Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA

Identification of Key Biomarkers of Growth-Related Traits in the Bay Scallop Argopecten irradians irradians via Multi-omics Analysis Strategies

In an attempt to promote and advance molecular-assisted selective breeding of shellfish, in this study, gills from selected (shell color as the marker trait and growth performance as the target trait) and unselected Argopecten irradians irradians were sampled. 2b-restriction site-associated DNA sequencing, genome-wide association study, comparative transcriptome, comparative small RNA transcriptome, miRNA-mRNA integrated analysis, quantitative real-time reverse transcription-polymerase chain reaction, and weighted gene co-expression network analysis were used to identify candidate SNPs, mRNAs, miRNAs, and miRNA/mRNA pairs with potential selective breeding value at a genomic scale in A. irradians irradians. The results revealed that a total of 6 significant SNPs were correlated closely with at least two examined growth-related traits. In addition, a total of 10 mRNAs and 7 miRNAs were identified to have positive correlations with shell length, shell height, shell width, total weight, and shell color. These candidate mRNAs and miRNAs may form 11 miRNA-mRNA pairs, which have great potential for developing molecular markers for molecular-assisted selective breeding of A. irradians irradians. The findings of this study will benefit the development of molecular-assisted breeding techniques in bay scallop aquaculture.

Identification of Hub Genes and Key Pathways Associated with Sepsis Progression Using Weighted Gene Co-Expression Network Analysis and Machine Learning

Sepsis is a life-threatening condition driven by dysregulated immune responses, resulting in organ dysfunction and high mortality rates. Identifying key genes and pathways involved in sepsis progression is crucial for improving diagnostic and therapeutic strategies. This study analyzed transcriptomic data from 49 samples (37 septic patients across days 0, 1, and 8, and 12 healthy controls) using weighted gene co-expression network analysis (WGCNA) and multi-algorithm feature selection approaches. Differential expression analysis, pathway enrichment, and network analyses were employed to uncover potential biomarkers and molecular mechanisms. WGCNA identified modules such as MEbrown4 and MEblack, which strongly correlated with sepsis progression (r > 0.7, p < 0.01). Differential expression analysis highlighted up-regulated genes like CD177 and down-regulated genes like LOC440311. KEGG analysis revealed significant pathways including neuroactive ligand-receptor interaction, PI3K-Akt signaling, and MAPK signaling. Gene ontology analysis showed involvement in immune-related processes such as complement activation and antigen binding. Protein-protein interaction network analysis identified hub genes such as TNFSF10, IGLL5, BCL2L1, and SNCA. Feature selection methods (random forest, LASSO regression, SVM-RFE) consistently identified top predictors like TMCC2, TNFSF10, and PLVAP. Receiver operating characteristic (ROC) analysis demonstrated high predictive accuracy for sepsis progression, with AUC values of 0.973 (TMCC2), 0.969 (TNFSF10), and 0.897 (PLVAP). Correlation analysis linked key genes such as TNFSF10, GUCD1, and PLVAP to pathways involved in immune response, cell death, and inflammation. This integrative transcriptomic analysis identifies critical gene modules, pathways, and biomarkers associated with sepsis progression. Key genes like TNFSF10, TMCC2, and PLVAP genes show strong diagnostic potential, providing novel insights into sepsis pathogenesis and offering promising targets for future therapeutic interventions. Among these, TNFSF10 and PLVAP are known to encode secreted proteins, suggesting their potential as circulating biomarkers. This enhances their translational relevance in clinical diagnostics.

SAA1 as a key mediator of immune inflammatory pathways in fungal keratitis through FOXO3a phosphorylation regulation

OBJECTIVE

Fungal keratitis (FK) is a severe ocular infection, with its underlying molecular mechanisms remaining incompletely understood. This study aimed to identify and investigate key genes involved in immune-inflammatory responses associated with FK pathogenesis using bioinformatics and in vitro assays.

METHODS

Transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE58291) were analyzed using the limma package to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to evaluate significant biological processes and pathways related to DEGs. Weighted gene co-expression network analysis (WGCNA) identified gene modules linked with FK-associated DEGs, and Venn diagram analysis highlighted core genes. Receiver operating characteristic (ROC) analysis assessed diagnostic potential. Immune cell composition was analyzed using CIBERSORT, and correlations between key genes and immune cells were evaluated. In vitro, human corneal epithelial cells (HCEC) were stimulated with Aspergillus fumigatus (A.F.), and pro-inflammatory cytokine expression (IL-1β, TNF-α, IL-6) was assessed using enzyme-linked immunosorbent assay (ELISA). Western blot and quantitative real-time polymerase chain reaction (RT-qPCR) analyzed FOXO3a phosphorylation and gene expression changes post-SAA1 siRNA transfection.

RESULTS

A total of 101 DEGs were identified, with WGCNA revealing 6 co-expression network modules, with significant associations noted in yellow and black modules. Nine shared genes were identified in DEGs and modules, with SAA1 strongly linked to FK pathogenesis. SAA1 expression was positively correlated with neutrophils, T cells CD4 memory activated, T cells gamma delta, and activated mast cells. Upon stimulation with A.F., cytokine expression increased, peaking at 24 h. Inhibition of SAA1 reduced FOXO3a phosphorylation and pro-inflammatory cytokine levels, underscoring SAA1's role in FK inflammation via FOXO3a regulation.

CONCLUSION

SAA1 is a key gene in FK, promoting inflammation by modulating FOXO3a phosphorylation. This highlights its potential as a therapeutic target in managing FK-related inflammation.

Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis

Background

Atopic dermatitis (AD) is a prevalent chronic inflammatory skin disorder with a complex pathogenesis involving genetic predisposition, environmental factors, and immune dysregulation. This study aimed to investigate key differentially expressed genes (DEGs) in AD and their association with immune cell infiltration patterns.

Methods

The GSE32924 dataset comprises gene expression data from 25 AD samples and 8 control samples. Differential expression analysis was performed using the R package limma. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using clusterProfiler. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules. Least Absolute Shrinkage and Selection Operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen hub genes. Immune cell infiltration was evaluated using CIBERSORT. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed to validate DEG expression in peripheral blood samples from AD patients and healthy controls. Potential microRNA (miRNA)-messenger RNA (mRNA) and miRNA-long non-coding RNA (lncRNA) interactions were predicted using miRanda and TargetScan tools.

Results

We identified 381 DEGs (217 upregulated, 164 downregulated). GO analysis revealed enrichment in skin barrier formation, epidermal development, and inflammatory response. KEGG analysis showed significant involvement of sphingolipid metabolism and Toll-like receptor signaling pathways. Five hub genes (ATP6V1A, CLDN23, ECSIT, LRFN5, USP16) were identified. Immune cell infiltration demonstrated significant differences in activated dendritic cells (aDCs) and regulatory T cells (Tregs) between AD and controls. RT-qPCR confirmed elevated ECSIT and decreased LRFN5 and USP16 expression in AD patients (P < 0.05). A competing endogenous RNA (ceRNA) network involving lncRNA-miRNA-mRNA interactions for the key gene ECSIT was also constructed.

Conclusion

ECSIT, LRFN5, and USP16 represent promising diagnostic biomarkers for AD and are involved in immune cell infiltration, providing new insights into AD pathogenesis.

Identification of key hub genes associated with anti-gastric cancer effects of lotus plumule based on machine learning algorithms

BACKGROUND

Lotus plumule and its active components have demonstrated inhibitory effects on gastric cancer (GC). However, the molecular mechanism of lotus plumule against GC remains unclear and requires further investigation.

AIM

To identify the key hub genes associated with the anti-GC effects of lotus plumule.

METHODS

This study investigated the potential targets of traditional Chinese medicine for inhibiting GC using weighted gene co-expression network analysis and bioinformatics. Initially, the active components and targets of the lotus plumule and the differentially expressed genes associated with GC were identified. Subsequently, a protein-protein interaction network was constructed to elucidate the interactions between drug targets and disease-related genes, facilitating the identification of hub genes within the network. The clinical significance of these hub genes was evaluated, and their upstream transcription factors and downstream targets were identified. The binding ability of a hub gene with its downstream targets was verified using molecular docking technology. Finally, molecular docking was performed to evaluate the binding affinity between the active ingredients of lotus plumule and the hub gene.

