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PubMed2026

Transcriptome sequencing provides novel insights into larval development and sexual dimorphism in the firefly Aquatica leii (Coleoptera: Lampyridae).

Fireflies are regarded as one of the most charismatic beetles due to their bioluminescence and ecological importance as bioindicators of freshwater quality. However, molecular mechanisms of larval development and sexual dimorphism in aquatic species remain poorly understood. Here, we performed multi-stage transcriptomic analysis of the aquatic firefly Aquatica leii across larval instars from L2 to L6, together with adult females and males, with three biological replicates per stage. Using time-series expression clustering, differential expression analysis, and weighted gene co-expression network analysis (WGCNA), we characterized the transcriptional dynamics of continuous larval development and the onset of sex-biased gene expression. We identified a critical transcriptional transition occurred at L5-L6, marked by downregulation of early morphogenetic genes and upregulation of juvenile hormone metabolism, oxidoreductase activity, and muscle contraction genes, indicating a shift from growth to metamorphic preparation. WGCNA identified a module strongly correlated with L6 (R = 0.97) enriched for the same functions, confirming a coordinated late-larval program. Notably, genes exhibiting sex-biased expression in adults were already expressed during late larval stages (L5 and L6), and 123 genes progressively upregulated from L2 to L6 showed enrichment in chitin biosynthesis, heart contraction, and ion transport; among these, six genes maintained high expression in adults with clear male-biased (Alei052192, Alei006658, and Alei087054) or female-biased (Alei003725, Alei096818, and Alei074026) patterns. These findings establish that transcriptional foundations for sexual dimorphism and adult tissue formation are laid during late larval stages, providing the first multi-stage transcriptomic resource for aquatic firefly conservation and breeding.

Animals↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2025

Global lncRNA expression profiles in medulloblastoma reveal crucial lncRNA-oncogene interactions in Sonic hedgehog and Group 4.

BACKGROUND: Advances in multi-omic studies have improved medulloblastoma (MB) characterization, yet novel molecular biomarkers are needed to refine tumor biology and therapeutic strategies. Current profiling mainly targets the protein-coding genome, while the potential of noncoding regions remains unexplored. This study aims to identify long noncoding RNAs (lncRNAs), emerging as crucial regulators in MB, as potential key biomarkers specific to molecular group, enhancing understanding of MB's genomic landscape. METHODS: RNA-seq data from 54 Spanish MB patients (C1) and 207 public samples (C2) were analyzed to profile lncRNAs. Expression and Weighted Gene Coexpression Network (WGCNA) analyses were performed to identify lncRNA-oncogene interactions. Group-specific interactions were examined to infer their role in MB pathogenesis and highlight potential lncRNA involvement in disease mechanisms. RESULTS: LncRNA expression profiles identified 4 clusters corresponding to the MB molecular groups, confirming their potential as biomarkers. Expression and WGCNA analyses revealed group-specific lncRNAs for Sonic hedgehog (SHH), Group 3 (Gr3), and Group 4 (Gr4) MB. Lnc-SMARCA2 was exclusively upregulated in SHH MB, and associated with ATOH1 and PDLIM3, key cilium regulators of this group's cell of origin. In Gr4 MB, MGC32805 and LOC107986446 were upregulated and linked to SNCAIP, potentially influencing PRDM6 activation via enhancer hijacking. Additionally, a 5-lncRNA signature linked to phototransduction was exclusive to Gr3, offering insights into its lineage switch and molecular regulation. CONCLUSIONS: Lnc-SMARCA2 and, MGC32805 and LOC107986446, are exclusively deregulated in SHH and Gr4 MB, respectively, and directly associated with group-specific MB oncogenes, representing promising novel biomarkers and therapeutic targets in MB.

cancer biomarkers↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2026

Transcriptomic analysis identifies novel ferroptosis-related biomarkers and therapeutic targets in pulmonary arterial hypertension.

