Gene Co-expression Network Research Articles

The m6A and immune regulatory gene signature predicts the prognosis and correlates with immune infiltration of head and neck squamous cell carcinoma

Recent investigations have underscored the epigenetic modulation of the immune response; however, the interplay between RNA N6-methyladenosine (m6A) modification and immunomodulation in head and neck squamous cell carcinoma (HNSC) remains relatively unexplored. To bridge this knowledge gap, we undertook an extensive examination of the potential contributions of m6A modification and immunomodulation in HNSC. We amalgamated and deduplicated 27 m6A -related genes (m6AGs) and 1342 immune regulation-related genes (IMRGs), resulting in a comprehensive dataset encompassing 1358 genes. This dataset was scrutinized for m6A modification and immunomodulatory patterns within HNSC specimens. Employing Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) technique, we developed a prognostic risk model for m6A regulator-mediated methylation modification and immunomodulation-related differentially expressed genes (m6A&IMRDEGs). Our differential expression analysis delineated 29 m6A&IMRDEGs, and Weighted Gene Co-expression Network Analysis (WGCNA) elucidated two module genes (IL11 and MMP13) subjected to correlation analysis. The prognostic prediction models revealed that the clinical predictive efficacy peaked for 1-year forecasts, followed sequentially by 3-year and 5-year predictions. The risk scores derived from the model adeptly categorized HNSC patients into high- and low-risk cohorts, with the high-risk group exhibiting a more unfavorable prognosis. Protein-Protein Interaction (PPI) analysis identified 7 hub genes implicated in m6A and immune regulation, namely BPIFB1, BPIFB2, GP2, MUC5B, MUC7, PIP, and SCGB3A1. Furthermore, we noted marked disparities in the expression profiles of 18 immune cell types between the high- and low-risk groups. Our results substantiate that the clustering subpopulations and risk models associated with m6A and immune regulatory genes portend a poor prognosis in HNSC. The risk score emerges as a potent prognostic biomarker and predictive metric for HNSC patients. A thorough assessment of m6A and immune regulatory genes in HNSC will augment our comprehension of the tumor immune microenvironment and facilitate the advancement of HNSC therapeutics.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment

BackgroundGliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data.MethodsIn this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted.ResultsCD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram’s predictions were supported by calibration plots, further validating its accuracy.ConclusionIn conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Exploring Shared Genetic Features and Molecular Mechanisms between Non-Alcoholic Steatohepatitis and Hepatocellular Carcinoma through Bioinformatics.

Hepatocellular Carcinoma (HCC) is one of the most common malignant tumors in the world, characterized by high incidence, high malignancy, and low survival rate. Currently, 1/4 of adults in the world suffer from Non-Alcoholic Fatty Liver Disease (NAFLD), with an incidence rate of 27% in Asia. We used TCGA and GEO public database data sets to conduct weighted gene coexpression network analysis to identify relevant gene modules, defined the intersection of tumorigenesis-related modules and NASH development-related modules as shared genes, and then used single-factor Cox, LASSO, and multivariate Cox regression analysis screened out core shared genes and verified their prognostic value. We further investigated the relationship between core shared genes and immune infiltration, tumor mutational load, and drug sensitivity. Finally, RT-qPCR was used to verify its mRNA expression in different cell lines. We identified Karyopherin α 2 (KPNA2) as the core shared gene between NASH and HCC. Patients were divided into low-risk groups and high-risk groups based on the expression of KPNA2. The prognosis of the low-risk group was significantly better than that of the highrisk group. Furthermore, we found significant differences in tumor immune cell infiltration, somatic mutations, microsatellite instability, and drug sensitivity between different expression groups. There are very few studies on the molecular mechanism of the relationship between NAFLD and HCC. Our study demonstrates that KPNA2 is a potential therapeutic target and immune-related biomarker for patients with NAFLD and HCC.

Dissecting the lung transcriptome of pulmonary fibrosis-associated pulmonary hypertension.