RESULTS

This study identified 26 genes closely associated with GC. Machine learning analysis and external validation narrowed the list to four genes: Aldo-keto reductase family 1 member B10, fructose-bisphosphatase 1, protein arginine methyltransferase 1, and carbonic anhydrase 9. These genes indicated a strong correlation with anti-GC activity.

CONCLUSION

Lotus plumule exhibits anti-GC effects. This study identified four hub genes with potential as novel targets for diagnosing and treating GC, providing innovative perspectives for its clinical management.

Identification of biomarkers associated with energy metabolism in hypertrophic cardiomyopathy and exploration of potential mechanisms of roles

Background

In hypertrophic cardiomyopathy (HCM), limited reports exist regarding its association with energy metabolism. Here, biomarkers related to energy metabolism in HCM were identified through bioinformatics analysis.

Methods

HCM transcriptome data were acquired from the GEO (GSE36961) database for comparative analysis in order to identify differentially expressed genes (DEGs). Subsequently, the identified DEGs were intersected with key module genes in Weighted gene co-expression network analysis (WGCNA) and energy metabolism related genes (EMRGs) to identify DE-EMRGs. Then, feature biomarkers were screened using the least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) methods, and the intersection of the feature biomarkers obtained from both methods was used for subsequent analysis. Furthermore, biomarkers defined as biomarkers with consistent expression trends across both GSE36961 and GSE89714 datasets and significant inter-cohort differences were selected for subsequent analysis. Subsequently, an immune analysis was conducted. Additionally, the transcription factors (TFs), and drugs regulating the biomarkers were predicted based on online databases.

Results

The co-selection of seven potential biomarkers based on machine learning identified IGFBP3 and JAK2 as biomarkers in HCM. Upregulation of IGFBP3 and JAK2 in the HCM cohort was observed in the GSE36961 and GSE89714 datasets. Utilizing ssGSEA, it was unveiled that the HCM cohort exhibited elevated ratings of effector memory CD4T cells while displaying diminished scores across 22 other immune cell categories. Notably, JAK2 expression exhibited a strong negative correlation with myeloid-derived suppressor cells (MDSCs) infiltration, while IGFBP3 showed no significant associations with immune cell infiltration. Utilizing NetworkAnalyst, miRNAs and TFs regulating biomarkers expression in HCM were predicted, with hsa-mir-16-5p, hsa-mir-147a, hsa-mir-210b-3p, hsa-let-7b-5p, and hsa-mir-34a-5p identified as regulators of both IGFBP3 and JAK2. GATA2 was also found to be a TF regulating the expression of both biomarkers. Furthermore, the potential therapeutic targets of JAK2 and IGFBP3 in HCM were ruxolitinib and celecoxib, respectively.

Conclusion

In conclusion, the identification of IGFBP3 and JAK2 as biomarkers in HCM, highlight promising avenues for further research and treatment development in HCM.

Identification of lncRNAs in peripheral blood mononuclear cells associated with sepsis immunosuppression based on weighted gene co-expression network analysis

BACKGROUND

Sepsis-induced immunosuppression involves complex molecular mechanisms, including dysregulated long noncoding RNAs (lncRNAs), which remain poorly understood.

OBJECTIVE

We aimed to identify immunosuppression-related lncRNAs and their functional pathways in sepsis. Methods: Using weighted gene coexpression network analysis (WGCNA), we analyzed lncRNA profiles from peripheral blood mononuclear cells (PBMCs) of three sepsis patients and three healthy controls. Key modules linked to immunosuppression were validated via RT-PCR and external datasets. Pathway enrichment and protein interaction networks were employed to prioritize mechanisms.

RESULTS

A sepsis-associated module containing 4,193 lncRNAs revealed three immunosuppression-related pathways: Th17 cell differentiation, cytokine-cytokine receptor interactions, and cancer-related proteoglycan signaling. Protein-protein interaction networks identified three central genes (SLFN12, ICOS, IKZF2) and their linked lncRNAs (ENSG00000267074, lnc-ICOSLG-1, lnc-IKZF2-7), all significantly downregulated in sepsis patients.

CONCLUSION

 Our findings highlight novel lncRNA-regulated pathways in sepsis-induced immunosuppression, providing potential targets for improved diagnosis and therapy.

Computational molecular insights into ibrutinib as a potent inhibitor of HER2-L755S mutant in breast cancer: gene expression studies, virtual screening, docking, and molecular dynamics analysis

Background

The proposed study integrates several advanced computational techniques to unravel the molecular mechanisms underlying breast cancer progression and drug resistance.

Methods

We investigated HER2-L755S mutation through a multi-step approach, including gene expression analysis, molecular docking, and molecular dynamics simulations.

Results and Discussion

By conducting a network-based analysis of gene expression data from breast cancer samples, key hub genes such as MYC, EGFR, CDKN2A, ERBB2, CDK1, E2F1, TOP2A, MDM2, TGFB1, and FOXM1 were identified, all of which are critical in tumor growth and metastasis. The study mainly focuses on the ERBB2 gene, which encodes the HER2 protein, and its common mutation HER2-L755S, associated with breast cancer and resistance to the drug lapatinib. The HER2-L755S mutation contributes to both tumorigenesis and therapeutic failure. To address this, alternative therapeutic strategies were investigated using combinatorial computational approaches. The stability and flexibility of the HER2-L755S mutation were evaluated through comparative molecular dynamics simulations over 1000 ns using Gromacs in the unbound (Apo) state. Virtual screening with Schrodinger Glide identified ibrutinib as a promising alternative to lapatinib for targeting the HER2-L755S mutant. Detailed docking and molecular dynamics simulations in the bound (Holo) state demonstrated that the HER2-L755S-ibrutinib complex exhibited higher binding affinity and lower binding energy, indicating more stable interactions compared to other complexes. MM-PBSA analysis revealed that the HER2-L755S-ibrutinib complex had more negative binding energy than the HER2-L755S-afatinib, HER2-L755S-lapatinib, and HER2-L755S-neratinib complexes, suggesting that ibrutinib forms the most stable complex with favorable binding interactions.

Conclusion

These results provide in-depth atomic-level insights into the binding mechanisms of these inhibitors, highlighting ibrutinib as a potentially effective inhibitor for the clinical treatment of breast cancer.

Identification of SNHG11 as a Therapeutic Target in Pulmonary Hypertension

Pulmonary hypertension (PH) is a life-threatening condition characterized by pulmonary vascular remodeling and endothelial dysfunction. Current therapies primarily target vasoactive imbalances but often fail to address adverse vascular remodeling. Long noncoding RNAs (lncRNAs), which are key regulators of various cellular processes, remain underexplored in the context of PH. To investigate the role of lncRNA in PH, we performed a comprehensive analysis using weighted gene coexpression network analysis on the GSE113439 dataset, comprising human lung tissue samples from different PH subtypes. Our analysis identified the lncRNA SNHG11 as consistently downregulated in PH. Functional assays in human pulmonary artery endothelial cells demonstrated that SNHG11 plays a critical role in modulating inflammation, cell proliferation, apoptosis, and the Janus kinase/signal transducers and activators of transcription and mitogen-activated protein kinase signaling pathways. Mechanistically, SNHG11 influences the stability of PRPF8, a crucial mRNA spliceosome component, thereby affecting multiple cellular functions beyond splicing. In vivo experiments using a hypoxic rat model showed that knockdown of SNHG11 alleviates PH development and improves right ventricular function. These findings highlight SNHG11 as a key regulator in PH pathogenesis and suggest it as a potential therapeutic target.

Molecular Mechanisms of Immunoinflammatory Infiltration and Ferroptosis in Arthritis Revealed by a Combination of Bioinformatics and Single-Cell Analysis

Background

Osteoarthritis (OA) is a widespread chronic inflammatory disease in orthopedics, and its molecular mechanisms are still poorly understood.

Objective

The purpose of this work was to detect the immunological infiltration of OA and the manner of cell death utilizing bioinformatics and single-cell analysis in order to provide guidelines for clinical therapy and medicine.

