RNA-seq analyses on gametogenic tissues of alfalfa (Medicago sativa) revealed plant reproduction- and ploidy-related genes.

BACKGROUND: In alfalfa (Medicago sativa), the coexistence of interfertile subspecies (i.e. sativa, falcata and coerulea) characterized by different ploidy levels (diploidy and tetraploidy) and the occurrence of meiotic mutants capable of producing unreduced (2n) gametes, have been efficiently combined for the establishment of new polyploids. The wealth of agronomic data concerning forage quality and yield provides a thorough insight into the practical benefits of polyploidization. However, many of the underlying molecular mechanisms regarding gene expression and regulation remained completely unexplored. In this study, we aimed to address this gap by examining the transcriptome profiles of leaves and reproductive tissues, corresponding to anthers and pistils, sampled at different time points from diploid and tetraploid Medicago sativa individuals belonging to progenies produced by bilateral sexual polyploidization (dBSP and tBSP, respectively) and tetraploid individuals stemmed from unilateral sexual polyploidization (tUSP). RESULTS: Considering the crucial role played by anthers and pistils in the reduced and unreduced gametes formation, we firstly analyzed the transcriptional profiles of the reproductive tissues at different stages, regardless of the ploidy level and the origin of the samples. By using and combining three different analytical methodologies, namely weighted-gene co-expression network analysis (WGCNA), tau (τ) analysis, and differentially expressed genes (DEGs) analysis, we identified a robust set of genes and transcription factors potentially involved in both male sporogenesis and gametogenesis processes, particularly in crossing-over, callose synthesis, and exine formation. Subsequently, we assessed at the same floral stage, the differences attributable to the ploidy level (tBSP vs. dBSP) or the origin (tBSP vs. tUSP) of the samples, leading to the identification of ploidy and parent-specific genes. In this way, we identified, for example, genes that are specifically upregulated and downregulated in flower buds in the comparison between tBSP and dBSP, which could explain the reduced fertility of the former compared to the latter materials. CONCLUSIONS: While this study primarily functions as an extensive investigation at the transcriptomic level, the data provided could represent not only a valuable original asset for the scientific community but also a fully exploitable genomic resource for functional analyses in alfalfa.

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). The WGCNA analysis showed the six schizophrenia-related modules containing 12,888 genes. The functional enrichment analyses indicated that the DEGs were highly involved in immune-related processes and functions. The immune cell infiltration analysis with the CIBERSORT algorithm revealed 12 types of immune cells that were significantly different between schizophrenia subjects and controls. Additionally, by intersecting DEGs, WGCNA module genes, and an immune gene set obtained from online databases, 151 schizophrenia-associated immune-related genes were obtained. Moreover, machine learning algorithms including LASSO and Random Forest were employed to further screen out 17 signature genes, including GRIN1, P2RX7, CYBB, PTPN4, UBR4, LTF, THBS1, PLXNB3, PLXNB1, PI15, RNF213, CXCL11, IL7, ARHGAP10, TTR, TYROBP, and EIF4A2. Then, SVM-RFE was added, and together with LASSO and Random Forest, a hub gene (EIF4A2) out of the 17 signature genes was revealed. Lastly, in a schizophrenia rat model, the EIF4A2 expression levels were reduced in the model rat brains in a brain-regional dependent manner, but can be reversed by risperidone. In conclusion, by using various bioinformatic and biological methods, this study found 17 immune-related signature genes and a hub gene of schizophrenia that might be potential diagnostic biomarkers and therapeutic targets of schizophrenia.

Altered expression of a raspberry homologue of VRN1 is associated with disruption of dormancy induction and misregulation of subsets of dormancy-associated genes.