Integrative multiomics can help elucidate the pathophysiology of pulmonary fibrosis (PF)-associated pulmonary hypertension (PH) (PF-PH). Weighted gene coexpression network analysis (WGCNA) was performed on a transcriptomic dataset of explanted lung tissue from 116 patients with PF. Patients were stratified by pulmonary vascular resistance (PVR), and differential gene expression analysis was conducted. Gene modules were correlated with hemodynamics at the time of transplantation and tested for enrichment in the lung transcriptomics signature of an independent pulmonary arterial hypertension (PAH) cohort. We found 1,250 differentially expressed genes between high and low PVR groups. WGCNA identified that black and yellowgreen modules negatively correlated with PVR, whereas the tan and darkgrey modules are positively correlated with PVR in PF-PH. In addition, the tan module showed the strongest enrichment for an independent PAH gene signature, suggesting shared gene expression patterns between PAH and PF-PH. Pharmacotranscriptomic analysis using the Connectivity Map implicated the tan and darkgrey modules as potentially pathogenic in PF-PH, given their combined module signature demonstrated a high negative connectivity score for treprostinil, a medication used in the treatment of PF-PH, and a high positive connectivity score for bone morphogenetic protein (BMP) loss of function. Pathway enrichment analysis revealed that inflammatory pathways and oxidative phosphorylation were downregulated, whereas epithelial-mesenchymal transition was upregulated in modules associated with increased PVR. Our integrative systems biology approach to the lung transcriptome of PF with and without PH identified several PH-associated coexpression modules and gene targets with shared molecular features with PAH warranting further investigation to uncover potential new therapies for PF-PH.NEW & NOTEWORTHY An integrative systems biology approach that included transcriptomic analysis of explanted lung tissue from patients with pulmonary fibrosis (PF) with and without pulmonary hypertension (PH) undergoing lung transplantation, combined with hemodynamic correlation and pharmacotranscriptomics, identified modules of genes associated with pulmonary vascular disease severity. Comparison with an independent pulmonary arterial hypertension (PAH) dataset identified shared gene expression patterns between PAH and PF-PH.

Integrated metagenomics and metabolomics analyses revealed biomarkers in β-casein A2A2-type cows.

In Holstein cows, β-casein, one of the most critical proteins in milk, exists in two main genotypes, A1 and A2. Herein, 45 Holstein cows [categorized into three groups based on β-casein A1A1, A1A2, and A2A2 genotypes (N = 15)] with the same feeding management and litter size were enrolled to explore differences in rumen microflora and metabolites across various β-casein genotypes. Rumen fluids were collected for metagenomics and metabolomics analyses. Metabolomics and weighted gene co-expression network analysis (WGCNA) revealed that arachidonic acid (AA), adrenic acid (AdA), glycocholic acid (GCA), and taurocholic acid (TCA) were significantly and positively correlated with milk fat % in dairy cows (p < 0.05). Furthermore, macro-genomics and Spearman's correlation analysis revealed significant positive correlations (p < 0.05) between the characteristic flora (g_Acetobacter, g_Pseudoxanthomonas, g_Streptococcus, and g_Pediococcus) and the five characteristic metabolites in the rumen of A2A2 dairy cows. Moreover, functional enrichment analysis revealed more genes enriched to the TRP channel's inflammatory mediator-regulated pathway and the mTOR signaling pathway in A2A2 genotyped cows. Additionally, the regulatory effects of AA on bovine mammary epithelial cells (BMECs) were examined using CCK-8, EdU, and qRT-PCR assays, revealing that AA promoted triglyceride (TG) synthesis and upregulated the milk fat marker genes including SREBF1, ACSS2, AGPAT6, and FASN. Overall, we identified characteristic microorganisms and metabolites in A2A2 Holstein cows and established that AA could be a biomarker for higher milk fat %.