Methods

Ferroptosis -associated genes were sourced from the ferroptosis Database, single-cell and bioinformatic expression profiles were chosen from the Gene Expression Comprehensive Database, and OA gene information was taken from GeneCards. To ascertain the categorization status of OA cells, single-cell analysis was conducted. Protein-protein interaction networks were established by SRING analysis, and functional enrichment was examined in the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. The important proteins of immune-ferroptosis death in OA were elucidated through co-analysis. Last but not least, network pharmacology and molecular docking support the mechanism by which resveratrol controls Ferroptosis in OA.

Results

The development of OA was found to be tightly related to chondrocytes and immune cells, particularly T and macrophage cells, according to single-cell analysis profile. In patients with OA, immune infiltration also revealed a notable infiltration of T cells, B cells, NK cells, monocytes, and macrophages. The hub genes were shown to be enriched in immunological responses, chemokine-mediated signaling pathways, and inflammatory responses, according to enrichment analysis. The main signaling pathways included autophagy, ferroptosis, the HIF-1 signaling pathway, the PI3K-Akt signaling pathway, and the FoxO signaling pathway. Ferroptosis is a significant cell death mechanism that contributes to the advancement of osteoarthritis. Ferroptosis in chondrocytes is lessened by resveratrol regulation of GPX4, TFRC, and SLC7A11.

Conclusion

Various immune cell infiltrates, especially T cells and macrophages, play an important role in the progression of OA, and resveratrol ameliorates OA by modulating chondrocyte ferroptosis.

TCF7L2 as a target of peripheral artery disease in patients with type 2 diabetes: A 2-sample Mendelian randomization and bioinformatics study

This study examines the causal relationship between type 2 diabetes (T2D) and peripheral artery disease (PAD) and their potential mechanisms based on the analysis of the Gene Expression Omnibus database and 2-sample Mendelian randomization (MR). The first part involved a 2-sample MR study and a comprehensive meta-analysis. Differences in the results were assessed using inverse-variance weighting. Heterogeneity was examined using the Cochrane Q statistical test. The leave-one-out method was applied for sensitivity analysis. The potential horizontal pleiotropic effect was assessed using the MR-Egger intercept technique. The second part involved differential gene analysis and weighted gene coexpression network analysis. Subsequently, we overlapped and consolidated the results from the 2 parts to identify the key genes between them. MR analysis results suggested a statistically significant correlation between the incidence of PAD and T2D (odds ratio: 1.22, 95% confidence interval: 1.13-1.32, P = 3.74e-07). We anticipated a pivotal role for TCF7L2 in PAD and T2D. T2D was significantly associated with PAD risk. Simultaneously, the study deepened our understanding of the underlying mechanisms of both diseases, proposing TCF7L2 as a promising target.

Identification and Evaluation of Hub Long Non-Coding RNAs and mRNAs in PM2.5-Induced Lung Cell Injury

Exposure to air pollution, especially fine particulate matter (PM2.5), is closely linked to various adverse health effects, particularly in the respiratory system. The present study was designed to investigate the lncRNA-mRNA interactions in PM2.5-induced lung cell injury using weighted gene co-expression network analysis (WGCNA). We downloaded the gene expression data of GSE138870 from the Gene Expression Omnibus (GEO) database and screened for differentially expressed lncRNAs and mRNAs. We constructed co-expression modules with WGCNA. Furthermore, functional enrichment analysis was also performed. We also constructed lncRNA-mRNA co-expression networks and lncRNA-mRNA-pathway networks to identify key regulatory relationships. The results revealed several modules significantly correlated with PM2.5-induced lung injury, such as the turquoise and blue modules. Genes within these modules were enriched in pathways related to signal transduction, metabolism, and cancer. Hub lncRNAs in the turquoise module, including LOC100129034 and CROCCP2, were found to be co-expressed with mRNAs involved in apoptosis and proliferation regulation. In the blue module, lnc-CLVS2-2 and GARS1-DT were connected to genes related to cell migration, invasion, and lung injury. These findings contribute novel perspectives to the molecular mechanisms involved in PM2.5-induced lung injury and suggest that WGCNA could be a valuable tool for predicting and understanding this disease process.

Mechanism of METTL3 in the proliferation, invasion, and migration of intrahepatic cholangiocarcinoma cells via m6A modification

Intrahepatic cholangiocarcinoma (ICC) is a primary invasive malignant tumor. This study was conducted to explore the role of methyltransferase-like 3 (METTL3)-mediated m6A modification in ICC cells and provide novel targets for ICC treatment. Levels of METTL3/YTH N6-methyladenosine RNA binding protein 2 (YTHDF2)/Nedd4 family interacting protein 1 (NDFIP1) in cells were determined. Cell viability, proliferation, invasion, and migration were evaluated. The enrichments of METTL3, YTHDF2, and m6A on NDFIP1 mRNA were analyzed. The mRNA stability was determined. Inhibition of YTHDF2 or NDFIP1 was combined with si-METTL3 to confirm the mechanism. The role of METTL3 in vivo was verified. METTL3 was overexpressed in ICC cells. METTL3 silencing suppressed ICC cell malignant behaviors, which were reversed by METTL3 overexpression. METTL3 increased m6A modification on NDFIP1 mRNA, facilitated YTHDF2 recognition of m6A, and promoted NDFIP1 mRNA degradation, thereby suppressing NDFIP1 expression. YTHDF2 inhibition increased NDFIP1 mRNA levels. NDFIP1 downregulation partially reversed the inhibitory effects of si-METTL3 on ICC cell behaviors, while NDFIP1 overexpression partially reversed the promotive effects of METTL3 on ICC cell behaviors. METTL3 downregulation suppressed ICC growth by increasing NDFIP1 expression. In conclusion, METTL3 aggravates ICC cell proliferation, invasion, and migration by promoting the degradation of NDFIP1 mRNA in a YTHDF2-dependent manner.

Bioinformatics in vivo and in vitro assays identified miR-486-5p as a tumor suppressor miRNA in hepatocellular carcinoma

Background

This study aimed to explore key microRNAs (miRNAs) and their effects on hepatocellular carcinoma (HCC) progression.

Methods

Key deregulated miRNAs in HCC were screened from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The anti-cancer effects of miR-486-5p were validated using a cell counting kit-8 assay, flow cytometry, scratch assay, transwell assay, and an orthotopic transplantation tumor model. Furthermore, the expression, clinical significance, and function of miR-486-5p and its targets were predicted using bioinformatics. Additionally, a luciferase reporter assay was performed to validate the miR-486-5p target.

Results

By integrating multiple datasets from TCGA and GEO databases, we identified miR-486-5p as the only lowly expressed miRNA in HCC, whose expression was also associated with clinical features. Additionally, miR-486-5p exhibited anti-cancer properties both in vitro and in vivo. Ser/Arg-rich splicing factor 3 (SRSF3) was the predicted target of miR-486-5p, and this finding was further supported by correlation analysis, quantitative polymerase chain reaction, and luciferase reporter assays. Furthermore, SRSF3 expression was upregulated, and high SRSF3 expression was correlated with poor survival in patients with HCC. According to Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, SRSF3 promotes cancer-related pathways.

Conclusion

miR-486-5p suppresses cancer progression in HCC by interacting with SRSF3. Therefore, miR-486-5p and SRSF3 may serve as promising therapeutic targets for HCC treatment.

Comparative transcriptomes and WGCNA reveal hub genes for spike germination in different quinoa lines

BACKGROUND

Quinoa, as a new food crop, has attracted extensive attention at home and abroad. However, the natural disaster of spike germination seriously threatens the quality and yield of quinoa. Currently, there are limited reports on the molecular mechanisms associated with spike germination in quinoa.

RESULTS

In this study, we utilized transcriptome sequencing technology and successfully obtained 154.51 Gb of high-quality data with a comparison efficiency of more than 88%, which fully demonstrates the extremely high reliability of the sequencing results and lays a solid foundation for subsequent analysis. Using these data, we constructed a weighted gene co-expression network (WGCNA) related to starch, sucrose, α-amylase, and phenolic acid metabolites, and screened six co-expression modules closely related to spike germination traits. Two of the modules associated with physiological indicators were analyzed in depth, and nine core genes were finally predicted. Further functional annotation revealed four key transcription factors involved in the regulation of dormancy and germination processes: gene LOC110698065, gene LOC110696037, gene LOC110736224, and gene LOC110705759, belonging to the bHLH, NF-YA, MYB, and FAR1 gene families, respectively.

