involvement in diverse biological activities, including the positive regulation of T cell- mediated immunity, WNT and MAPK signaling pathways, as well as phagocytosis and natural killer (NK) cell activity. Particularly noteworthy was the observation of suppressed NK cell activity in broilers under heat stress, likely influenced by heightened corticosterone levels and subsequent inhibition of thyroid hormone secretion, thereby impacting temperature regulation and metabolism. Moreover, the elevation in blood glucose levels observed in stressed chicks may contribute to their adaptive survival response. Importantly, heat stress was observed to disrupt cellular homeostasis and molecular pathways, manifesting in alterations in biological processes such as the cell cycle. This disruption in the cell cycle may signify a novel mechanism of stress resistance in vertebrates, further underscoring the complexity of the physiological response to thermal stress in chickens. Conclusion: The analysis of gene functionality within the network underscores the intricate nature of the innate immune response and cellular demise elicited by heat stress. Notably, certain genes identified in this analysis participate in positively regulating T cell-mediated immunity, vascular endothelial growth factor (VEGF), WNT signaling, and mitogen-activated protein kinase (MAPK) pathways. Introducing a biomarker panel comprising genes, such as MAPK14, NT3, SOX2, YAP1, FGF2, LRRK1, LEF1, and TLE1, holds promise for advancing our understanding of the underlying molecular mechanisms governing stress responses in poultry. Furthermore, this approach may facilitate the discovery of novel genes whose modulation could mitigate the effects of thermal stress. [ABSTRACT FROM AUTHOR]

In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. Methods: We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE178331 and obtained the most relevant high-throughput sequenced genes for an increased permeability of vascular endothelial cells due to inflammation. We constructed two weighted gene co-expression network analysis (WGCNA) networks, and the differential expression of high-throughput sequenced genes related to endothelial cell permeability were screened from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differential genes. Their degree values were obtained from the topological properties of protein-protein interaction (PPI) networks of differential genes, and the hub genes associated with an increased endothelial cell permeability were analyzed. Reverse transcription-polymerase chain reaction (RT-PCR) and western blotting techniques were used to detect the presence of these hub genes in TNF-α induced mRNA and the protein expression in endothelial cells. Results: In total, 1,475 differential genes were mainly enriched in the cell adhesion and TNF-α signaling pathway. With TNF-α inducing an increase in the endothelial cell permeability and significantly increasing mRNA and protein expression levels, we identified three hub genes, namely PTGS2, ICAM1, and SNAI1. There was a significant difference in the high-dose TNF-α group and in the low-dose TNF-α group compared to the control group, in the endothelial cell permeability experiment (p = 0.008 vs. p = 0.02). Measurement of mRNA and protein levels of PTGS2, ICAM1, and SNAI1 by western blotting analysis showed that there was a significant impact on TNF-α and that there was a significant dose-dependent relationship (p < 0.05 vs. p < 0.01). Conclusions: The three hub genes identified through bioinformatics analyses in the present study may serve as biomarkers of increased vascular endothelial cell permeability. The findings offer valuable insights into the progress and mechanism of vascular endothelial cell permeability. [ABSTRACT FROM AUTHOR]

Aims To investigate the potential mechanisms of fatty acid metabolism (FAM)-related genes in IgA nephropathy (IgAN) and to explore its immune cell infiltration characteristic.Methods Datasets for IgAN and FAM-related genes were obtained from GEO and MSigDB database, respectively. We employed differential expression analysis and WGCNA to identify common genes. GO and KEGG analyses were performed to compare the differences between IgAN and control groups. Furthermore, LASSO logistic regression was applied to develop a predictive model based on FAM-related genes. The efficacy of this prognostic model was evaluated using ROC analysis. The infiltration of immune cells and immune-related functions were assessed with CIBERSORT tool. Finally, the identified key genes were validated in blood samples from IgAN and control patients, as well as in human mesangial cells (HMCs) following Gd-IgA stimulation using Real-time PCR.Results A total of 12 hub genes linked to FAM were identified in patients with IgAN. A predictive model consisting of four genes was conducted through COX and LASSO regression analysis, revealing AUC values that indicate a relatively strong diagnostic capability. Immune infiltration analysis indicated that various immune cells have significant associations with IgAN. Additionally, Real-time PCR assays confirmed that the expression levels of hub genes were markedly reduced in IgAN patients and in Gd-IgA treated HMCs compared to controls.Conclusion This study employed bioinformatics methods to unveiled the immune cell infiltration associated with IgAN and to explore the potential genetic connection between FAM and IgAN. This could aid in predicting the risk of IgAN and enhance both diagnosis and prognosis of this condition.