Utilizing nontargeted metabolomics integrated with quality component quantitation via differential analysis to reveal the taste differences between and metabolite characteristics of hawk black tea and hawk white tea

This study conducted a comprehensive analysis of the differences in the taste characteristics of hawk black tea and hawk white tea, exploring the intrinsic relationship between nonvolatile metabolites and these taste characteristics. The results revealed significant differences in taste properties of the two types of hawk tea. Hawk black tea mainly exhibits a fresh, sweet, and sweet aftertaste quality, whereas hawk white tea offers a mellow and pure taste. Comparative analysis revealed that the content of amino acids and carbohydrates in hawk black tea was significantly higher than that in hawk white tea, while the content of flavonoids and organic acids was relatively lower. Quality composition results indicated significant differences in the 13 major quality components and water extracts between the two hawk teas, aligning with the quantitative descriptive analysis results. Additionally, through weighted gene coexpression network analysis, significant associations were found between 671 metabolites across 155 metabolic pathways and 9 taste characteristics. Among them, flavonoid biosynthesis and degradation are the main metabolic pathways of the two types of hawk tea. And it was found that butin, phlorizin, naringin, dihydrokaempferol, sakuranetin, apigenin, (−)-epicatechin, (+)-gallocatechin, caffeoyl shikimic acid, homoeriodictyol, which are 10 flavonoids, are the key metabolites affecting the taste difference of hawk black and white tea. Additionally, this study found that hawk tea does not contain caffeine, which is of great significance for the development of caffeine-free beverages in the future.

CT1812 biomarker signature from a meta-analysis of CSF proteomic findings from two Phase 2 clinical trials in Alzheimer's disease.

CT1812 is in clinical development for the treatment of Alzheimer's disease (AD). Cerebrospinal fluid (CSF) exploratory proteomics was employed to identify pharmacodynamic biomarkers of CT1812 in mild to moderate AD from two independent clinical trials. Unbiased analysis of tandem-mass tag mass spectrometry (TMT-MS) quantitative proteomics, pathway analysis and correlation analyses with volumetric magnetic resonance imaging (vMRI) were performed for the SPARC cohort (NCT03493282). Comparative analyses and a meta-analysis with the interim SHINE cohort (NCT03507790; SHINE-A) followed by network analysis (weighted gene co-expression network analysis [WGCNA]) were used to understand the biological impact of CT1812. CT1812 pharmacodynamic biomarkers and biological pathways were identified that replicate across two clinical cohorts. The meta-analysis revealed novel candidate biomarkers linked to S2R biology and AD, and network analysis revealed treatment-associated networks driven by S2R. DISCUSSION: Early clinical validation of CT1812 candidate biomarkers replicating in independent cohorts strengthens the understanding of the biological impact of CT1812 in patients with AD, and supports CT1812's synaptoprotective mechanism of action and its continued clinical development. This exploratory proteomics study identified candidate biomarkers of CT1812 in SPARC (NCT03493282) Comparative analyses identified biomarkers replicating across trials/cohorts Two independent Ph2 trial cohorts (SPARC and interim SHINE [NCT03507790; SHINE-A]) were used in a meta-analysis Amyloid beta (Aβ)& synaptic biology impacted by CT1812 and volumetric magnetic resonance imaging (vMRI) treatment-related correlates emerge Network analyses revealed sigma-2 receptor (S2R)-interacting proteins that may be "drivers" of changes.

Transcriptome analysis of the coexpression network of genes related to antioxidant characteristics after grain filling in purple rice

Antioxidant capacity is an important indicator for evaluating the growth and developmental quality of rice. This study has guiding significance for the cultivation of high-nutrient-value varieties. To investigate the molecular mechanisms underlying the antioxidant characteristics of rice grains after the filling stage, Yangzinuo 1 (YZN1) was used as the experimental material, and grains collected at five different time points (7 days apart) after the filling stage were used for transcriptome sequencing. Through weighted gene coexpression network analysis (WGCNA), a coexpression network of gene weights related to antioxidant characteristics was constructed. LOC_Os10g39140, LOC_Os10g38276, and LOC_Os05g45740 were identified from the 2 modules showing the highest correlations with the target traits. GO functional annotation showed that target modules were enriched in pathways related to phenylalanine, flavonoids, and other related pathways, such as GO:0006558, GO:0006559, GO:0009812, and GO:0009813. Correlation analysis with metabolites revealed that differentially expressed genes were significantly enriched in pathways related to antioxidant characteristics and energy metabolism processes, such as glycolysis/gluconeogenesis and flavonoid biosynthesis. The core genes identified in this study were found to be highly correlated with antioxidant characteristics and enriched in pathways related to metabolic and energy pathways and molecular activities. These results provide an effective dataset supporting breeding targeting functional rice characteristics.