CONCLUSIONS

These results provide clues to identify the core genes involved in quinoa spike germination. This will ultimately provide a theoretical basis for breeding new quinoa varieties with resistance.

Co-expression network and survival analysis of breast cancer inflammation and immune system hallmark genes

The tertiary lymphoid structure (TLS) plays a central role in cancer immune response, and its gene expression pattern, called the TLS signature, has shown prognostic value in breast cancer. The formation of TLS and tumor-associated high endothelial venules (TA-HEVs), responsible for lymphocytic infiltration within the TLS, is associated with the expression of cancer hallmark genes (CHGs) related to immunity and inflammation. In this study, we performed co-expression network analysis of immune- and inflammation-related CHGs to identify predictive genes for breast cancer. In total, 382 immune- and inflammation-related CHGs with high expression variance were extracted from the GSE86166 microarray dataset of patients with breast cancer. CHGs were classified into five modules by applying weighted gene co-expression network analysis. The survival analysis results for each module showed that one module comprising 45 genes was statistically significant for relapse-free and overall survival. Four network properties identified key genes in this module with high prognostic prediction abilities: CD34, CXCL12, F2RL2, JAM2, PROS1, RAPGEF3, and SELP. The prognostic accuracy of the seven genes in breast cancer was synergistic and exceeded that of other predictors in both small and large public datasets. Enrichment analysis predicted that these genes had functions related to leukocyte infiltration of TA-HEVs. There was a positive correlation between key gene expression and the TLS signature, suggesting that gene expression levels are associated with TLS density. Co-expression network analysis of inflammation- and immune-related CHGs allowed us to identify genes that share a standard function in cancer immunity and have a high prognostic predictive value. This analytical approach may contribute to the identification of prognostic genes in TLS.

Apoptosis-associated genetic mechanisms in the transition from rheumatoid arthritis to osteoporosis: A bioinformatics and functional analysis approach

This study explores the mechanisms of glucocorticoid-induced osteoporosis (OP) and Rheumatoid arthritis (RA), focusing on apoptosis and its role in the progression from RA to OP. Using microarray data from the GEO database, differential gene expression analysis was conducted with the limma package, identifying significant genes in RA and OP. Weighted Gene Co-expression Network Analysis (WGCNA) further examined gene relationships with the disease status, identifying co-expression patterns. Key genes were pinpointed by intersecting differentially expressed genes from RA and OP datasets with WGCNA module genes. Functional enrichment analysis using the "clusterProfiler" package focused on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Machine learning methods, including Lasso and Random Forest, refined the selection of key genes related to apoptosis. Immune infiltration analysis using CIBERSORT assessed immune cell differences between disease and normal samples. The study highlighted two crucial genes: ATXN2L and MMP14. These genes were identified through various analyses and found to be significantly associated with the progression of RA and OP. Gene Set Enrichment Analysis of ATXN2L and MMP14 revealed their involvement in specific biological processes and pathways. Correlation analysis between these key genes and immune cell infiltration showed significant associations. The ROC analysis evaluated the diagnostic performance of ATXN2L and MMP14, with miRNA regulatory networks related to these genes also predicted. In summary, this research provides valuable insights into the molecular mechanisms of RA and OP, emphasizing the importance of apoptosis and immune processes.

MiRNA target enrichment analysis of co-expression network modules reveals important miRNAs and their roles in breast cancer progression

Breast cancer has the highest incidence and is the fifth cause of death in cancers. Progression is one of the important features of breast cancer which makes it a life-threatening cancer. MicroRNAs are small RNA molecules that have pivotal roles in the regulation of gene expression and they control different properties in breast cancer such as progression. Recently, systems biology offers novel approaches to study complicated biological systems like miRNAs to find their regulatory roles. One of these approaches is analysis of weighted co-expression network in which genes with similar expression patterns are considered as a single module. Because the genes in one module have similar expression, it is rational to think the same regulatory elements such as miRNAs control their expression. Herein, we use WGCNA to find important modules related to breast cancer progression and use hypergeometric test to perform miRNA target enrichment analysis and find important miRNAs. Also, we use negative correlation between miRNA expression and modules as the second filter to ensure choosing the right candidate miRNAs regarding to important modules. We found hsa-mir-23b, hsa-let-7b and hsa-mir-30a are important miRNAs in breast cancer and also investigated their roles in breast cancer progression.

Identification of Key Genes Correlated with Economic Trait Superiorities and Their SNP Screening Through Transcriptome Comparisons, WGCNA and Pearson Correlation Coefficient in the Sea Cucumber Apostichopus Japonicus

Variation in morphology-driven economic traits is a common issue hindering the development of the sea cucumber aquaculture industry. In this study, transcriptome comparisons, weighted gene correlation network analysis (WGCNA) and Pearson correlation coefficient (PCC) were first employed to identify key genes correlated with morphological variation in the sea cucumber Apostichopus japonicus, after which the relationship between identified key genes (relative expression and genotype) and economic trait phenotypes was investigated to screen potential biomarker targets for molecular-assisted breeding. The results showed that three genes (putative ficolin-2, fibrinogen c domain-containing protein 1, and angiopoietin-4) were closely associated with economic trait superiorities. Two single nucleotide polymorphisms (SNPs) were identified in the putative ficolin-2 gene as having a strong correlation with body weight and papilla number. The findings from this study will enrich breeding biomarker resources and benefit the development of molecular-assisted breeding techniques in sea cucumber aquaculture.

Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach

INTRODUCTION

Peri-implantitis, a destructive inflammatory condition affecting the tissues surrounding dental implants, shares pathological similarities with periodontitis, a chronic inflammatory disease that impacts the supporting structures of natural teeth. This study utilizes a network-based approach to classify interactome hub genes associated with peri-implantitis and periodontitis, aiming to improve understanding of disease mechanisms and identify potential therapeutic targets.

METHODS

We employed gradient boosting and Weighted Gene Co-expression Network Analysis (WGCNA) to predict and classify these interactome hub genes. Gene expression data related to these diseases were sourced from the NCBI GEO dataset GSE223924, and differential gene expression analysis was conducted using the NCBI GEO R tool. Through WGCNA, we constructed a co-expression network to identify key hub genes, while gradient boosting was used to predict these hub genes.

RESULTS

Our analysis revealed a co-expression network comprising 216 genes, including prominent hub genes such as IL17RC, CCN2, BMP7, TPM1, and TIMP1, which are implicated in periodontal disease. The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis.

DISCUSSION

These identified genes play roles in inflammation, osteoclast genesis, angiogenesis, and immune response regulation. This study highlights essential hub genes and molecular pathways associated with peri-implantitis and periodontitis, suggesting potential therapeutic targets for developing innovative treatment strategies.

Identification and validation of a novel prognostic signature and key genes related to development of anaplastic thyroid carcinoma

BACKGROUND

Anaplastic thyroid carcinoma (ATC) is a rare but the most aggressive type of thyroid carcinoma. Nevertheless, limited advances were made to reduce mortality and improve survival over the last decades. Therefore, identifying novel diagnostic biomarkers and therapeutic targets for ATC patients is still needed.

MATERIALS AND METHODS

RNA sequencing data and corresponding clinical features were available from GEO and TCGA databases. We integrated WGCNA and PPI network analysis to identify hub genes associated with ATC development, and RT-qPCR was employed for data verification. Univariate and LASSO Cox regression analyses were used to generate prognostic signatures.

RESULTS

Based on PPI and WGCNA, 6 hub genes were identified, namely KIF2C, PBK, TOP2A, CDK1, KIF20A, and ASPM, which play vital roles in ATC development. Subsequently, RT-qPCR experiments showed that most of these genes were significantly upregulated in CAL-62 cells compared to Nthy-ori 3-1 cells. Moreover, a prognostic signature featuring GPSM2, FGF5, ASXL3, CYP4B1, CLMP, and DUXAP9 was generated, which was also verified by RT-qPCR results and proved as an independent predictor of poorer prognosis of ATC. Additionally, a nomogram incorporating the risk score and clinicopathological parameters was further constructed for accurate prediction of 1-, 3- and 5-year survival probabilities of ATC.