BACKGROUND: Ferroptosis plays a significant role in pulmonary arterial hypertension (PAH), although its underlying mechanisms and key pathogenic genes remain unclear. METHODS: Transcriptomic data from human PAH and control lung tissue were obtained from the Gene Expression Omnibus (GEO) database, whereas ferroptosis-related genes (FRGs) were sourced from the MsigDb and FerrDb databases. Differentially expressed FRGs (DE-FRGs) were identified through the intersection of FRGs with differentially expressed genes (DEGs). Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Key hub genes were identified through Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and weighted correlation network analysis (WGCNA). Gene set enrichment analysis (GSEA) was conducted to explore the functional roles and associated pathways of hub genes. The relationship between hub genes and immune infiltration was investigated. Expression levels of potential biomarkers were validated via Quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) in two PAH animal models (monocrotaline-induced and Sugen5416 plus hypoxia-induced PAH). Finally, molecular docking was employed to screen potential therapeutic compounds. RESULTS: A total of 133 DE-FRGs were identified, with KEGG and GO analyses highlighting their involvement in intracellular iron homeostasis and ferroptosis. Hub genes, notably FZD7 and NFE2, were identified using LASSO, SVM-RFE, and WGCNA. Immune infiltration analysis suggested that monocytes and neutrophils play key roles in PAH pathogenesis. Validation in PAH animal models showed significant upregulation of Fzd7 and downregulation of Nfe2 in lung tissues of both MCT- and SuHx-induced PAH models. Molecular docking identified tetrachlorodibenzodioxin (TCDD) has good binding affinity. CONCLUSION: In summary, we investigated two ferroptosis-related biomarkers, FZD7 and NFE2, in PAH using transcriptomics, offering new insights into molecular mechanisms and potential targeted therapies for the disease.

Ferroptosis↗

PubMed2025

Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.

BACKGROUND: The remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive. METHODS: Transcriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters. RESULTS: Using NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)+ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)+ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4+ and CFD+ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan-Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. CONCLUSION: We identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4+ and CFD+ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Humans↗

PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

PubMed2025

Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. METHODS: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. RESULTS: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR. CONCLUSION: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Humans↗

PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

PubMed2026

Identification of mitochondrial energy metabolism-related candidate genes UQCR10 and NDUFA6 in pediatric tetralogy of fallot: an exploratory bioinformatics study.

BACKGROUND: Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases in infants and young children. Its molecular basis remains incompletely understood. This study aimed to identify mitochondrial energy metabolism-related candidate genes associated with pediatric TOF using public heart tissue transcriptomic datasets from the GEO database. METHODS: Datasets GSE146218 and GSE217772 were downloaded and merged, followed by batch-effect correction. Differential expression analysis was performed to identify differentially expressed genes (DEGs). Functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were used to prioritize candidate genes. The Comparative Toxicogenomics Database (CTD) was used as an exploratory literature-based tool to summarize gene-disease associations. RESULTS: A total of 960 DEGs were identified. Functional enrichment analyses showed that these genes were mainly enriched in mitochondrial energy metabolism-related pathways, including oxidative phosphorylation and the mitochondrial respiratory chain. WGCNA and PPI network analyses further prioritized UQCR10 and NDUFA6 as candidate genes, and both genes showed increased expression in TOF heart tissue samples. CTD analysis suggested literature-based associations between these genes and cardiovascular or developmental disease-related terms. CONCLUSION: This exploratory bioinformatics study identified UQCR10 and NDUFA6 as mitochondrial energy metabolism-related candidate genes upregulated in pediatric TOF heart tissue. These findings suggest that mitochondrial respiratory chain-related transcriptional alterations may be involved in TOF-associated myocardial remodeling or stress responses. Further experimental and clinical validation is required to confirm their biological relevance.

Humans↗

PubMed2026

Decoding age-stratified clinical and molecular heterogeneity in male breast cancer through multiomic profiling.