Multi-omics analyzes of Rosa gigantea illuminate tea scent biosynthesis and release mechanisms.

Rose is an important ornamental crop cultivated globally for perfume production. However, our understanding of the mechanisms underlying scent production and molecular breeding for fragrance is hindered by the lack of a reference genome for tea roses. We present the first complete telomere-to-telomere (T2T) genome of Rosa gigantea, with high quality (QV > 60), including detailed characterization of the structural features of repetitive regions. The expansion of genes associated with phenylpropanoid biosynthesis may account for the unique tea scent. We uncover the release rhythm of aromatic volatile organic compounds and their gene regulatory networks through comparative genomics and time-ordered gene co-expression networks. Analyzes of eugenol homologs demonstrate how plants attract pollinators using specialized phenylpropanoids in specific tissues. This study highlights the conservation and utilization of genetic diversity from wild endangered species through multi-omics approaches, providing a scientific foundation for enhancing rose fragrance via de novo domestication.

Pan-genome analysis of GT64 gene family and expression response to Verticillium wilt in cotton

BackgroundThe GT64 subfamily, belonging to the glycosyltransferase family, plays a critical function in plant adaptation to stress conditions and the modulation of plant growth, development, and organogenesis processes. However, a comprehensive identification and systematic analysis of GT64 in cotton are still lacking.ResultsThis study used bioinformatics techniques to conduct a detailed investigation on the GT64 gene family members of eight cotton species for the first time. A total of 39 GT64 genes were detected, which could be classified into five subfamilies according to the phylogenetic tree. Among them, six genes were found in upland cotton. Furthermore, investigated the precise chromosomal positions of these genes and visually represented their gene structure details. Moreover, forecasted cis-regulatory elements in GhGT64s and ascertained the duplication type of the GT64 in the eight cotton species. Evaluation of the Ka/Ks ratio for similar gene pairs among the eight cotton species provided insights into the selective pressures acting on these homologous genes. Additionally, analyzed the expression profiles of the GT64 gene family. Overexpressing GhGT64_4 in tobacco improved its disease resistance. Subsequently, VIGS experiments conducted in cotton demonstrated reduced disease resistance upon silencing of the GhGT64_4, may indicate its involvement in affecting lignin and jasmonic acid biosynthesis pathways, thus impacting cotton resistance. Weighted Gene Co-expression Network Analysis (WGCNA) revealed an early immune response against Verticillium dahliae in G. barbadense compared to G. hirsutum. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis indicated that some GT64 genes might play a role under various biotic and abiotic stress conditions.ConclusionsThese discoveries enhance our knowledge of GT64 family members and lay the groundwork for future investigations into the disease resistance mechanisms of this gene in cotton.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes—EP300, MTOR, NFE2L1, TXN—exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

Comprehensive Analysis of a Platelet- and Coagulation-Related Prognostic Gene Signature Identifies CYP19A1 as a Key Tumorigenic Driver of Colorectal Cancer.

The intricate interplay between the platelet-coagulation system and the progression of malignant tumors has profound therapeutic implications. However, a thorough examination of platelet and coagulation markers specific to colorectal cancer (CRC) is conspicuously absent in the current literature. Consequently, there is an urgent need for further exploration into the mechanistic underpinnings of these markers and their potential clinical applications. By integrating RNA-seq data and clinicopathological information from patients with CRC in the cancer genome atlas, we identified genes related to the platelet-coagulation system using weighted gene co-expression networks and univariate Cox analysis. We established a prognostic risk model based on platelet- and coagulation-related genes using Lasso Cox regression analysis and validated the model in two independent CRC cohorts. We explored potential biological functional disparities between high-risk and low-risk groups through comprehensive bioinformatics analysis. Our findings indicate that colorectal cancer patients classified as high-risk generally exhibit poorer prognoses. Moreover, the model's risk scores were associated with the differential composition of the immune tumor microenvironment, suggesting its applicability to infer immunotherapy responsiveness. Cellular functional experiments and animal experiments indicated that CYP19A1 expression in CRC influences malignant phenotype and platelet activation. In summary, we present a novel platelet- and coagulation-related risk model for prognostic assessment of patients with CRC and confirm the important role of CYP19A1 in promoting malignant progression of CRC.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways. In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA). A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients. In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