CONCLUSIONS

Our study identified 6 key genes critical to ATC development and constructed a prognostic signature. These findings provide reliable biomarkers and a relatively comprehensive tumorigenesis profile of ATC, which may inform future strategies for clinical diagnosis and pharmaceutical design.

Bioinformatics Analysis and Experimental Validation of Endoplasmic Reticulum Stress-Related Genes in Osteoporosis

Background

Endoplasmic reticulum stress (ERS) is closely associated with Osteoporosis (OP). In order to explore the role of ERS related genes in OP and its molecular mechanism.

Methods

OP-related transcriptome data were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to screen OP-related genes. Differentially expressed ERS-related genes (DE-ERSGs) between OP and controls were identified by overlapping OP-related, differentially expressed genes (DEGs), and ERS-related genes. ERS-related genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore their functions. Receiver operating characteristic (ROC) curves assessed the diagnostic value of DE-ERSGs, and comparative toxicogenomics database (CTD) was used to predict targeting agents for key DE-ERSGs. Finally, biomarker expression was verified by real time quantitative polymerase chain reaction (RT-qPCR).

Results

A total of 10 DE-ERSGs were screened in OP patients. GO and KEGG analyses indicated their enrichment in Alcoholic liver disease, Endometrial cancer, and Glycerolipid metabolism. ROC curve analysis revealed that RPN2, FOXO3, ERGIC2, and MYO9A had significant diagnostic value, thus being identified as key DE-ERSGs. Moreover, the key DE-ERSGs-drug interaction network showed that some drugs such as bisphenol A, Cisplatin, Cyclosporine, and Valproic Acid might play roles by targeting key DE-ERSGs in OP. The expression validation analysis of key DE-ERSGs revealed that RPN2, ERGIC2, and MYO9A was significantly expressed in the GSE62402. Ultimately, The blood samples RT-qPCR verification results show that RPN2, ERGIC2, and MYO9A were significantly lower in OP samples compared to normal samples (p < 0.05), whereas there was no difference in the expression levels of FOXO3.

Conclusion

RPN2, FOXO3, ERGIC2 and MYO9A as the biomarkers associated with ERS in OP by bioinformatics analysis, which may provide new biological targets for clinical treatment.

Comprehensive analysis of clinical features, mRNA splicing, and immunological role of REEP5 in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignancy within the digestive system, characterized by high incidence and mortality rates. The biological role of REEP5 in ESCC progression remains poorly understood, despite its associations with various diseases, potentially accelerating tumor malignancy. We retrieved RNA-seq data and clinical information from 179 ESCC patients from the Gene Expression Omnibus (GEO) and 93 patients from The Cancer Genome Atlas (TCGA) databases. Bioinformatics analyses were conducted to explore the biological functions of REEP5 in ESCC, its role in the tumor microenvironment, and its prognostic value. Additionally, utilizing single-cell RNA-seq (scRNA-seq) data from 3 ESCC patients in the GEO database, we performed cluster analyses to investigate cell-specific expression differences of REEP5 between cancerous and adjacent non-cancerous tissues. Molecular biology experiments were also conducted to validate REEP5 expression disparities between tumor and non-tumor tissues. Compared to normal tissues, REEP5 was significantly enriched in ESCC tissues. High REEP5 expression was closely associated with poor prognosis in ESCC patients. Gene Ontology (GO) analysis revealed strong correlations between REEP5 and processes such as mRNA splicing and protein stabilization. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) indicated positive correlations between REEP5 and mRNA spliceosome assembly and disassembly. Pearson correlation analysis demonstrated positive associations between REEP5 and cancer-inhibitory immune checkpoints CTLA-4, TIM-3, and HVEM. Single-cell clustering and CIBERSORT analysis showed that REEP5 expression was closely related to T-cell infiltration in ESCC, with significant enrichment effects observed in CD8+ T-cell infiltration. REEP5 expression is closely correlated with the pathological and molecular pathology of ESCC, potentially playing a crucial role in Mast cell or T-cell-mediated immune responses in ESCC. Therefore, REEP5 holds promise as a novel therapeutic target for ESCC.

Assessment of pulmonary fibrosis using weighted gene co-expression network analysis

For many industrial chemicals toxicological data is sparse regarding several regulatory endpoints, so there is a high and often unmet demand for NAMs that allow for screening and prioritization of these chemicals. In this proof of concept case study we propose multi-gene biomarkers of compounds' ability to induce lung fibrosis and demonstrate their application in vitro. For deriving these biomarkers we used weighted gene co-expression network analysis to reanalyze a study where the time-dependent pulmonary gene-expression in mice treated with bleomycin had been documented. We identified eight modules of 58 to 273 genes each which were particularly activated during the different phases (inflammatory; acute and late fibrotic) of the developing fibrosis. The modules' relation to lung fibrosis was substantiated by comparison to known markers of lung fibrosis from DisGenet. Finally, we show the modules' application as biomarkers of chemical inducers of lung fibrosis based on an in vitro study of four diketones. Clear differences could be found between the lung fibrosis inducing diketones and other compounds with regard to their tendency to induce dose-dependent increases of module activation as determined using a previously proposed differential activation score and the fraction of differentially expressed genes in the modules. Accordingly, this study highlights the potential use of composite biomarkers mechanistic screening for compound-induced lung fibrosis.

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.

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.

Promotion of apoptosis in melanoma cells by taxifolin through the PI3K/AKT signaling pathway: Screening of natural products using WGCNA and CMAP platforms

Melanoma is a skin cancer originating from melanocytes. The global incidence rate of melanoma is rapidly increasing, posing significant public health challenges. Identifying effective therapeutic agents is crucial in addressing this growing problem. Natural products have demonstrated promising anti-tumor activity. In this study, a plant flavonoid, taxifolin, was screened using Weighted Correlation Network Analysis (WGCNA) in combination with the Connectivity Map (CMAP) platform. Taxifolin was confirmed to inhibit the proliferation, migration, and invasion ability of melanoma A375 and MV-3 cells by promoting apoptosis. Additionally, it suppressed the Epithelial-Mesenchymal Transition (EMT) process of melanoma cells. Cyber pharmacological analysis revealed that taxifolin exerts its inhibitory effect on melanoma through the PI3K/AKT signaling pathway, specifically by downregulating the protein expression of p-PI3K and p-AKT. Notably, the addition of SC-79, an activator of the PI3K/AKT signaling pathway, reversed the effects of taxifolin on cell migration and apoptosis. Furthermore, in vivo experiments demonstrated that taxifolin treatment slowed tumor growth in mice without significant toxic effects. Based on these findings, taxifolin holds promise as a potential drug for melanoma treatment.

POU Class 2 Homeobox Associating Factor 1, as a Hub Candidate Gene in OP, Relieves Osteoblast Apoptosis

Increasing evidence suggests that osteoblast apoptosis contributes to the pathogenesis of postmenopausal osteoporosis (PMOP). This study aimed to identify a hub gene associated with osteoporosis (OP) progression and its functions. We utilized the GSE68303 expression dataset from GEO database and conducted weighted gene co-expression network analysis (WGCNA) to investigate changes in co-expressed genes between sham and ovariectomy (OVX) groups. Differentially expressed genes (DEGs) were identified using the "limma" R package on GSE68303 dataset. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. A protein-protein interaction (PPI) network was constructed using the STRING database, which was visualized by Cytoscape software. The top ten hub genes were screened using the Cytohubba plugin, among which POU class 2 homeobox associating factor 1 (POU2AF1), an OP-related hub gene, showed a significant increase in OVX-induced mouse model based on immunohistochemical staining. Inhibition of POU2AF1 suppressed cell viability, induced cell cycle arrest at the G1 phase, and promoted osteoblast apoptosis as demonstrated by CCK-8 assay, flow cytometry analysis, and TUNEL assay. Moreover, overexpression of POU2AF1 decreased cleaved caspase-3/-8/-9 expression while increasing cyclinD1 and Ki67 expression in MC3T3-E1 and hFOB1.19 cells. Therefore, POU2AF1 may serve as a potential diagnostic biomarker for slowing down the progression of OP.