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.

ABSTRACT: 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.

Background: Schizophrenia (SCZ) is a prevalent and serious mental disorder, and the exact pathophysiology of this condition is not fully understood. In previous studies, it has been proven that ferroprotein levels are high in SCZ. It has also been shown that this inflammatory response may modify fibromodulin. Accumulating evidence indicates a strong link between metabolism and ferroptosis. Therefore, the present study aims to identify ferroptosis‐linked hub genes to further investigate the role that ferroptosis plays in the development of SCZ. Material and methods: From the GEO database, four microarray data sets on SCZ (GSE53987, GSE38481, GSE18312, and GSE38484) and ferroptosis‐linked genes were extracted. Using the prefrontal cortex expression matrix of SCZ patients and healthy individuals as the control group from GSE53987, weighted gene co‐expression network analysis (WGCNA) was performed to discover SCZ‐linked module genes. From the feed, genes associated with ferroptosis were retrieved. The intersection of the module and ferroptosis-linked genes was done to obtain the hub genes. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were conducted. The SCZ diagnostic model was established using logistic regression, and the GSE38481, GSE18312, and GSE38484 data sets were used to validate the model. Finally, hub genes linked to immune infiltration were examined. Results: A total of 13 SCZ module genes and 7 hub genes linked to ferroptosis were obtained: DECR1, GJA1, EFN2L2, PSAT1, SLC7A11, SOX2, and YAP1. The GO/KEGG/GSEA study indicated that these hub genes were predominantly enriched in mitochondria and lipid metabolism, oxidative stress, immunological inflammation, ferroptosis, Hippo signaling pathway, AMP‐activated protein kinase pathway, and other associated biological processes. The diagnostic model created using these hub genes was further confirmed using the data sets of three blood samples from patients with SCZ. The immune infiltration data showed that immune cell dysfunction enhanced ferroptosis and triggered SCZ. Conclusion: In this study, seven critical genes that are strongly associated with ferroptosis in patients with SCZ were discovered, a valid clinical diagnostic model was built, and a novel therapeutic target for the treatment of SCZ was identified by the investigation of immune infiltration.

Abstract Objective We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis. Methods Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein–protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin–eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot. Results We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells. Conclusion CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway.