ANXA3 as a novel biomarker for sepsis diagnosis: Evidence from integrative WGCNA analysis

Sepsis is a dysregulated immune response to infection that comes with multiple organ dysfunction and high mortality. The management of sepsis relies heavily on early recognition and diagnosis, but current diagnostic methods have limitations in timeliness, sensitivity, and discriminability. This study aims to discover novel biomarkers for sepsis diagnosis. Four datasets from different regions were analyzed using weighted gene co-expression network analysis (WGCNA), and genes with high Gene Significance values across these datasets were overlapped. Finally, two genes, CD177 and ANXA3, were identified. ANXA3 was validated as a potential sepsis biomarker by checking multiple datasets and Receiver Operating Characteristic (ROC) Curve Analysis. Of note, ANXA3 could distinguish not only between adult and child sepsis patients and healthy controls, but also between septic shock and cardiogenic shock. Moreover, a murine sepsis model was established and the results showed that the transcription of ANXA3 in peripheral blood of septic mice was significantly higher than that of healthy controls, while Escherichia coli infection alone did not significantly increase the transcription level of this gene. Subsequent studies of sepsis in mice revealed that the predictive effect of Anxa3 on sepsis could be observed as early as 6 h post-modeling. Interestingly, ANXA3 expression was predominantly up-regulated in myeloid cells, up-regulated in spleen, down-regulated in lung, and not detected in liver after sepsis modeling. Taken together, this study provides a way for the discovery of biomarkers and finds that ANXA3 may be a novel diagnostic biomarker for sepsis.

Single-cell analysis of preimplantation embryonic development in guinea pigs

BackgroundGuinea pigs exhibit numerous physiological similarities to humans, yet the details of their preimplantation embryonic development remain largely unexplored.ResultsTo address this, we conducted single-cell sequencing on the transcriptomes of cells isolated from the zygote stage through preimplantation stages in guinea pigs. This study identified seven distinct cell types within guinea pig preimplantation embryos and pinpointed the timing of zygotic gene activation (ZGA). Trajectory analysis revealed a bifurcation into two lineage-specific branches, accompanied by alterations in specific pathways, including oxidative phosphorylation and vascular endothelial growth factor (VEGF). Additionally, co-expressed gene network analysis highlighted the most enriched functional modules for the epiblast (EPI), primitive endoderm (PrE), and inner cell mass (ICM). Finally, we compared the similarities and differences between human and guinea pig epiblasts (EPIs).ConclusionThis study systematically constructs a cell atlas of guinea pig preimplantation embryonic development, offering fresh insights into mammalian embryonic development and providing alternative experimental models for studying human embryonic development.

Transcriptome and Metabolome Analyses Reveal the Molecular Mechanisms of Albizia odoratissima’s Response to Drought Stress