Winter dormancy is a key process in the phenology of temperate perennials. Climate change is severely impacting its course leading to economic losses in agriculture. A better understanding of the underlying mechanisms, as well as the genetic basis of the different responses, are necessary for the development of climate-resilient cultivars. This study aims to provide an insight into winter dormancy in red raspberry (Rubus idaeus L). We report the transcriptomic profiles during dormancy in two raspberry cultivars with contrasting responses. The cultivar 'Glen Ample' showed a typical perennial phenology, whereas 'Glen Dee' registered consistent dormancy dysregulation, exhibiting active growth and flowering out of season. RNA-seq combined with weighted gene co-expression network analysis (WGCNA) highlighted gene clusters in both genotypes that exhibited time-dependent expression profiles. Functional analysis of 'Glen Ample' gene clusters highlighted the significance of the cell and structural development prior to dormancy entry as well the role of genetic and epigenetic processes such as RNAi and DNA methylation in regulating gene expression. Whereas dormancy release in 'Glen Ample' was associated with upregulation of transcripts associated with the resumption of metabolism, nucleic acid biogenesis and processing signal response pathways. Many of the processes occurring in 'Glen Ample' were dysregulated in 'Glen Dee' and twenty-eight transcripts exhibiting time-dependent expression in 'Glen Ample' that also had an Arabidopsis homologue were not found in 'Glen Dee'. These included a gene with homology to Arabidopsis VRN1 (RiVRN1.1) that exhibited a sharp decline in expression following dormancy induction in 'Glen Ample'. Characterisation of the gene region in the 'Glen Dee' genome revealed two large insertions upstream of the ATG start codon. We propose that expression below detection level of a specific VRN1 homologue in 'Glen Dee' causes dormancy misregulation as a result of inappropriate expression of a subset of genes that are directly or indirectly regulated by RiVRN1.1.

A useful mTORC1 signaling-related RiskScore model for the personalized treatment of osteosarcoma patients by using the bulk RNA-seq analysis.

AIMS: This research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature. BACKGROUND: The mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear. OBJECTIVE: The datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected. METHODS: The mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package. RESULTS: The mTORC1 score and WGCNA with ß = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability. CONCLUSION: We developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature.

Weighted gene co-expression network analysis identified GBP2 connected to PPARα activity and liver cancer.

Liver cancer is the fourth leading cause of cancer-related deaths with a steadily increasing rate worldwide, as a well-known hallmark of liver cancer, metabolic alterations are related to liposomal changes, a common characteristic of primary liver cancers based on recent lipidomics studies. Peroxisome proliferator-activated receptor α (PPARα) is a ligand-activated transcription factor with important lipid homeostasis function, therefore we aimed to understand the molecular mechanisms and pathways that activate PPARα after using PPAR-α agonist WY-14643 and identify candidate biomarkers related to PPARα activity and evaluate their effects in liver cancer. The data from differently expressed genes (DEGs) between liver cancer tissue from obese subjects alone and liver tissue after treatment were evaluated by DESeq2 and module genes were analyzed using weighted gene co-expression network analysis (WGCNA). Final candidate genes were identified by intersecting genes among highly ranked DEGs and the brown module, which demonstrated a significant negative correlation with drug treatments. We conducted a protein-protein interaction network, and KEGG enrichment analysis, and core hub genes (CD40, CXCL9, CXCL10, TNFSF14, GBP2, GBP3, APOL3, CLDN1) were identified using the cyto-hubba plugin, among them we focused on GBP2 that plays key roles in oncogenesis and evaluate its expressional with clinical outcomes. In conclusion, the WGCNA-based co-expression network identified GBP2 as one of the hub genes with a negative relation with PPARα agonist treatments. higher expression of GBP2 was closely associated with HCC progression. Therefore, GBP2 might be a potential candidate for the study of PPARα activity in HCC.

Identification of gene co-expression modules from zebrafish brain data: Applications in psychiatry illustrated through alcohol-related traits.

Cumulative evidence suggests that zebrafish is a useful model in psychiatric research. Weighted Gene Co-expression Network Analysis (WGCNA) enables the reduction of genome-wide expression data to modules of highly co-expressed genes, which are hypothesized to interact within molecular networks. In this study, we first applied WGCNA to zebrafish brain expression data across different experimental conditions. Then, we characterized the different co-expression modules by gene-set enrichment analysis and hub gene-phenotype association. Finally, we analyzed association of polygenic risk scores (PRSs) based on genes of some interesting co-expression modules with alcohol dependence in 524 patients and 729 controls from Galicia, using competitive tests. Our approach revealed 34 co-expression modules in the zebrafish brain, with some showing enrichment in human synaptic genes, brain tissues, or brain developmental stages. Moreover, certain co-expression modules were enriched in psychiatry-related GWAS and comprised hub genes associated with psychiatry-related traits in both human GWAS and zebrafish models. Expression patterns of some co-expression modules were associated with the tested experimental conditions, mainly with substance withdrawal and cold stress. Notably, a PRS based on genes from co-expression modules exclusively associated with substance withdrawal in zebrafish showed a stronger association with human alcohol dependence than PRSs based on randomly selected brain-expressed genes. In conclusion, our analysis led to the identification of co-expressed gene modules that may model human brain gene networks involved in psychiatry-related traits. Specifically, we detected a cluster of co-expressed genes whose expression was exclusively associated with substance withdrawal in zebrafish, which significantly contributed to alcohol dependence susceptibility in humans.

Polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA) are associated with the PI3K-AKT pathway activation.

Aims: We aimed to elucidate the mechanism leading to polycystic ovarian syndrome (PCOS) and recurrent spontaneous abortion (RSA). Background: PCOS is an endocrine disorder. Patients with RSA also have a high incidence rate of PCOS, implying that PCOS and RSA may share the same pathological mechanism. Objective: The single-cell RNA-seq datasets of PCOS (GSE168404 and GSE193123) and RSA GSE113790 and GSE178535) were downloaded from the Gene Expression Omnibus (GEO) database. Methods: Datasets of PSCO and RSA patients were retrieved from the Gene Expression Omnibus (GEO) database. The "WGCNA" package was used to determine the module eigengenes associated with the PCOS and RSA phenotypes and the gene functions were analyzed using the "DAVID" database. The GSEA analysis was performed in "clusterProfiler" package, and key genes in the activated pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Real-time quantitative PCR (RT-qPCR) was conducted to determine the mRNA level. Cell viability and apoptosis were measured by cell counting kit-8 (CCK-8) and flow cytometry, respectively. Results: The modules related to PCOS and RSA were sectioned by weighted gene co-expression network analysis (WGCNA) and positive correlation modules of PCOS and RSA were all enriched in angiogenesis and Wnt pathways. The GSEA further revealed that these biological processes of angiogenesis, Wnt and regulation of cell cycle were significantly positively correlated with the PCOS and RSA phenotypes. The intersection of the positive correlation modules of PCOS and RSA contained 80 key genes, which were mainly enriched in kinase-related signal pathways and were significant high-expressed in the disease samples. Subsequently, visualization of these genes including PDGFC, GHR, PRLR and ITGA3 showed that these genes were associated with the PI3K-AKT signal pathway. Moreover, the experimental results showed that PRLR had a higher expression in KGN cells, and that knocking PRLR down suppressed cell viability and promoted apoptosis of KGN cells. Conclusion: This study revealed the common pathological mechanisms between PCOS and RSA and explored the role of the PI3K-AKT signaling pathway in the two diseases, providing a new direction for the clinical treatment of PCOS and RSA.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Identification and Validation of MYADM as a Novel Prognostic Marker Related to EMT in ESCC.

Background: Esophageal squamous cell carcinoma (ESCC), one of the most aggressive gastrointestinal malignancies, remains an enormous challenge in terms of medical treatment and prognostic improvement. Based on the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases in R language, the myeloid-associated differentiation marker (MYADM) was confirmed using bioinformatics analysis and experimental verification. MYADM is upregulated in multiple cancer types; however, the oncogenic mechanism by which MYADM promotes ESCC remains largely unknown. Methods: In the present study, we used weighted gene coexpression network analysis to filter four hub genes (AKAP12, ITGA1, JAM2, and MYADM) in GSE45670 and GSE23400 that are related to the malignant progression of ESCC. Transcription factors and target miRNAs of the hub genes were predicted using the TarBase and JASPRAR databases, respectively, and a regulatory network was established. MYADM was selected based on the analysis of expression differences and prognostic value in ESCC. Next, we confirmed the level of MYADM in ESCC samples using immunohistochemistry of the tissue microarray. The molecular mechanisms of MYADM were further elucidated by experimental analyses, including Transwell assays, wound healing assays, and CCK8. Results: The correlation between MYADM levels and the clinical data of patients with ESCC was confirmed, including tumor differentiation, the node and metastasis stage, T stage, lymphatic metastasis, and postoperative distant metastasis. MYADM was significantly upregulated in ESCC and positively correlated with overall survival. MYADM induced cell proliferation, migration, invasion, and wound healing via the epithelial to mesenchymal transition (EMT) pathway in multiple experiments. Moreover, our results supported the hypothesis that MYADM promotes EMT during paclitaxel resistance. Conclusion: MYADM is closely correlated with ESCC progression, metastasis, and paclitaxel resistance and could be regarded as a novel biomarker and therapeutic target for ESCC patients.