HCS-hierarchical algorithm for simulation of omics datasets

MOTIVATION

Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducing the dataset to include only those variables that are related to the studied phenomena. Existing simulators of the omics data were mostly developed with the goal of improving the methods for generations of high-quality data, that correspond with the highest possible fidelity to the real level of molecular markers in the biological materials. The current study aims to simulate the data on a higher level of generalization. Such datasets can then be used to perform tests of the feature selection and ML algorithms on systems that have structures mimicking those of real data, but where the ground truth may be implanted by design. They can also be used to generate contrast variables with the desired correlation structure for the feature selection.

RESULTS

We proposed the algorithm for the reconstruction of the omic dataset that, with high fidelity, preserves the correlation structure of the original data with a reduced number of parameters. It is based on the hierarchical clustering of variables and uses principal components of the clusters. It reproduces well topological descriptors of the correlation structure. The correlation structure of the principal components of the clusters then is used to obtain datasets with correlation structures similar to the original data but not correlated with the original variables.

AVAILABILITY AND IMPLEMENTATION

The code and data is available at: https://github.com/p100mma/hcrs_omics.

Effect of Different Dietary Iron Contents on Liver Transcriptome Characteristics in Wujin Pigs

Iron is an important trace element that affects the growth and development of animals and regulates oxygen transport, hematopoiesis, and hypoxia adaptations. Wujin pig has unique hypoxic adaptability and iron homeostasis; however, the specific regulatory mechanisms have rarely been reported. This study randomly divided 18 healthy Wujin piglets into three groups: the control group, supplemented with 100 mg/kg iron (as iron glycinate); the low-iron group, no iron supplementation; and the high-iron group, supplemented with 200 mg/kg iron (as iron glycinate). The pre-feeding period was 5 days, and the formal period was 30 days. Serum was collected from empty stomachs before slaughter and at slaughter to detect changes in the serum iron metabolism parameters. Gene expression in the liver was analyzed via transcriptome analysis to determine the effects of low- and high-iron diets on transcriptome levels. Correlation analysis was performed for apparent serum parameters, and transcriptome sequencing was performed using weighted gene co-expression network analysis to reveal the key pathways underlying hypoxia regulation and iron metabolism. The main results are as follows. (1) Except for the hypoxia-inducible factor 1 (HIF-1) content (between the low- and high-iron groups), significant differences were not observed among the serum iron metabolic parameters. The serum HIF-1 content of the low-iron group was significantly higher than that of the high-iron group (p < 0.05). (2) Sequencing analysis of the liver transcriptome revealed 155 differentially expressed genes (DEGs) between the low-iron and control groups, 229 DEGs between the high-iron and control groups, and 279 DEGs between the low- and high-iron groups. Bioinformatics analysis showed that the HIF-1 and transforming growth factor-beta (TGF-β) signaling pathways were the key pathways for hypoxia regulation and iron metabolism. Four genes were selected for qPCR validation, and the results were consistent with the transcriptome sequencing data. In summary, the serum iron metabolism parameter results showed that under the influence of low- and high-iron diets, Wujin piglets maintain a steady state of physiological and biochemical indices via complex metabolic regulation of the body, which reflects their stress resistance and adaptability. The transcriptome results revealed the effects of low-iron and high-iron diets on the gene expression level in the liver and showed that the HIF-1 and TGF-β signaling pathways were key for regulating hypoxia adaptability and iron metabolism homeostasis under low-iron and high-iron diets. Moreover, HIF-1α and HEPC were the key genes. The findings provide a theoretical foundation for exploring the regulatory pathways and characteristics of iron metabolism in Wujin pigs.

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.

MZT2A serves as a prognostic biomarker and promotes the progression of kidney renal clear cell carcinoma

MZT2A is a novel core component in the γ-tubulin ring complex and aberrantly expressed in some types of tumors. However, MZT2A expression pattern across different cancers and its role in kidney renal clear cell carcinoma have not been sufficiently investigated. A thorough analysis of MZT2A expression landscape across 33 cancer types was conducted, utilizing 712 normal samples and 9807 tumor samples from TCGA (version 37.0), as well as 5112 normal samples from the GTEx databases. MZT2A's impact on KIRC cell viability and proliferation were evaluated through BrdU assays and monitored by cell imaging readers in MZT2A-expressing plasmid or siRNA-transfected cells. Additionally, the effects of MZT2A on cell apoptosis and cell cycle were detected using flow cytometry and Western blot. WGCNA analysis was employed to construct a co-expression gene network associated with MZT2A expression in KIRC, and Pearson correlation coefficient was utilized to examine the relationships between pairs of genes. MZT2A is overexpressed in 25 out of 33 types of cancer, including KIRC. In KIRC, high level of MZT2A was associated with higher clinical stage progression and poorer patients' survival. Downregulation of MZT2A decreased KIRC cell proliferation, retarded cell cycle and promoted apoptosis, while upregulation of MZT2A produced the opposite effects. WGCNA analysis and in vitro experiments revealed that MZT2A activated PI3K/AKT signaling pathway in KIRC. In all, MZT2A was overexpressed in most types of tumors. MZT2A served as an oncogene in KIRC and might be a potential target for guiding future treatments.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

(-)-Epicatechin ameliorates type 2 diabetes mellitus by reshaping the gut microbiota and Gut-Liver axis in GK rats

As one of the most abundant plant polyphenols in the human diet, (-)-epicatechin (EC) can improve insulin sensitivity and regulate glucose homeostasis. However, the primary mechanisms involved in EC anti-T2DM benefits remain unclear. The present study explored the effects of EC on the gut microbiota and liver transcriptome in type 2 diabetes mellitus (T2DM) Goto-Kakizaki rats for the first time. The findings showed that EC protected glucose homeostasis, alleviated systemic oxidative stress, relieved liver damage, and increased serum insulin. Further investigation showed that EC reshaped gut microbiota structure, including inhibiting the proliferation of lipopolysaccharide (LPS)-producing bacteria and reducing serum LPS. In addition, transcriptome analysis revealed that the insulin signaling pathway may be the core pathway of the EC anti-T2DM effect. Therefore, EC may modulate the gut microbiota and liver insulin signaling pathways by the gut-liver axis to alleviate T2DM. As a diet supplement, EC has promising potential in T2DM prevention and treatment.

Identification of immune- and oxidative stress-related signature genes as potential targets for mRNA vaccines for pancreatic cancer patients

Adenocarcinoma of the pancreas (PAAD) is one of the deadliest malignant tumors, and messenger ribonucleic acid vaccines, which constitute the latest generation of vaccine technology, are expected to lead to new ideas for the treatment of pancreatic cancer. The Cancer Genome Atlas-PAAD and Genotype-Tissue Expression data were merged and analyzed. Weighted gene coexpression network analysis was used to identify gene modules associated with tumor mutational burden among the genes related to both immunity and oxidative stress. Differentially expressed immune-related oxidative stress genes were screened via univariate Cox regression analysis, and these genes were analyzed via nonnegative matrix factorization. After immune infiltration analysis, least absolute shrinkage and selection operator regression combined with Cox regression was used to construct the model, and the usefulness of the model was predicted based on the receiver operating characteristic curve and decision curve analysis curves after model construction. Finally, metabolic pathway enrichment was analyzed using gene set enrichment analysis combined with Kyoto Encyclopedia of Genes and Genomes and gene ontology biological process analyses. This model consisting of the ERAP2, mesenchymal-epithelial transition factor (MET), CXCL9, and angiotensinogen (AGT) genes can be used to help predict the prognosis of pancreatic cancer patients more accurately than existing models. ERAP2 is involved in immune activation and is important in cancer immune evasion. MET binds to hepatocyte growth factor, leading to the dimerization and phosphorylation of c-MET. This activates various signaling pathways, including MAPK and PI3K, to regulate the proliferation, invasion, and migration of cancer cells. CXCL9 overexpression is associated with a poor patient prognosis and reduces the number of CD8 + cytotoxic T lymphocytes in the PAAD tumor microenvironment. AGT is cleaved by the renin enzyme to produce angiotensin 1, and AGT-converting enzyme cleaves angiotensin 1 to produce angiotensin 2. Exposure to AGT-converting enzyme inhibitors after pancreatic cancer diagnosis is associated with improved survival. The 4 genes identified in the present study - ERAP2, MET, CXCL9, and AGT - are expected to serve as targets for messenger ribonucleic acid vaccine development and need to be further investigated in depth.