Albizia odoratissima is a deciduous tree species belonging to the family Leguminosae. It is widely distributed in the southern subtropical and tropical areas of China and has important ecological and economic value. The growth and metabolic processes of A. odoratissima are affected by drought stress, but the molecular mechanisms remain unknown. Therefore, this study investigated the physicochemical properties, gene expression, and metabolites of A. odoratissima seedlings under drought stress. The results show that, in leaves of A. odoratissima seedlings, drought stress reduced the moisture content, chlorophyll content, photosynthetic efficiency, superoxide dismutase (SOD) activity, and gibberellin (GA) and indoleacetic acid (IAA) contents while increasing the catalase (CAT) and peroxidase (POD) activities and malondialdehyde (MDA), proline, soluble sugar, and soluble protein contents. Within the CK5 (Day 5 of control group) vs. T5 (Day 5 of drought treatment), CK10 vs. T10, CK15 vs. T15, and CK20 vs. T20 groups (CK: control group; T: drought treatment), a total of 676 differentially expressed genes (DEGs) were upregulated and 518 DEGs were downregulated, and a total of 228 and 143 differential accumulation metabolites (DAMs) were identified in the CK10 vs. T10 and CK20 vs. T20 groups. These were mainly involved in the amino acid and alkaloid metabolism pathways in the leaves of the A. odoratissima seedlings. In the amino acid and alkaloid biosynthesis pathways, the relative expression levels of the AoproA (Aod04G002740, ORTHODONTIC APPLIANCE), AoOAT (Aod07G015970, ORNITHINE-OXO-ACID TRANSAMINASE), and AoAOC3 (Aod12G005010/08G003360/05G023920/08G003000/08G003010, AMINE OXIDASE COPPER CONTAINING 3) genes increased, which concurrently promoted the accumulation of arginine, proline, piperine, cadaverine, and lysine. Furthermore, some key transcription factors in the response to drought were identified in the leaves using the weighted gene co-expression network analyses (WGCNA) method. These findings reveal that A. odoratissima seedlings respond to drought stress by improving the capacities of the antioxidant system and secondary metabolism.

Identification of key hub genes in knee osteoarthritis through integrated bioinformatics analysis.

Knee osteoarthritis (KOA) is a common chronic joint disease globally. Synovial inflammation plays a pivotal role in its pathogenesis, preceding cartilage damage. Identifying biomarkers in osteoarthritic synovial tissues holds promise for early diagnosis and targeted interventions. Gene expression profiles were obtained from the Gene Expression Omnibus database. Subsequent analyses included differential expression gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) on the combined datasets. We performed functional enrichment analysis on the overlapping genes between DEGs and module genes and constructed a protein-protein interaction network. Using Cytoscape software, we identified hub genes related to the disease and conducted gene set enrichment analysis on these hub genes. The CIBERSORT algorithm was employed to evaluate the correlation between hub genes and the abundance of immune cells within tissues. Finally, Mendelian randomization analysis was utilized to assess the potential of these hub genes as biomarkers. We identified 46 differentially expressed genes (DEGs), comprising 20 upregulated and 26 downregulated genes. Using WGCNA, we constructed a gene co-expression network and selected the most relevant modules, resulting in 24 intersecting genes with the DEGs. KEGG enrichment analysis of the intersecting genes identified the IL-17 signaling pathway, associated with inflammation, as the most significant pathway. Cytoscape software was utilized to rank the candidate genes, with JUN, ATF3, FOSB, NR4A2, and IL6 emerging as the top five based on the Degree algorithm. A nomogram model incorporating these five genes, supported by ROC curve analysis, validated their diagnostic efficacy. Immune infiltration and correlation analysis revealed that macrophages were significantly associated with JUN (p < 0.01), FOSB (p < 0.01), and NR4A2 (p < 0.05). Additionally, T follicular helper cells showed significant associations with ATF3 (p < 0.05), FOSB (p < 0.05), and JUN (p < 0.05). Mendelian randomization analysis provided strong evidence linking JUN (IVW: OR = 0.910, p = 0.005) and IL6 (IVW: OR = 1.024, p = 0.026) with KOA. Through the utilization of various bioinformatics analysis methods, we have pinpointed key hub genes relevant to knee osteoarthritis. These findings hold promise for advancing pre-symptomatic diagnostic strategies and enhancing our understanding of the biological underpinnings behind knee osteoarthritis susceptibility genes.