Brief research report: WGCNA-driven identification of histone modification genes as potential biomarkers in AQP4-Associated optic neuritis.

Introduction: Neuromyelitis Optica spectrum disorder (NMOSD) is an autoimmune disease characterized by anti-aquaporin-4 (AQP4) auto-antibodies. The discovery of antibodies AQP4 and myelin oligodendrocyte glycoprotein (MOG) has expanded our understanding of the pathogenesis of neuromyelitis optica. However, the molecular mechanisms underlying the disease, particularly AQP4-associated optic neuritis (AQP4-ON), remain to be fully elucidated. Methods: In this study, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the transcriptomic profiles of peripheral blood samples from patients with AQP4-ON and MOG-positive optic neuritis (MOG-ON), compared to healthy controls. Results: WGCNA revealed a brown module (ME brown) strongly associated with AQP4-ON, which correlated positively with post-onset visual acuity decline. A total of 132 critical genes were identified, mainly involved in histone modification and microtubule dynamics. Notably, genes HDAC4, HDAC7, KDM6A, and KDM5C demonstrated high AUC values in ROC analysis, indicating their potential as biomarkers for AQP4-ON. Conclusion: Our findings provide novel insights into the molecular signature of AQP4-ON and highlight the potential of systems biology approaches in identifying biomarkers for NMOSD. The identified histone modification genes warrant further investigation for their role in disease pathogenesis and as therapeutic targets.

Profiling of phenolic composition in camellia oil and its correlative antioxidant properties analysis.

Less research has been conducted on the association between camellia oil's (CO) phenolic composition and antioxidant capability. In this study, the phenolic profile of CO and its connection to antioxidant capacity were examined utilizing a combination of widely-targeted phenolic metabolomics and multivariate statistical analysis. A total of 751 phenolics were discovered. The WGCNA was used to link phenols to antioxidants, yielding 161 antioxidant-related phenols from the blue module. In response to several antioxidant assays, 59 (FRAP), 59 (DPPH), and 53 (ABTS) phenolics were identified as differential phenolic markers (DPMs). Further stepwise multiple linear regression revealed six DPMs that substantially influenced the antioxidant capacities. Nine metabolic pathways and their associated network mechanisms for the most significant phenolics were developed. This study sheds light on the phenolic content of CO, elucidates their role in antioxidant activity, and lays the groundwork for improving extraction techniques and generating improved product.

CD2 glycoprotein and CD44 structure and prevention of diabetes nephropathy: Central characteristics of related genes based on WGCNA and PPI.

Diabetic nephropathy (DN) is a prevalent complication of diabetes mellitus, characterized by complex pathogenesis that involves numerous molecules and signaling pathways. Among these, CD2 glycoprotein and CD44 play pivotal roles in cell adhesion, signal transduction, and inflammatory responses, potentially contributing significantly to the onset and progression of DN. This study aimed to investigate the central features of CD2 glycoprotein and CD44 in preventing diabetic nephropathy. To achieve this, kidney tissue sample data from DN patients were sourced from a public gene expression database. The roles of CD2 glycoprotein and CD44 within the PPI network were then analyzed, focusing on their interactions with other related genes. WGCNA analysis identified several significant gene modules associated with DN, including CD2 glycoprotein and CD44. PPI network analysis showed that these two proteins had a high degree of connectivity in the network, suggesting that they may be central regulatory molecules of DN. Further functional enrichment analysis revealed the potentially important role of CD2 glycoprotein and CD44 in diabetic nephropathy.

Ferulic Acid Methyl Ester Attenuates Cerebral Ischemia-Reperfusion Injury in Rats by Modulating PI3K/HIF-1α/VEGF Signaling Pathway.