OBJECTIVE: Age-associated molecular heterogeneity is well described in female breast cancer but remains insufficiently characterized in male breast cancer (MBC). We profiled age-stratified clinical and molecular differences between younger (≤55 years) male breast cancer (YMBC) and older (>55 years) male breast cancer (OMBC). METHODS: We retrospectively analyzed 347 patients with MBC diagnosed at Fudan University Shanghai Cancer Center by integrating clinicopathological data, RNA sequencing, and whole-exome sequencing (WES). Survival, differential expression, and mutational signature analyses were performed. Tumor microenvironment features were inferred using xCell and ESTIMATE, and weighted gene co-expression network analysis (WGCNA) was conducted to identify age-associated co-expression modules. Candidate therapeutics were prioritized using the Genomics of Drug Sensitivity in Cancer (GDSC) resource and evaluated using patient-derived organoids (PDOs). RESULTS: Compared with OMBC, YMBC more frequently had human epidermal growth factor receptor 2 (HER2)-positive status (14.91% vs. 4.02%) and triple-negative tumors (4.92% vs. 1.78%), and had worse 5-year recurrence-free survival (hazard ratio=2.19, P=0.018). Transcriptomic analyses indicated enrichment of neural-related programs and reduced immune-related signaling in YMBC, and xCell/ESTIMATE supported lower immune infiltration. Consistently, WGCNA identified age-associated modules linking neural-related programs with reduced immune infiltration. Immunohistochemistry supported increased perineural invasion and lower CD8+ T cell infiltration in YMBC. GDSC-guided prioritization with PDO testing nominated sepantronium bromide (YM155) as a candidate vulnerability in YMBC. WES showed a higher NBPF10 mutation frequency in YMBC (54.5% vs. 14.3%, P<0.05). CONCLUSIONS: Integrated multi-omics profiling revealed age-stratified clinical and molecular heterogeneity in MBC. YMBC patients demonstrated inferior recurrence-free survival, neural signaling enrichment, an immune-cold microenvironment, and enriched NBPF10 mutations. These findings support age as a meaningful stratification variable in MBC risk assessment and treatment planning, and highlight the need for caution when considering treatment de-escalation in younger patients, while nominating YM155 as a candidate agent for prospective evaluation.

Male breast cancer↗

PubMed2026

Integrated Metabolomic and Transcriptomic Analysis Reveals Tissue-Specific Secondary Metabolic Differentiation and Indole Alkaloid Accumulation in Evodia rutaecarpa.

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R2Y > 0.9, Q2 > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production.

Evodia rutaecarpa↗

PubMed2026

New insights into diagnostic values and mechanisms of ferroptosis associated with immune infiltration in diabetic kidney disease.

The pathogenesis of diabetic kidney disease (DKD) is complex and closely related to ferroptosis and immune dysregulation, but the relevance is unclear. The present study investigates the potential mechanisms of ferroptosis-related genes (FRGs) in DKD and their relationship with the immune-inflammatory response. It searches for new diagnostic biomarkers to help diagnose and treat DKD. Four Gene Expression Omnibus (GEO) datasets, GSE30528, GSE30529 and GSE30122 as the test set, and GSE96804 for validation, were analyzed. FRGs were obtained from GeneCards, and 47 ferroptosis-related differentially expressed genes (FRDEGs) were identified by intersecting with DKD-related differentially expressed genes. Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis and Gene Set Variation Analysis, revealed that these FRDEGs are primarily associated with ferroptosis, hypoxia response and immune inflammation. Subsequently, the weighted gene co-expression network analysis (WGCNA) was employed to expand the ferroptosis-related gene network, and intersection of the 47 FRDEGs with key WGCNA module genes yielded 10 key genes. Based on the 10 key genes, the least absolute shrinkage and selection operator and support vector machine algorithms identified three hub genes [chemokine ligand 5 (CCL5), forkhead box C1 (FOXC1) and lactotransferrin (LTF)] for DKD diagnosis. Receiver operating characteristic curves confirmed their diagnostic value, with FOXC1 and LTF validated in the independent dataset. Immune infiltration analysis via CIBERSORT revealed eight immune cell types with significantly different infiltration levels between the DKD and control group in the integrated GEO datasets. Notably, both LTF and CCL5 showed a significant positive correlation with gamma delta T cells (γδT). Quantitative PCR results confirmed differential expression of the three hub genes in the DKD group, with elevated expression observed in DKD mice following intervention with rosiglitazone and hyperoside.

bioinformatics analysis↗

PubMed2026

Distinct periarticular muscle transcriptomes: inflammation in rheumatoid arthritis versus metabolic dysregulation in osteoarthritis.