目的 本研究对烟雾病患者血管样本的差异表达基因（differentially expressed genes，DEGs）进行生物信息学鉴定和分析，旨在探讨烟雾病的潜在发病机制。 方法 本研究以烟雾病和颈内动脉瘤患者大脑血管样本为研究对象。利用R语言线性模型微阵列数据（linear models for microarray data，limma）分析包对基因表达综合数据库（gene expression omnibus，GEO）中的GSE141025数据集进行分析，该数据集涵盖4例烟雾病患者和4例颈内动脉瘤患者的大脑中动脉和颞浅动脉样本各1个，共计16个样本。选择烟雾病患者的大脑中动脉、颞浅动脉及颈内动脉瘤患者的颞浅动脉共12个样本进行DEGs筛选。通过R语言功能富集分析工具包clusterProfiler，对筛选出的DEGs进行基因本体（gene ontology，GO）富集分析和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes，KEGG）通路分析。利用STRING数据库构建蛋白质-蛋白质相互作用（protein-protein interaction，PPI）网络，并使用网络可视化软件Cytoscape进行蛋白质网络的可视化和枢纽基因筛选。 结果 本研究在烟雾病患者的大脑中动脉与颞浅动脉样本间鉴定出138个DEGs，包括18个上调基因和120个下调基因。GO富集分析显示，以上DEGs在细胞外基质、受体配体活性和生长因子活性等方面显著富集，可能与烟雾病相关的血管病变和神经保护机制有关。KEGG通路分析提示，DEGs主要在酪氨酸代谢通路中富集。通过PPI网络分析，共筛选出9个枢纽基因，包括骨膜蛋白（periostin，POSTN）、脑源性神经营养因子（brain derived neurotrophic factor，BDNF）、血小板衍生生长因子受体α（platelet derived growth factor receptor alpha，PDGFRA）、Thy-1细胞表面抗原（Thy-1 cell surface antigen，THY1）、ⅩⅤ型胶原蛋白α1链（collagen type ⅩⅤ alpha 1 chain，COL15A1）、成纤维细胞生长因子7（fibroblast growth factor 7，FGF7）、光蛋白聚糖（lumican，LUM）、层粘连蛋白α2亚基（laminin subunit alpha 2，LAMA2）和RELN（reelin）。此外，上调基因delta样典型Notch配体4（delta like canonical Notch ligand 4，DLL4）在本研究中首次被发现可能在烟雾病中扮演重要角色，或与烟雾病的病理性血管生成有关。 结论 细胞外基质、生长因子及其受体的表达失调等可能参与烟雾病的发病过程。DEGs分析筛选出的枢纽基因（POSTN、BDNF、PDGFRA、THY1、COL15A1、FGF7、LUM、LAMA2、RELN）以及DLL4可能在烟雾病的病理形成过程中发挥作用。 Abstract: Objective In this study, bioinformatics analysis of differentially expressed genes (DEGs) between blood vessel samples of patients with moyamoya disease were conducted to explore the potential pathogenesis of moyamoya disease. Methods The cerebral blood vessel samples from patients with moyamoya disease and internal carotid aneurysm were taken as research subjects. The linear models for microarray data (limma) package in R was used to analyze the GSE141025 dataset downloaded from the gene expression omnibus (GEO). The dataset included samples from 4 moyamoya disease patients and 4 internal carotid aneurysm patients, providing a total of 16 samples including one middle cerebral artery and one superficial temporal artery from each patient. A total of 12 samples of middle cerebral artery and superficial temporal artery from moyamoya disease patients and superficial temporal artery from internal carotid aneurysm patients were selected for DEGs screening. Gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis on the DEGs were conducted using the clusterProfiler package in R. The STRING database was utilized to construct the protein-protein interaction (PPI) network, and the network visualization software Cytoscape was used to visualize the protein network and to filter hub genes. Results In the comparison between middle cerebral artery and superficial temporal artery samples from patients with moyamoya disease, 138 DEGs were identified, including 18 up-regulated genes and 120 down-regulated genes. GO enrichment analysis revealed that these DEGs were significantly enriched in extracellular matrix, receptor ligand activity, and growth factor activity, suggesting their potential involvement in vasculopathy and neuroprotective mechanisms associated with moyamoya disease. KEGG pathway analysis indicated that DEGs were primarily enriched in the tyrosine metabolic pathway. Through PPI network analysis, 9 hub genes were filtered, including periostin (POSTN), brain derived neurotrophic factor (BDNF), platelet derived growth factor receptor alpha (PDGFRA), Thy-1 cell surface antigen (THY1), collagen type ⅩⅤ alpha 1 chain (COL15A1), fibroblast growth factor 7 (FGF7), lumican (LUM), laminin subunit alpha 2 (LAMA2) and reelin (RELN). Additionally, the up-regulated gene delta like canonical Notch ligand 4 (DLL4) was firstly founded possibly associated with pathological angiogenesis of moyamoya disease in this study, suggesting that it may play a significant role in moyamoya disease. Conclusions The study indicates that the dysregulation of the expression of extracellular matrix components, growth factors and their receptors might be involved in the pathogenesis of moyamoya disease. The hub genes selected by DEGs analysis (POSTN, BDNF, PDGFRA, THY1, COL15A1, FGF7, LUM, LAMA2, RELN) and DLL4 may play a role in the pathological process of moyamoya disease.

The Fucaceae family of marine brown algae includes Ascophyllum nodosum. Fucosterol (FSL) is a unique bioactive component that was identified through GC-MS analysis of the hydroalcoholic extract of A. nodosum. Fucosterol's mechanism of action towards hepatocellular cancer was clarified using network pharmacology and docking study techniques. The probable target gene of FSL has been predicted using the TargetNet and SwissTargetPred databases. GeneCards and the DisGNet database were used to check the targeted genes of FSL. By using the web programme Venny 2.1, the overlaps of FSL and HCC disease demonstrated that 18 genes (1.3%) were obtained as targeted genes Via the STRING database, a protein–protein interaction (PPI) network with 18 common target genes was constructed. With the aid of CytoNCA, hub genes were screened using the Cytoscape software, and the targets' hub genes were exported into the ShinyGo online tool for study of KEGG and gene ontology enrichment. Using the software AutoDock, a hub gene molecular docking study was performed. Ten genes, including AR, CYP19A1, ESR1, ESR2, TNF, PPARA, PPARG, HMGCR, SRC, and IGF1R, were obtained. The 10 targeted hubs docked with FSL successfully. The active components FSL of ASD, the FSL, are engaged in fatty liver disease, cancer pathways, and other signalling pathways, which could prove beneficial for the management of HCC. [ABSTRACT FROM AUTHOR]