Feeding Eucommia ulmoides extract enhances protection against high-temperature stress in chicks

Chick's susceptibility to heat stress often leads to growth retardation, immune function impairment, disease, and mortality. This thesis explores the potential ameliorative effect of 0.8% Eucommia ulmoides extract (EUE) into the diet of heat-stressed chicks in a 15-d feeding trial. The investigation reveals that feeding EUE significantly enhances the BW, ADG, AFI, and F/G of chicks experiencing heat stress. Additionally, the EUE groups exhibited higher levels of T-AOC (at 7 and 15d), SOD (at 15 d), GSH-Px (at 15 d), as well as lower MDA concentrations (at 7 and 15d) in chick serum. Pathological changes and H&E staining revealed that EUE effectively improved tissue damage in the duodenum, heart, and stomach induced by heat stress in the chicks. The EUE groups also showed higher levels of IgA (at 7 d), IgG and IgM (at 7 and 15 d). RNA-seq and WGCNA analysis revealed that EUE mitigates cellular damage and losses in heat-stressed chicks primarily through pathways involving signal transduction, protein synthesis and degradation, as well as cell cycle regulation, particularly the latter. This investigation serves as a fundamental and cognitive framework for the development and application of Eucommia ulmoides feed additives aimed at safeguarding the well-being of chicks in adverse environmental conditions.

Screening of key modules and key genes in the prevention of skin cancer development based on gene volcano plot and WGCNA

BACKGROUND

Skin cancer, a prevalent form of cancer that is on the rise worldwide, requires proactive prevention strategies to reduce the burden of screening, treatment, and mortality. The KEGG research highlighted the significant involvement of red module genes in protein digestion and absorption. These findings provide valuable insights into the underlying molecular mechanisms associated with skin cancer susceptibility, offering potential targets for further research and development of preventive strategies.

MATERIALS AND METHODS

Hub genes numbered 130. "limma" in R found 600 DEGs from GSE66359 dataset. DEGs are enriched in BP: chromosome segregation, CC: chromosomal region, and MF: DNA replication origin binding, according to GO analysis. Cell cycle was enriched in DEGs by KEGG and GSEA. Finally, significant genes were COL5A1, CTHRC1, ECM1, FSTL1, KDELR3, and WIPI1.

RESULTS

ECM1 and WIPI1 greatly prevented skin cancer. This study created a coexpression network using WGCNA to investigate skin cancer susceptibility modules and cardiovascular disease genes.

CONCLUSION

Our study finds a module and many important genes that are essential building blocks in the etiology of skin cancer, which may help us understand the molecular mechanisms of disease prevention.

Exploring the prognostic analysis of autophagy and tumor microenvironment based on monocyte cells in lung cancer

A deep understanding of the biological mechanisms of lung cancer offers more precise treatment options for patients. In our study, we integrated data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to investigate lung adenocarcinoma. Analyzing 538 lung cancer samples and 31 normal samples, we focused on 3076 autophagy-related genes. Using Seurat, dplyr, tidyverse, and ggplot2, we conducted single-cell data analysis, assessing the quality and performing Principal Component Analysis (PCA) and t-SNE analyses. Differential analysis of TCGA data using the "Limma" package, followed by immune infiltration analysis using the CIBERSORT algorithm, led us to identify seven key genes. These genes underwent further scrutiny through consensus clustering and gene set variation analysis (GSVA). We developed a prognostic model using Lasso Cox regression and multivariable Cox analysis, which was then validated with a nomogram, predicting survival rates for lung adenocarcinoma. The model's accuracy and universality were corroborated by ROC curves. Additionally, we explored the relationship between immune checkpoint genes and immune cell infiltration and identified two key genes, HLA-DQB1 and OLR1. This highlighted their potential as therapeutic targets. Our comprehensive approach sheds light on the molecular landscape of lung adenocarcinoma and offers insights into potential treatment strategies, emphasizing the importance of integrating single-cell and genomic data in cancer research.

Weighted Gene Co-expression Network Analysis of the Inflammatory Wnt Signaling Reveals Biomarkers Related to Bone Formation

Background and aim Osteocytes regulate bone metabolism and balance through various mechanisms, including the Wnt (Wingless-related integration site signal transduction) signaling pathway. Weighted gene co-expression network analysis (WGCNA) is a computational method to identify functionally related genes based on expression patterns, especially in the Wnt-beta-catenin and osteo-regenerative pathways. This study aims to analyze gene modules of the Wnt signaling pathway from WGCNA analysis. Methods The study used a microarray dataset from the GEO (GSE228306) to analyze differential gene expression in human primary monocytes. The study standardized datasets using Robust Multi-Array Average (RMA) expression measure and Integrated Differential Expression and Pathway (IDEP) analysis tool, building a co-expression network for group-specific component (GC) genes. Results The study uses WGCNA to identify co-expression modules with dysregulated mRNAs, revealing enrichment in Wnt-associated pathways and top hub-enriched genes like colony-stimulating factor 3 (CSF3), interleukin-6 (IL-6), IL-23 subunit alpha (IL23A), suppressor of cytokine signaling 1 (SOCS1), and C-C motif chemokine ligand 19 (CCL19). Conclusion WGCNA analysis of the Wnt signaling pathway will involve functional annotation, network visualization, validation, integration with other omics data, and addressing method limitations for better understanding.

Identification of Immune-Related Gene Signature in Schizophrenia

BACKGROUND

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ.

METHODS

The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ.

RESULTS

A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms.

CONCLUSIONS

This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation

BACKGROUND

Osteoporosis (OP) is characterized by bone mass decrease and bone tissue microarchitectural deterioration in bone tissue. This study identified potential biomarkers for early diagnosis of OP and elucidated the mechanism of OP.

METHODS

Gene expression profiles were downloaded from Gene Expression Omnibus (GEO) for the GSE56814 dataset. A gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) to identify key modules associated with healthy and OP samples. Functional enrichment analysis was conducted using the R clusterProfiler package for modules to construct the transcriptional regulatory factor networks. We used the "ggpubr" package in R to screen for differentially expressed genes between the two samples. Gene set variation analysis (GSVA) was employed to further validate hub gene expression levels between normal and OP samples using RT-PCR and immunofluorescence to evaluate the potential biological changes in various samples.

RESULTS

There was a distinction between the normal and OP conditions based on the preserved significant module. A total of 100 genes with the highest MM scores were considered key genes. Functional enrichment analysis suggested that the top 10 biological processes, cellular component and molecular functions were enriched. The Toll-like receptor signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, osteoclast differentiation, JAK-STAT signaling pathway, and chemokine signaling pathway were identified by Kyoto Encyclopedia of Genes and Genomes pathway analysis. SIRT1 and ZNF350 were identified by Wilcoxon algorithm as hub differentially expressed transcriptional regulatory factors that promote OP progression by affecting oxidative phosphorylation, apoptosis, PI3K-Akt-mTOR signaling, and p53 pathway. According to RT-PCR and immunostaining results, SIRT1 and ZNF350 levels were significantly higher in OP samples than in normal samples.

CONCLUSION

SIRT1 and ZNF350 are important transcriptional regulatory factors for the pathogenesis of OP and may be novel biomarkers for OP treatment.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Weighted gene co-expression network analysis reveals immune evasion related genes in Echinococcus granulosus sensu stricto

Cystic echinococcosis (CE) is a zoonotic disease caused by the tapeworm Echinococcus granulosus sensu lato (s.l). In the intermediate host, this disease is characterized by the growth of cysts in viscera such as liver and lungs, inside of which the parasite develops to the next infective stage known as protoscoleces. There are records that the infected viscera affect the development and morphology of E. granulosus s.l. protoscolex in hosts such as buffalo or humans. However, the molecular mechanisms that drive these differences remains unknown. Weighted gene co-expression network analysis (WGCNA) using a set of RNAseq data obtained from E. granulosus sensu stricto (s.s.) protoscoleces found in liver and lung cysts reveals 34 modules in protoscoleces of liver origin, of which 12 have differential co-expression from protoscoleces of lung origin. Three of these twelve modules contain hub genes related to immune evasion: tegument antigen, tegumental protein, ubiquitin hydrolase isozyme L3, COP9 signalosome complex subunit 3, tetraspanin CD9 antigen, and the methyl-CpG-binding protein Mbd2. Also, two of the twelve modules contain only hypothetical proteins with unknown orthology, which means that there are a group of unknown function proteins co-expressed inside the protoscolex of liver CE cyst origin. This is the first evidence of gene expression differences in protoscoleces from CE cysts found in different viscera, with co-expression networks that are exclusive to protoscoleces from liver CE cyst samples. This should be considered in the control strategies of CE, as intermediate hosts can harbor CE cysts in liver, lungs, or both organs simultaneously.

UvHOS3-mediated histone deacetylation is essential for virulence and negatively regulates ustilaginoidin biosynthesis in Ustilaginoidea virens

Ustilaginoidea virens is the causal agent of rice false smut, which has recently become one of the most important rice diseases worldwide. Ustilaginoidins, a major type of mycotoxins produced in false smut balls, greatly deteriorates grain quality. Histone acetylation and deacetylation are involved in regulating secondary metabolism in fungi. However, little is yet known on the functions of histone deacetylases (HDACs) in virulence and mycotoxin biosynthesis in U. virens. Here, we characterized the functions of the HDAC UvHOS3 in U. virens. The ΔUvhos3 deletion mutant exhibited the phenotypes of retarded growth, increased mycelial branches and reduced conidiation and virulence. The ΔUvhos3 mutants were more sensitive to sorbitol, sodium dodecyl sulphate and oxidative stress/H2 O2 . ΔUvhos3 generated significantly more ustilaginoidins. RNA-Seq and metabolomics analyses also revealed that UvHOS3 is a key negative player in regulating secondary metabolism, especially mycotoxin biosynthesis. Notably, UvHOS3 mediates deacetylation of H3 and H4 at H3K9, H3K18, H3K27 and H4K8 residues. Chromatin immunoprecipitation assays indicated that UvHOS3 regulates mycotoxin biosynthesis, particularly for ustilaginoidin and sorbicillinoid production, by modulating the acetylation level of H3K18. Collectively, this study deepens the understanding of molecular mechanisms of the HDAC UvHOS3 in regulating virulence and mycotoxin biosynthesis in phytopathogenic fungi.

A study on the role of Taxifolin in inducing apoptosis of pancreatic cancer cells: screening results using weighted gene co-expression network analysis

Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor in the pancreas. The incomplete understanding of cancer etiology and pathogenesis, as well as the limitations in early detection and diagnostic methods, have created an urgent need for the discovery of new therapeutic targets and drugs to control this disease. As a result, the current therapeutic options are limited. In this study, the weighted gene co-expression network analysis (WGCNA) method was employed to identify key genes associated with the progression and prognosis of pancreatic adenocarcinoma (PAAD) patients in the Gene Expression Profiling Interactive Analysis (GEPIA) database. To identify small molecule drugs with potential in the treatment of pancreatic adenocarcinoma (PAAD), we compared key genes to the reference dataset in the CMAP database. First, we analyzed the antitumor properties of small molecule drugs using cell counting kit-8 (CCK-8), AO/EB and Transwell assays. Subsequently, we integrated network pharmacology with molecular docking to explore the potential mechanisms of the identified molecules' anti-tumor effects. Our findings indicated that the progression and prognosis of PAAD patients in pancreatic cancer were associated with 11 genes, namely, DKK1, S100A2, CDA, KRT6A, ITGA3, GPR87, IL20RB, ZBED2, PMEPA1, CST6, and MUC16. These genes were filtered based on their therapeutic potential through comparing them with the reference dataset in the CMAP database. Taxifolin, a natural small molecule drug with the potential for treating PAAD, was screened by comparing it with the reference dataset in the CMAP database. Cell-based experiments have validated the potential of Taxifolin to facilitate apoptosis in pancreatic cancer cells while restraining their invasion and metastasis. This outcome is believed to be achieved via the HIF-1 signaling pathway. In conclusion, this study provided a theoretical basis for screening genes related to the progression of pancreatic cancer and discovered potentially active small molecule drugs. The experimental results confirm that Taxifolin has the ability to promote apoptosis in pancreatic cancer cells.

Identification and validation of iron metabolism genes in osteoporosis

BACKGROUND

Osteoporosis is the most common metabolic bone disease in humans. Exploring the expression difference of iron metabolism-related genes in osteoporosis can provide a new target for diagnosis and treatment.

METHODS

First, we used online databases to identify differentially expressed genes (DEGs) related to iron metabolism in patients with osteoporosis. The differential genes were comprehensively analyzed by bioinformatics method (GO, KEGG, GSEA, immune infiltration analysis, PPI). The expression levels of hub genes and important signaling pathways were verified by qRT-PCR and Western blotting.

RESULTS

A total of 23 iron metabolism-related genes with significant differences were identified, which were enriched in "regulation of protein dephosphorylation" and "negative regulation of protein dephosphorylation". The GSEA results, heme metabolism and Myc targets v1 were among the top two pathways, both upregulated. The immune infiltration analysis revealed that the expressions of genes such as ABCA5, D2HGDH, GNAI2, and CTSW were correlated with the infiltration degree of significantly different cells. The PPI network contained 12 differentially expressed iron metabolism-related genes. Additionally, YWHAE, TGFB1, PPP1R15A, TOP2A, and CALR were mined as hub genes using the Cytoscape software. qRT PCR showed that the expression of TGF-β1, YWHAE, TOP2A and CALR increased. We also verified the expression of related proteins and genes in the oxidative stress signaling pathway by qRT PCR and Western blotting. The results showed that Mob1, YAP and TAZ molecules were highly expressed at the gene and protein levels.

CONCLUSIONS

These differentially expressed iron metabolism-related genes could provide new potential targets for the diagnosis and treatment of osteoporosis.

Comprehensive Analysis Identifies Hyaluronan Mediated Motility Receptor and Cell Division Cycle 25C as Potential Prognostic Biomarkers in Head and Neck Squamous Cell Carcinoma

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, but its pathogenic mechanisms remain unclear. This study aimed to identify the potential biomarkers underlying the diagnosis and treatment of HNSCC.

METHODS

Weighted gene co-expression network analysis (WGCNA) followed by pathway enrichment analysis, analysis of infiltrating immune cells, survival analysis, and methylation analysis were applied to identify the potential hub genes underlying the prognosis of HNSCC. The expression of hub genes was validated by immunofluorescence staining.

RESULTS

A total of 10,274 differentially expressed genes (DEGs) were identified. Through WGCNA, the yellow module (R2 = 0.33, P = 2e-14) was confirmed to be the most significantly associated with the histological grade of HNSCC, and the "Cell Cycle" proved to be the most enriched signaling pathway. Based on the results of survival analysis and immune cell infiltration, 10 hub genes (HMMR, CENPK, AURKA, CDC25C, FEN1, CKS1B, MAJIN, PCLAF, SPC25, and STAG3) were identified. Eight of these (excluding MAJIN and STAG3) were confirmed by performing survival analysis using another dataset (GSE41613). Further, we identified 4 methylation loci in 3 hub genes (cg15122828 and cg20554926 in HMMR, cg12519992 in CDC25C, and cg2655739 in KIAA0101/PCLAF) as being significantly related to survival. Finally, we demonstrated the high mRNA and protein expression of HMMR and CDC25C in HNSCC patients.

CONCLUSION

Two real hub genes (HMMR and CDC25C) and 3 methylation loci were identified that could potentially serve as prognostic and therapeutic targets for HNSCC, which is significant for studying the pathological mechanisms underlying HNSCC and for developing novel therapies for this disease.