OBJECTIVES: Periarticular skeletal muscle abnormalities are recognised in rheumatoid arthritis (RA) and osteoarthritis (OA), but their divergent molecular pathologies are poorly defined. This study aimed to elucidate and directly compare the transcriptomic profiles of periarticular muscle in patients with RA and OA. METHODS: We performed bulk RNA sequencing of periarticular skeletal muscle samples collected during total joint arthroplasty from RA (n=6) and OA (n=4) patients. Differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), pathway enrichment, and gene set variation analyses were conducted to identify disease-specific molecular features and their clinical associations. RESULTS: The two conditions showed fundamentally distinct profiles. RA muscle exhibited a pronounced inflammatory signature, characterised by upregulation of cytokine-responsive genes including FOS, EGR1, and CXCL2, and enrichment of tumour necrosis factor-α and interleukin-6 (IL-6)/JAK-STAT3 signalling. In contrast, OA muscle was characterised by metabolic dysregulation, with upregulation of genes linked to adipogenesis (PCK1, SFRP4) and significant enrichment of epithelial-to-mesenchymal transition (EMT) signalling. These divergent profiles were further supported by WGCNA, which identified distinct modules reflecting heightened innate immune and complement activation in RA, and disrupted metabolic processes in OA. Notably, in RA, the IL-2-STAT5 signalling pathway was unique among those tested in showing a strong positive correlation with DAS28-ESR (r=0.94, p=0.019). CONCLUSIONS: This study reveals distinct molecular pathologies in the periarticular muscle of RA and OA. RA muscle shows an intense inflammatory profile potentially linked to cachexia, whereas OA muscle displays features of metabolic disease and pro-fibrotic remodelling.

Humans↗

PubMed2026

Identifying Co-Expressed lncRNAs Correlated With Traits of Interest in an Animal Model for Metabolic Diseases in Humans.

Nutrigenomics investigates how nutrients modulate gene expression. Among them, fatty acids (FA) play important roles in regulating gene transcription, while long non-coding RNAs (lncRNAs) may be associated with gene regulation and metabolic diseases. This study aimed to analyze the hepatic transcriptome of pigs, a species frequently used as a model for nutrigenomic studies, to identify novel lncRNAs and their potential target genes in response to diets containing different sources of FA. Seventy-two pigs were fed four diets supplemented with 1.5% soybean oil (control), 3% canola oil, 3% fish oil, and 3% soybean oil. RNA sequencing of liver samples was performed to identify novel lncRNAs. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify modules associated with phenotypic traits related to lipid metabolism and inflammation. Functional enrichment analyses were then conducted to annotate genes within these modules using Gene Ontology (GO) terms and to assess overlap with Quantitative Trait Loci (QTL). The results revealed 106 novel lncRNAs potentially regulating genes associated with lipid metabolism and immune responses in pigs fed diets with different FA sources. These findings enhance understanding of the regulatory role of lncRNAs in pigs and reinforce their relevance as models for human metabolic diseases.

Animals↗

PubMed2026

Integrative transcriptomic, spatial and functional-genomic analysis identifies a UFMylation-related vascular-stromal program and prioritizes WWTR1 in glioblastoma.

Glioblastoma (GBM) contains spatially organized stress-adaptive and vascular niches. Because transcript abundance does not measure UFM1 conjugation, we asked whether a UFMylation-related transcriptional axis identifies a reproducible tissue program and alters candidate prioritization. In 518 unique primary TCGA-GBM tumors profiled on the Affymetrix HT Human Genome U133A array, weighted gene co-expression network analysis of 8,000 variable genes yielded 12 modules. The 278-gene green module ranked first across nine prespecified traits (mean |r|=0.637). Direct overlap comprised 1/3 measurable UFMylation-core, 5/19 ER-stress/UPR, and 2/15 proteostasis genes; after excluding overlapping genes, correlations with the green eigengene remained significant (r = 0.373, 0.831, 0.639, and 0.699 for UFMylation-core, ER-stress/UPR, proteostasis, and composite scores, respectively). The green score was associated with overall survival per standard-deviation increase (HR 1.17, 95% CI 1.07-1.28), although clinical adjustment attenuated the estimate. In a 10-sample single-cell dataset, sample-level scores were higher in pericytes and endothelial cells than in malignant cells. Donor-aware IvyGAP analysis supported regional organization, whereas one Visium section showed stronger concordance with ER-stress/UPR and mesenchymal scores than with the UFMylation-core score. CellChat indicated pathway-selective rather than global remodeling of inferred vascular communication. Layer ablation moved WWTR1 from rank 48 using WGCNA alone to rank 4 overall and rank 1 among non-common-essential genes after cross-platform integration. These findings define an ER-stress/mesenchymal-weighted, UFMylation-related vascular-stromal transcriptional association and nominate WWTR1 for experimental testing.