Signaling Pathways in Clear Cell Renal Cell Carcinoma and Candidate Drugs Unveiled through Transcriptomic Network Analysis of Hub Genes

Clear cell renal cell carcinoma (ccRCC) is a type of kidney cancer. It advances quickly and often metastasizes, making the prognosis for patients challenging. This study used weighted gene co-expression network analysis (WGCNA) to study gene expression data of different stages of ccRCC obtained in the GEO database. The analysis identified three significant highly preserved gene modules across the datasets: GSE53757, GSE22541, GSE66272, and GSE73731. Functional annotation and pathway enrichment analysis using DAVID revealed inflammatory pathways (e.g., NF-kB, Hippo, and HIF-1 pathways) that may drive ccRCC development and progression. The study also introduced the involvement of viral infections associated with the disease in the metabolic reprogramming of ccRCC. A drug repurposing analysis was also conducted to identify potential drug candidates for ccRCC using the upregulated and downregulated hub genes. The top candidates are ziprasidone (dopamine and serotonin receptor antagonist) and fentiazac (cyclooxygenase inhibitor). Other drug candidates were also obtained, such as phosphodiesterase/DNA methyltransferase/ATM kinase inhibitors, acetylcholine antagonists, and NAD precursors. Overall, the study’s findings suggest that identifying several genes and signaling pathways related to ccRCC may uncover new targets, biomarkers, and even drugs that can be repurposed, which can help develop new and effective treatments for the disease.

Integrating network pharmacology, molecular docking and experimental verification to explore the therapeutic effect and potential mechanism of nomilin against triple-negative breast cancer

BackgroundNomilin is a limonoid compound known for its multiple biological activities, but its role in triple negative breast cancer (TNBC) remains unclear. This study aims to uncover the potential therapeutic effect of nomilin on TNBC and elucidate the specific mechanism of its action.MethodsWe employed weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the GeneCards database to identify potential targets for TNBC. Simultaneously, we utilized the Swiss Target Prediction, ChEMBL, and STITCH databases to identify potential targets of nomilin. The core targets and mechanisms of nomilin against TNBC were predicted through protein-protein interaction (PPI) network analysis, molecular docking, and enrichment analysis. The results of the network pharmacology were corroborated by conducting experiments.ResultsA total of 17,204 TNBC targets were screened, and 301 potential targets of nomilin were identified. Through the PPI network, eight core targets of nomilin against TNBC were pinpointed, namely BCL2, Caspase3, CyclinD1, EGFR, HSP90AA1, KRAS, PARP1, and TNF. Molecular docking, molecular dynamics simulation and proteome microarray revealed that nomilin exhibits strong binding activity to these core proteins. Enrichment analysis results indicated that the anti-TNBC effect of nomilin is associated with PI3K/Akt pathway. In vitro and in vivo experiments have demonstrated that nomilin inhibits TNBC cell proliferation and migration while promoting cell apoptosis through the PI3K/Akt pathway.ConclusionFor the first time, the research effectively discovered the objectives and mechanisms of nomilin in combating TNBC using network pharmacology, molecular docking, molecular dynamics simulation, proteome microarray and experimental confirmation, presenting a hopeful approach for treating TNBC.Graphical

A time-course transcriptomic analysis reveals the key responses of a resistant rice cultivar to brown planthopper infestation.

The brown planthopper (BPH) is one of the most problematic pests affecting rice (Oryza sativa L.) yields in Asia. Breeding rice varieties containing resistance genes is the most economical and effective means of controlling BPH. In this study, the key factors in resistance to BPH were investigated between the high-resistance rice variety "R26" and the susceptible variety "TN1" using RNA-sequencing. We identified 9527 differentially expressed genes (DEGs) between the rice varieties under BPH-induced stress. Weighted time-course gene co-expression network analysis (WGCNA) indicated that the increased expression of genes is associated with plant hormones, MAPK signaling pathway and biosynthesis of other secondary metabolites, which were involved in disease resistance. A connection network identified a hub gene, OsREM4.1 (BGIOSGA024059), that may affect rice resistance to the BPH. Knocking out OsREM4.1 in rice can lead to a decrease in callose, making it less resistant to BPH. Overall, the expression of differentially expressed genes varies among rice varieties with different resistance in BPH invasion. Inaddition, R26 enhances resistance to BPH by upregulating genes and secondary metabolites related to stress resistance and plant immunity. In summary, our study provides valuable insights into the genome-wide expression profile of DEGs in rice under BPH invasion through high-throughput sequencing, and further suggests that R26 can be used to develop high resistance rice lines in BPH resistant breeding programs.