Background: Cerebral ischaemia-reperfusion injury (CIRI) could worsen the inflammatory response and oxidative stress in brain tissue. According to previous studies, ferulic acid methyl ester (FAME), as the extract with the strongest comprehensive activity in the traditional Chinese medicine Huang Hua oil dot herb, has significant anti-oxidative stress and neuroprotective functions, and can effectively alleviate CIRI, but its mechanism of action is still unclear. Methods: Firstly, the pharmacological effects of FAME were investigated by in vitro oxidative stress and inflammatory experiments. Secondly, evaluate the therapeutic effects of FAME in the treatment of CIRI by brain histopathological staining and cerebral infarct area by replicating the in vivo MACO model. Thirdly, RNA-Seq and network pharmacology were utilized to predict the possible targets and mechanisms of FAME for CIRI at the molecular level. Finally, the expression of key target proteins, as well as the key regulatory relationships were verified by molecular docking visualization, Western Blotting and immunohistochemistry. Results: The results of in vitro experiments concluded that FAME could significantly reduce the content of TNF-α, IL-1ß and ROS, inhibiting COX-2 and iNOS protein expression in cells(p<0.01). FAME was demonstrated to have anti-oxidative stress and anti-inflammatory effects. The results of in vivo experiments showed that after the administration of FAME, the area of cerebral infarction in rats with CIRI was reduced, the content of Bcl-2 and VEGF was increased(p<0.05). Network pharmacology and RNA-Seq showed that the alleviation of CIRI by FAME may be through PI3K-AKT and HIF-1 signaling pathway. Enhanced expression of HIF-1α, VEGF, p-PI3K, p-AKT proteins in the brain tissues of rats in the FAME group was verified by molecular docking and Western Blotting. Conclusion: FAME possesses significant anti-inflammatory and anti-oxidative stress activities and alleviates CIRI through the PI3K/HIF-1α/VEGF signaling pathway.

Identification of potential diagnostic biomarkers and therapeutic targets in patients with hypoxia pulmonary hypertension.

BACKGROUND: Pulmonary hypertension is a serious disease. Emerging studies have shown that M2 macrophages play an essential role in pulmonary hypertension; however, their mechanism of action is uncertain. METHODS: Four GEO datasets were downloaded. The differentially expressed genes (DEGs) were obtained using the limma package. Simultaneously, the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and weighted gene co-expression network analysis (WGCNA) were used to get the information about M2 macrophage-related modules. Potential key genes were obtained by intersecting DEGs with M2 macrophage-related module genes (M2MRGs), and finally the area under the curve (AUC) was calculated. Rats were exposed to hypoxia condition (10 % O2) for 4 weeks to induce PH. Subsequently, potential key genes with AUC>0.7 were analyzed by quantitative real-time polymerase chain reaction and Western blot using normoxia and hypoxia rat lungs. We knocked down EPHA3 in Raw264.7 cells and detected the protein expression of M2 macrophage markers including arginase 1 (ARG1) and interleukin 10 (IL-10), phospho-protein kinase B (P-Akt), and protein kinase B (Akt) to explore the downstream pathways of EPHA3. RESULTS: Seven potential hub genes were detected by intersecting M2MRGs and DEGs. Six genes with AUC values above 0.7 were used for further exploration. The expression of EPHA3 mRNA and protein was significantly more upregulated in rats with hypoxia than in rats with normoxia. The expression levels of IL10, ARG1, and P-Akt/Akt decreased after knocking down EPHA3. CONCLUSIONS: This study suggested that the activation of the P-Akt/Akt signaling pathway promoted by EPHA3 played an essential role in the progression of pulmonary hypertension.

Identification and Mendelian randomization validation of pathogenic gene biomarkers in obstructive sleep apnea.

Background: By 2020, obstructive sleep apnea (OSA), a prevalent respiratory disorder, had affected 26.6-43.2% of males and 8.7-27.8% of females worldwide. OSA is associated with conditions such as hypertension, diabetes, and tumor progression; however, the precise underlying pathways remain elusive. This study aims to identify genetic markers and molecular mechanisms of OSA to improve understanding and treatment strategies. Methods: The GSE135917 dataset related to OSA was obtained from the GEO database. Differentially expressed genes (DEGs) were subsequently identified. Weighted gene co-expression network analysis (WGCNA) was conducted to pinpoint disease-associated genes. The intersection of these data enabled the identification of potential diagnostic DEGs. Further analyses included Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment studies, exploration of protein-protein interactions based on these genes, and an examination of immune infiltration. Mendelian randomization was employed to validate core genes against the Genome-Wide Association Study database. Results: A total of 194 DEGs were identified in this study. WGCNA network analysis highlighted 2,502 DEGs associated with OSA. By intersecting these datasets, 53 diagnostic DEGs primarily involved in metabolic pathways were identified. Significant alterations were observed in immune cell populations, including memory B cells, plasma cells, naive CD4 T cells, M0 macrophages, and activated dendritic cells. CETN3, EEF1E1, PMM2, GTF2A2, and RRM2 emerged as hub genes implicated in the pathogenesis. A line graph model provides diagnostic insights. Mendelian randomization analysis confirmed a causal link between CETN3 and GTF2A2 with OSA. Conclusion: Through WGCNA, this analysis uncovered significant genetic foundations of OSA, identifying 2,502 DEGs and 194 genes associated with the disorder. Among these, CETN3 and GTF2A2 were found to have causal relationships with OSA.