Humans↗

PubMed2026

Integrative analysis and experiment validation of SLC12A8 as a biomarker for the malignant transition from endometriosis to endometriosis associated ovarian cancer.

Endometriosis (EM) is a chronic inflammatory, estrogen‑dependent benign gynecological disorder. A subset of patients with EM may subsequently develop endometriosis‑associated ovarian cancer (EAOC), implying a biological continuum between these two conditions. Nevertheless, the molecular events underlying the progression from benign endometriotic lesions toward EAOC remain incompletely characterized. In this study, transcriptomic datasets retrieved from the GEO database were interrogated through differentially expressed gene screening, functional enrichment analysis, and weighted gene co‑expression network analysis (WGCNA) to identify key genes and pathways relevant to EM and EAOC. Candidate genes were further prioritized by integrating survival analysis via the Kaplan‑Meier Plotter, LASSO regression, random‑forest modeling, and CIBERSORT immune‑infiltration profiling. Loss and gain‑of‑function cellular models were established using siRNA and overexpression plasmids, and in‑vitro functional assays were performed to characterize the phenotypic effects of target genes.We identified several candidate genes associated with EM and EAOC and evaluated their discriminatory performance. Among them, SLC12A8 elevated expression across EM and EAOC tissues and exhibited moderate diagnostic capacity. Higher SLC12A8 expression was also associated with poorer prognosis in EAOC patients. In‑vitro experiments further demonstrated that SLC12A8 modulates proliferation, invasion, and migration in both EM and EAOC cell lines. Collectively, our exploratory research findings support SLC12A8 as a candidate functional mediator and potential biomarker linked to EM‑EAOC pathological progression, thereby extending the mechanistic understanding of these disorders.

Female↗

PubMed2026

Integrated analysis uncovers exogenous induction and molecular regulation of erinacine A accumulation in Hericium erinaceus.

Erinacine A, a cyathane-type diterpenoid mainly from Hericium erinaceus mycelia, exhibits prominent neurotrophic and neuroprotective activities, making it a promising candidate for managing neurodegenerative diseases. However, its low abundance and unclear genetic regulatory mechanisms hinder its application as a nutraceutical. This study aimed to decipher its regulatory mechanisms and enhance production. Four exogenous inducers were screened, with salicylic acid (SA) and ergosterol (ERG) significantly increasing erinacine A content by 62.21% and 146.70% at 20 days, respectively. Transcriptome and WGCNA of inducer-treated sample identified darkorange and magenta modules associated with erinacine A biosynthesis, with the eri gene cluster enriched in the darkorange module and eriG and eriF as hub genes. Forward genetic analysis via QTL mapping of the HeD127 dikaryon population revealed significant phenotypic variation in erinacine A content (0.341-13.085 mg/g) and identified two loci (erA-1 and erA-2) explaining 18.63% of phenotypic variation. Integrating these forward and reverse genetic analyses revealed that salicylic acid and ergosterol synergistically regulate core carbon metabolic pathways to augment acetyl-CoA supply for the mevalonate pathway, suppressed competitive metabolism, enhanced diterpene skeleton construction and structural modification. These results deepen our understanding of the genetic and molecular basis governing accumulation of erinacine A, and facilitate its application in neuroprotective pharmaceuticals.

Diterpenes↗