Simple Summary: The reproduction of goats is a highly complex and dynamic process of life regulation, which is synergistically regulated by various aspects such as central nervous system regulation, reproductive system development, oocyte maturation, and fertilized egg development. In goats, there is still an urgent need to explore marker genes related to reproductive performance. This study identified the RPL4 gene as a key marker gene related to reproductive capacity in goat oocytes through various bioinformatics analysis methods including WGCNA, PPI network analysis, and differential gene expression analysis. The results reveal that the RPL4 gene in oocytes has the potential to serve as a biological marker for determining the reproductive rate of goats, deepening our understanding of the regulatory mechanisms of goat reproductive performance. Reproduction in goats is a highly complex and dynamic process of life regulation, involving coordinated regulation from various aspects such as central nervous system regulation, reproductive system development, oocyte maturation, and fertilized egg development. In recent years, researchers have identified numerous genes associated with goat reproductive performance through high-throughput sequencing, single-cell sequencing, gene knockout, and other techniques. However, there is still an urgent need to explore marker genes related to goat reproductive performance. In this study, a single-cell RNA sequencing dataset of oocytes (GSE136005) was obtained from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to identify modules highly correlated with goat litter size. Through gene function enrichment analysis, it was found that genes within the modules were mainly enriched in adhesive junctions, cell cycle, and other signaling pathways. Additionally, the top 30 hub genes with the highest connectivity in WGCNA were identified. Subsequently, using Protein–Protein Interaction (PPI) network analysis, the top 30 genes with the highest connectivity within the modules were identified. The intersection of hub genes, key genes in the PPI network, and differentially expressed genes (DEGs) led to the identification of the RPL4 gene as a key marker gene associated with reproductive capacity in goat oocytes. Overall, our study reveals that the RPL4 gene in oocytes holds promise as a biological marker for assessing goat litter size, deepening our understanding of the regulatory mechanisms underlying goat reproductive performance. [ABSTRACT FROM AUTHOR]

Objective: We aimed to screen novel gene signatures for ovarian cancer (OC) and explore the role of biomarkers in OC via regulating pyroptosis using bioinformatics analysis. Methods: Differentially expressed genes (DEGs) of OC were screened from GSE12470 and GSE16709 datasets. Hub genes were determined from protein–protein interaction networks after bioinformatics analysis. The role of Centromeric protein M (CENPM) in OC was assessed by subcutaneous tumor experiment using hematoxylin–eosin and immunohistochemical staining. Tumor metastasis was evaluated by detecting epithelial-mesenchymal transition-related proteins. The proliferation, migration, and invasion were determined using cell counting kit and transwell assay. Enzyme-linked immunosorbent assay was applied to measure inflammatory factors. The mRNA and protein expression were detected using real-time quantitative PCR and western blot. Results: We determined 9 hub genes (KIFC1, PCLAF, CDCA5, KNTC1, MCM3, OIP5, CENPM, KIF15, and ASF1B) with high prediction value for OC. In SKOV3 and A2780 cells, the expression levels of hub genes were significantly up-regulated, compared with normal ovarian cells. CENPM was selected as a key gene. Knockdown of CENPM suppressed proliferation, migration, and invasion of OC cells. Subcutaneous tumor experiment revealed that CENPM knockdown significantly suppressed tumor growth and metastasis. Additionally, pyroptosis was promoted in OC cells and xenograft tumors after CENPM knockdown. Furthermore, CENPM knockdown activated cGAS-STING pathway and the pathway inhibitor reversed the inhibitory effect of CENPM knockdown on viability, migration, and invasion of OC cells. Conclusion: CENPM was a novel biomarker of OC, and knockdown of CENPM inhibited OC progression by promoting pyroptosis and activating cGAS-STING pathway. [ABSTRACT FROM AUTHOR]

Abdominal subcutaneous fat deposition (ASFD) is not only related to meat quality in the pig industry but also to human health in medicine. It is of great value to elucidate the potential molecular mechanisms of ASFD. The present study aims to identify obese-specific biomarkers and key pathways correlated with ASFD in pigs. The ASF-related mRNA expression dataset GSE136754 was retrieved from the Gene Expression Omnibus (GEO) database and systematically analyzed using a comprehensive bioinformatics method. A total of 565 differentially expressed genes (DEGs) were identified between three obese and three lean pigs, and these DEGs were mainly involved in the p53 signaling pathway, MAPK signaling pathway and fatty acid metabolism. A protein-protein interaction (PPI) network, consisting of 540 nodes and 1,065 edges, was constructed, and the top ten genes with the highest degree scores—ABL1, HDAC1, CDC42, HDAC2, MRPS5, MRPS10, MDM2, JUP, RPL7L1 and UQCRFS1—were identified as hub genes in the whole PPI network. Especially HDAC1, MDM2, MRPS10 and RPL7L1 were identified as potential robust obese-specific biomarkers due to their significant differences in single gene expression levels and high ROC area; this was further verified by quantitative real-time PCR (qRT-PCR) on abdominal subcutaneous fat samples from obese-type (Saba) and lean-type (Large White) pigs. Additionally, a mRNA-miRNA-lncRNA ceRNA network consisting of four potential biomarkers, 15 miRNAs and 51 lncRNAs was established, and two targeted lncRNAs with more connections, XIST and NEAT1, were identified as potentially important regulatory factors. The findings of this study may provide novel insights into the molecular mechanism involved in ASFD. [ABSTRACT FROM AUTHOR]

Background: Patients with coronavirus disease 2019 (COVID-19) were vulnerable to venous thromboembolism (VTE), which further increases the risk of unfavorable outcomes. However, neither genetic correlations nor shared genes underlying COVID-19 and VTE are well understood. Objective: This study aimed to characterize genetic correlations and common pathogenic mechanisms between COVID-19 and VTE. Methods: We used linkage disequilibrium score (LDSC) regression and Mendelian Randomization (MR) analysis to investigate the genetic associations and causal effects between COVID-19 and VTE, respectively. Then, the COVID-19 and VTE-related datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed by bioinformatics and systems biology approaches with R software, including weighted gene co-expression network analysis (WGCNA), enrichment analysis, and single-cell transcriptome sequencing analysis. The miRNA–genes and transcription factor (TF)–genes interaction networks were conducted by NetworkAnalyst. We performed the secondary analysis of the ATAC-seq and Chip-seq datasets to address the epigenetic-regulating relationship of the shared genes. Results: This study demonstrated positive correlations between VTE and COVID-19 by LDSC and bidirectional MR analysis. A total of 26 potential shared genes were discovered from the COVID-19 dataset (GSE196822) and the VTE dataset (GSE19151), with 19 genes showing positive associations and 7 genes exhibiting negative associations with these diseases. After incorporating two additional datasets, GSE164805 (COVID-19) and GSE48000 (VTE), two hub genes TP53I3 and SLPI were identified and showed up-regulation and diagnostic capabilities in both illnesses. Furthermore, this study illustrated the landscapes of immune processes in COVID-19 and VTE, revealing the downregulation in effector memory CD8+ T cells and activated B cells. The single-cell sequencing analysis suggested that the hub genes were predominantly expressed in the monocytes of COVID-19 patients at high levels. Additionally, we identified common regulators of hub genes, including five miRNAs (miR-1-3p, miR-203a-3p, miR-210-3p, miR-603, and miR-124-3p) and one transcription factor (RELA). Conclusions: Collectively, our results highlighted the significant correlations between COVID-19 and VTE and pinpointed TP53I3 and SLPI as hub genes that potentially link the severity of both conditions. The hub genes and their common regulators might present an opportunity for the simultaneous treatment of these two diseases. [ABSTRACT FROM AUTHOR]

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BackgroundPulmonary 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.MethodsWe 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.ResultsWe 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.ConclusionAHR, 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.

BackgroundCardioembolic 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.MethodsWe 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.ResultsFunctional 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.ConclusionIn 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.

BackgroundThe ongoing global health crisis of COVID-19, and particularly the challenges posed by recurrent infections of the Omicron variant, have significantly strained healthcare systems worldwide. There is a growing body of evidence indicating an increased susceptibility to Omicron infection in patients suffering from Acute Kidney Injury (AKI). However, the intricate molecular interplay between AKI and Omicron variant of COVID-19 remains largely enigmatic.MethodsThis study employed a comprehensive analysis of human RNA sequencing (RNA-seq) and microarray datasets to identify differentially expressed genes (DEGs) associated with Omicron infection in the context of AKI. We engaged in functional enrichment assessments, an examination of Protein-Protein Interaction (PPI) networks, and advanced network analysis to elucidate the cellular signaling pathways involved, identify critical hub genes, and determine the relevant controlling transcription factors and microRNAs. Additionally, we explored protein-drug interactions to highlight potential pharmacological interventions.ResultsOur investigation revealed significant DEGs and cellular signaling pathways implicated in both Omicron infection and AKI. We identified pivotal hub genes, including EIF2AK2, PLSCR1, GBP1, TNFSF10, C1QB, and BST2, and their associated regulatory transcription factors and microRNAs. Notably, in the murine AKI model, there was a marked reduction in EIF2AK2 expression, in contrast to significant elevations in PLSCR1, C1QB, and BST2. EIF2AK2 exhibited an inverse relationship with the primary AKI mediator, Kim-1, whereas PLSCR1 and C1QB demonstrated strong positive correlations with it. Moreover, we identified potential therapeutic agents such as Suloctidil, Apocarotenal, 3′-Azido-3′-deoxythymidine, among others. Our findings also highlighted a correlation between the identified hub genes and diseases like myocardial ischemia, schizophrenia, and liver cirrhosis. To further validate the credibility of our data, we employed an independent validation dataset to verify the hub genes. Notably, the expression patterns of PLSCR1, GBP1, BST2, and C1QB were consistent with our research findings, reaffirming the reliability of our results.ConclusionOur bioinformatics analysis has provided initial insights into the shared genetic landscape between Omicron COVID-19 infections and AKI, identifying potential therapeutic targets and drugs. This preliminary investigation lays the foundation for further research, with the hope of contributing to the development of innovative treatment strategies for these complex medical conditions.

Abstract Background and objective In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. Materials and Methods Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes. Result The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value 0.90, which showed excellent predictive value, were thus considered hub genes. Conclusion The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Abstract Background Intramuscular fat (IMF) is an important factor in meat quality, and triglyceride (TG) and Phospholipids (PLIP), as the main components of IMF, are of great significance to the improvement of meat quality. Results In this study, we used 30 RNA sequences generated from the transcriptome of chicken breast muscle tissues at different developmental stages to construct a gene expression matrix to map RNA sequence reads to the chicken genome and identify the transcript of origin. We used weighted gene co-expression network analysis (WGCNA) and identified 27 co-expression modules, 10 of which were related to TG and PLIP. We identified 150 highly-connected hub genes related to TG and PLIP, respectively, which were found to be mainly enriched in the adipocytokine signaling pathway, MAPK signaling pathway, mTOR signaling pathway, FoxO signaling pathway, and TGF-beta signaling pathway. Additionally, using the BioMart database, we identified 134 and 145 candidate genes related to fat development in the TG-related module and PLIP-related module, respectively. Among them, RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 were identified as candidate genes related to fat development and highly-connected hub genes in the module, suggesting that these ten genes may be important candidate genes affecting IMF deposition. Conclusions RPS6KB1, BRCA1, CDK1, RPS3, PPARGC1A, ACSL1, NDUFAB1, NDUFA9, ATP5B and PRKAG2 may be important candidate genes affecting IMF deposition. The purpose of this study was to identify the co-expressed gene modules related to chicken IMF deposition using WGCNA and determine key genes related to IMF deposition, so as to lay a foundation for further research on the molecular regulation mechanism underlying chicken fat deposition.

Abstract Melanoma is a malignant skin tumor. This study aimed to explore and assess the effect of novel biomarkers on the progression of melanoma. Differently expressed genes (DEGs) were screened from GSE3189 and GSE46517 datasets of Gene Expression Omnibus database using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted based on the identified DEGs. Hub genes were identified and assessed using protein–protein interaction networks, principal component analysis, and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction was employed to measure the mRNA expression levels. TIMER revealed the association between aldehyde dehydrogenase 2 (ALDH2) and tumor immune microenvironment. The viability, proliferation, migration, and invasion were detected by cell counting kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, and transwell assays. Total 241 common DEGs were screened out from GSE3189 and GSE46517 datasets. We determined 6 hub genes with high prediction values for melanoma, which could distinguish tumor samples from normal samples. ALDH2, ADH1B, ALDH3A2, DPT, EPHX2, and GATM were down-regulated in A375 and SK-MEL-2 cells, compared with the human normal melanin cell line (PIG1 cells). ALDH2 was selected as the candidate gene in this research, presenting a high diagnostic and predictive value for melanoma. ALDH2 had a positive correlation with the infiltrating levels of immune cells in melanoma microenvironment. Overexpression of ALDH2 inhibited cell viability, proliferation, migration, and invasion of A375/SK-MEL-2 cells. ALDH2 is a new gene biomarker of melanoma, which exerts an inhibitory effect on melanoma.