Identification of candidate genes and molecular mechanisms related to asthma progression using bioinformatics.

BACKGROUND: Asthma is a heterogeneous disorder. This study aimed to identify changes in gene expression and molecular mechanisms associated with moderate to severe asthma. METHODS: Differentially expressed genes (DEGs) were analyzed in GSE69683 dataset among moderate asthma and its controls as well as between severe asthma and moderate asthma. Key module genes were identified via co-expression analysis, and the molecular mechanism of the module genes was explored through enrichment analysis and gene set enrichment analysis (GSEA). GSE89809 was used to verify the characteristic genes related to moderate and severe asthma. RESULTS: Accordingly, 2540 DEGs were present between moderate asthma and the control group, while 6781 DEGs existed between severe asthma and moderate asthma. These genes were identified into 14 co-expression modules. Module 7 had the highest positive correlation with severe asthma and was recognized to be a key module by STEM. Enrichment analysis demonstrated that the module genes were mainly involved in oxidative stress-related signaling pathways. The expression of HSPA1A, PIK3CG and PIK3R6 was associated with moderate asthma, while MAPK13 and MMP9 were associated with severe asthma. The AUC values were verified by GSE89809. Additionally, 322 drugs were predicted to target five genes. CONCLUSION: These results identified characteristic genes related to moderate and severe asthma and their corresponding molecular mechanisms, providing a basis for future research.

mir-744-5p inhibits cell growth and angiogenesis in osteosarcoma by targeting NFIX.

BACKGROUND: Osteosarcoma (OS) is a malignant bone tumor that commonly occurs in children and adolescents under the age of 20. Dysregulation of microRNAs (miRNAs) is an important factor in the occurrence and progression of OS. MicroRNA miR-744-5p is aberrantly expressed in various tumors. However, its roles and molecular targets in OS remain unclear. METHODS: Differentially expressed miRNAs in OS were analyzed using the Gene Expression Omnibus dataset GSE65071, and the potential hub miRNA was identified through weighted gene co-expression network analysis. Quantitative real-time PCR (qRT-PCR) was used to detect the expression of miR-744-5p in OS cell lines. In vitro experiments, including CCK-8 assays, colony formation assays, flow cytometry apoptosis assays, and tube formation assays, were performed to explore the effects of miR-744-5p on OS cell biological behaviors. The downstream target genes of miR-744-5p were predicted through bioinformatics, and the binding sites were validated by a dual-luciferase reporter assay. RESULTS: The lowly expressed miRNA, miR-744-5p, was identified as a hub miRNA involved in OS progression through bioinformatic analysis. Nuclear factor I X (NFIX) was confirmed as a direct target for miR-744-5p in OS. In vitro studies revealed that overexpression of miR-744-5p could restrain the growth of OS cells, whereas miR-744-5p inhibition showed the opposite effect. It was also observed that treatment with the conditioned medium from miR-744-5p-overexpressed OS cells led to poorer proliferation and angiogenesis in human umbilical vein endothelial cells (HUVECs). Furthermore, NFIX overexpression restored the suppression effects of miR-744-5p overexpression on OS cell growth and HUVECs angiogenesis. CONCLUSION: Our results indicated that miR-744-5p is a potential tumor-suppressive miRNA in OS progression by targeting NFIX to restrain the growth of OS cells and angiogenesis in HUVECs.

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

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

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis.

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

Insights into the ameliorative effect of ZnONPs on arsenic toxicity in soybean mediated by hormonal regulation, transporter modulation, and stress responsive genes.

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils.