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To explore the hub gene closely related to the progression of gastric cancer (GC), so as to provide a theoretical basis for revealing the therapeutic mechanism of GC. The gene expression profile and clinical data of GSE15459 in Gene Expression Omnibus (GEO) database were downloaded. The weighted gene co-expression network analysis (WGCNA) was used to screen the key modules related to GC progression. Survival analysis was used to assess the influence of hub genes on patients' outcomes. CIBERSORT analysis was used to predict the tissue infiltrating immune cells in patients. Immunohistochemical staining was conducted to further verify the expression of hub genes. Through WGCNA, a total of 26 co-expression modules were constructed, in which salmon module and royalblue module had strong correlation with GC progression. The results of enrichment analysis showed that genes in the two modules were mainly involved in toll-like receptor signaling pathway, cholesterol metabolism and neuroactive ligand-receptor interaction. Six hub genes ( , , , , and ) related to GC progression were screened. Survival analysis showed overall survival in the high expression group was significantly lower than that in the low expression group. CIBERSORT analysis revealed that immune characteristics difference between patients in early stage and advanced stage. Immunohistochemical results confirmed that , , and were significantly associated with disease progression in GC. Our study identified that , , and played important roles in the progression of GC, and their specific mechanisms are worth further study.

Terpenoids are the key bioactive constituents of Rehmannia glutinosa, a valuable medicinal plant. The rising economic and pharmaceutical values of R. glutinosa have necessitated elucidating the metabolic pathways governing terpenoid metabolism. Herein, we integrated transcriptome sequencing (RNA−seq) and weighted gene co−expression network analysis (WGCNA) to identify co−expression modules and hub genes closely linked to terpenoid biosynthesis in tuberous roots of cultivar ‘Wen85−5’ across eight developmental stages. A total of 20996 differentially expressed genes (DEGs) were identified, with GO/KEGG annotation analysis confirming enrichment in various metabolic and cellular processes. Further, we screened 16 terpenoid biosynthesis−related DEGs, mapping to the MVA (6 genes) and MEP (10 genes) pathways. WGCNA clustered 19957 DEGs into 16 modules, of which 9 modules (containing 28 hub genes) were potentially participated in the regulation of terpenoid biosynthesis. Functional annotation of these 9 modules revealed enrichment in secondary metabolic processes, as well as the biosynthetic pathways of terpenoids and polyketides, secondary metabolites, sesquiterpenes, and triterpenes. Among the 28 hub genes, 11 and 17 were mapped to the MVA and MEP pathways, respectively. Co-expression network analysis revealed intricate interactions between hub genes and between hub genes and key transcription factors. Notably, 11 of these hub genes exhibited conserved co-expression patterns across multiple modules and served as candidate genes potentially associated with terpenoid biosynthesis. The expression profiles of these 11 hub genes, inferred from FPKM values, were further validated by RT-qPCR, demonstrating consistent expression trends. This study provides the first systematic characterization of terpenoid biosynthetic network in R. glutinosa, offering critical insights and a valuable genetic resource for metabolic engineering to enhance terpenoid production.

The SWEET (Sugars Will Eventually be Exported Transporters) gene family plays crucial roles in sugar transport, plant development, and abiotic stress responses. However, its pan-genomic characteristics and practical breeding potential in Brassica napus remain unclear. In this study, we systematically identified 96 BnSWEET genes across eight rapeseed genomes, revealing extensive presence/absence variations (PAVs) and clear classification into four subfamilies (Groups I–IV). Evolutionary analyses highlighted diverse selection pressures, with specific members (e.g., BnSWEET37 ) exhibiting strong signatures of positive selection (Ka/Ks > 1.0), while others were conserved under purifying selection. Haplotype analysis revealed that elite alleles of BnSWEET5 are significantly associated with seed oil content, silique length, and germination vigor. Furthermore, Weighted Gene Co-expression Network Analysis (WGCNA) identified BnSWEET47 as a core regulatory hub coordinating sugar partitioning and secondary metabolism. Transcriptomic profiling and RT-qPCR validation confirmed that BnSWEET genes, particularly those representing specific structural and evolutionary variants (e.g., the PAV-specific BnSWEET26 ), exhibit highly heterogeneous and tissue-specific temporal expression patterns under multiple abiotic stresses. Overall, this study elucidates the evolutionary and functional divergence of the BnSWEET family and proposes a practical breeding pathway—utilizing the identified elite haplotypes and key PAV variants for precision germplasm screening—to optimize source-sink balance and enhance abiotic stress resilience in B. napus .

Soluble sugars, organic acids and volatiles are important components that determine unique fruit flavor and consumer preferences. However, the metabolic dynamics and underlying regulatory networks that modulate overall flavor formation during fruit development and ripening remain largely unknown for most fruit species. In this study, by integrating flavor-associated metabolism and transcriptome data from 12 fruit developmental and ripening stages of Actinidia chinensis cv Hongyang, we generated a global map of changes in the flavor-related metabolites throughout development and ripening of kiwifruit. Using this dataset, we constructed complex regulatory networks allowing to identify key structural genes and transcription factors that regulate the metabolism of soluble sugars, organic acids and important volatiles in kiwifruit. Moreover, our study revealed the regulatory mechanism involving key transcription factors regulating flavor metabolism. The modulation of flavor metabolism by the identified key transcription factors was confirmed in different kiwifruit species providing the proof of concept that our dataset provides a suitable tool for clarification of the regulatory factors controlling flavor biosynthetic pathways that have not been previously illuminated. Overall, in addition to providing new insight into the metabolic regulation of flavor during fruit development and ripening, the outcome of our study establishes a foundation for flavor improvement in kiwifruit.

Breast cancer (BRCA) has the highest incidence among female malignancies, and the prognosis for these patients remains poor. In this study, core modules and central genes related to BRCA were identified through a weighted gene co-expression network analysis (WGCNA). Gene expression profiles and clinical data of GSE25066 were obtained from the Gene Expression Omnibus (GEO) database. The result was validated with RNA-seq data from The Cancer Genome Atlas (TCGA) and Oncomine database. The top 30 key module genes with the highest intramodule connectivity were selected as the core genes (R = 0.40). According to TCGA and Oncomine datasets, seven genes were selected as candidate hub genes. Following further experimental verification, four hub genes (FAM171A1, NDFIP1, SKP1, and REEP5) were retained. We identified four hub genes as candidate biomarkers for BRCA. These hub genes may provide a theoretical basis for targeted therapy against BRCA.

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8+ T cells while CDCA8 with CD4+ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed. Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database. Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters. By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.

The discovery of bioactive natural products is often challenged by the complexity of isolating and characterizing active compounds within diverse mixtures. Previously, we introduced a [sup.1]H NMR-based weighted gene correlation network analysis (WGCNA) approach to identify spectral features linked to growth inhibitory activity of Piper (Piperaceae) leaf extracts against model plant, fungal, and bacterial organisms. This method enabled us to prioritize specific spectral features linked to bioactivity, offering a targeted approach to natural product discovery. In this study, we validate the predictive capacity of the WGCNA by isolating the compounds responsible for the bioactivity-associated resonances and confirming their antifungal efficacy. Using growth inhibition assays, we verified that the isolated compounds, including three novel antifungal agents, exhibited significant bioactivity. Notably, one of these compounds contains a rare imidazolium heterocyclic motif, marking a new structural class in Piper. These findings substantiate the [sup.1]H NMR-based WGCNA as a reliable tool for identifying structural types associated with biological activity, streamlining the process of discovering bioactive natural products in complex extracts.

To investigate exosomal RNAs (long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs)) profiling and their related networks in endometriosis (EMs). RNA sequence was performed in exosomes from ovarian endometriomas (EC), eutopic endometria (EU) and normal endometria (Control) stromal cells. The bioinformatics algorithms evaluated competing endogenous RNA (ceRNA) networks. The top-ranked ceRNA networks were confirmed by RT-PCR. Overlapped differentially expressed 938 lncRNAs, 39 miRNAs and 1449 mRNAs were identified. 13 co-expression modules and 61 ceRNA networks were constructed. This study for the first time shows exosomal RNA biomarkers and lncRNA-related networks in EMs, which reveals a novel molecular mechanism of EMs and provides new resources for EM diagnosis and treatment.

BackgroundInflammatory bowel disease (IBD) is a complex chronic intestinal inflammatory disorder. Oxidative stress (OS) is crucial in the pathogenesis of IBD by promoting inflammation and disrupting the intestinal epithelial barrier.MethodsWeighted gene co-expression network analysis and machine learning were applied to publicly available gene expression data to identify OS-related biomarkers for IBD. Receiver operating characteristic curve, immune infiltration analysis, and single-cell analysis were used to evaluate diagnostic performance and cellular localization. RT-qPCR and Western blotting were conducted to validate gene expression.ResultsThree hub genes (LCN2, APP, and TNFSF4) demonstrated strong diagnostic accuracy (AUC: 0.781, 0.805, and 0.754, respectively) in the discovery cohort (GSE3365). Furthermore, external validation in three independent cohorts also confirmed the robust diagnostic performance of the three hub genes. These genes were enriched in OS and inflammatory pathways, strongly correlated with infiltration of macrophages M0, monocytes, and neutrophils (p<0.01), and were highly expressed in goblet cells, stem cells, and T cells. DSS model validation confirmed significant upregulation at mRNA and protein levels.ConclusionThese OS-associated genes represent novel molecular targets for precise diagnosis and personalized treatment of IBD. This finding highlights the interplay among OS, immune dysregulation, and inflammation in IBD pathogenesis.