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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

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗

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

Pan-cancer single-cell atlas of immunotherapy response identifies ZNF385A as a regulator of immune evasion in small cell lung cancer.

Although immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape of solid tumors, response rates in patients with small cell lung cancer (SCLC) remain limited, and acquired resistance is highly prevalent. The underlying mechanisms of this immunotherapy resistance remain to be fully elucidated. Clinically, SCLC typically manifests as an "immune-cold" tumor, characterized by a low abundance of CD8+ T cell infiltration and the rare formation of tertiary lymphoid structures (TLS). While DNA damage repair (DDR) is closely linked to innate immune responses, how DDR networks orchestrate the SCLC immune microenvironment remains obscure. In this study, we integrated single-cell transcriptomic data (comprising 344,447 high-quality cells) from six cancer types (BCC, CRC, HCC, HNSCC, iCCA, and SCLC). Our comparative analysis revealed a fundamental depletion of TLS-associated cellular subpopulations (e.g., CXCL13+ CD8+ T cells, HLA-DRB5+ B cells, and CXCL9+ dendritic cells) in SCLC, which was significantly correlated with aberrant DDR activity. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), we identified ZNF385A as the core hub gene within the DDR-associated module. ZNF385A is highly expressed in SCLC and is associated with poorer prognosis. In vitro, ZNF385A depletion suppressed SCLC cell proliferation and induced apoptosis, accompanied by R-loop accumulation and activation of cGAS-STING signaling, indicating a potential link between ZNF385A, genomic stability and tumor-intrinsic innate immune signaling. Collectively, these findings identify ZNF385A as a potential regulator associated with TLS deficiency and immune evasion in SCLC.

Immunotherapy resistance↗

PubMed2026

Integrated Multi-Omics Analysis Reveals the Genetic Basis of Phenotypic Variation in Tibetan Sheep.

Body size is a key economic trait influencing the profitability of farmed animals. This study used genome-wide association studies (GWAS) to identify five single nucleotide polymorphisms (SNPs) significantly associated with body size in the Tibetan sheep population, advancing molecular breeding and providing a basis for genomic selection. These SNPs are located within five candidate genes. SNaPshot validated GWAS results, demonstrating significant correlations between candidate SNPs and body size traits in Tibetan sheep. Concurrently, hematoxylin and eosin staining, alongside muscle fiber analysis, confirmed pronounced morphological differences in muscle tissue between sheep of varying conformation. Therefore, transcriptome and proteomics were performed on the longest dorsi muscle from large and small Tibetan sheep of both sexes. The transcriptome, together with weighted gene co-expression network analysis (WGCNA), identified VEPH1 and PRKG1 as core genes regulating body characteristics in Tibetan sheep through their involvement in the PI3K-Akt signaling pathway and pathways related to fat deposition. The integrative analyses demonstrated significantly different expression of CARNS1 and CRYAB at both transcriptional and protein levels between the muscles of large- and small-sized Tibetan sheep of both sexes, suggesting their importance in body size traits by influencing muscle morphology. This study provides valuable genomic resources that advance sheep genetics research.

GWAS↗

PubMed2026

Mechanistic analysis of rice caryopsis morphogenesis regulated by exogenous hormones and related precursor substances under blue light conditions.

Rice caryopsis morphogenesis is regulated by light signals and hormonal networks. However, the mechanism by which exogenous hormones and related precursor substances modulate rice caryopsis morphogenesis under blue light remains elusive. In the present study, we aimed to elucidate the molecular mechanisms underlying the regulatory effects of exogenous phytohormones and related precursor substances on caryopsis development at 10 days after pollination (10 DAP) in the japonica rice cultivar 'Chujing 27' under blue light conditions. Results showed that tryptamine treatment increased caryopsis cell volume, thereby significantly driving caryopsis expansion; meanwhile, it markedly enhanced the activities of TDC and TAA, the key rate-limiting enzymes mediating the conversion of tryptophan to auxin, leading to a significant elevation in endogenous auxin content (P < 0.05). In comparison, exogenous auxin treatment significantly boosted carbohydrate accumulation and the activities of associated metabolic enzymes (P < 0.05). Integrated transcriptomic and metabolomic analyses revealed that tryptamine treatment led to significant enrichment of the starch and sucrose metabolic pathway, and drove the coordinated enhancement of carbon metabolic flux and auxin biosynthesis by upregulating key auxin biosynthetic genes (e.g., TAA1) and repressing auxin oxidative degradation. Genes Os04g0531100, Os03g0266100 and Os11g0221200 identified by weighted gene co-expression network analysis (WGCNA) may serve as important candidate targets regulating rice caryopsis morphology and physiological traits under blue light conditions. This study first uncovers the critical function of the "tryptamine-auxin axis" in regulating rice caryopsis development under blue light, laying a theoretical foundation for regulating caryopsis morphogenesis via exogenous hormones and their precursors.

Oryza↗

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

ACE2 and Parkinsonism‑related bone metabolic alterations: signaling pathways and hub gene analysis.

Clinical co-occurrence of Parkinson's disease (PD) and age-related bone loss in elderly patients has garnered increasing attention, yet its molecular mechanisms remain incompletely elucidated. This study used an 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model in Ace2-/y mice to investigate the regulatory mechanisms of bone-brain axis-related genes and signaling pathways. Behavioral tests assessed motor and non-motor symptoms. Immunohistochemistry, Western blot, and histopathological staining analyzed dopaminergic neuron activity, microglial activation, and bone metabolic abnormalities. GEO dataset transcriptomics and weighted gene co-expression network analysis (WGCNA) identified key hub genes, with receiver operating characteristic (ROC) curves evaluating their diagnostic value in public single-disease transcriptome data. MPTP significantly exacerbated motor dysfunction and depression-like behaviors; Ace2 deletion lowered total Wnt, β-catenin, BMP and IGF-1 protein abundance alongside reduced phosphorylation ratios of their downstream kinases in brain and bone, while upregulating RANKL/RANK/OPG-associated inflammatory mediators, accompanied by elevated total α-synuclein, Casp3 and Bax protein levels. The parallel reduction of these signaling proteins only suggests potential perturbation of related cascades; WGCNA identified 10 hub genes (e.g., DNM1, OCRL, OPA1), whose dysregulation was linked to synaptic dysfunction and inflammation. ROC analysis based on single-disease datasets showed high diagnostic accuracy for PD and `osteoporosis (OP) (AUC: 0.683-0.981), with core genes influencing synaptic, MAPK, Rap1, and Ras pathways. These preclinical findings indicate that Ace2 deficiency is associated with concurrent pathological abnormalities in the brain and transient bone metabolic disturbance under short-term MPTP treatment in growing young male mice; coordinated dysregulation of shared signaling pathways was observed in the two tissues, consistent with a potential bone-brain axis pathological phenotype, though causal bidirectional tissue cross-talk cannot be confirmed in the current experimental design, providing candidate targets that warrant further validation.

Animals↗

PubMed2026

Uncovering molecular regulatory networks of low-temperature stress response in Trachinotus ovatus via integrated transcriptome and metabolome analyses.

Golden pompano (Trachinotus ovatus) is one of the most economically important marine fish species in China. It is susceptible to low-temperature stress, which significantly challenges its production and supply. Nevertheless, study on the regulatory mechanisms underlying low-temperature stress responses in golden pompano remains limited. Here, we firstly performed a time-series transcriptome analysis to reconstruct dynamic response patterns under low-temperature stress in golden pompano. Transcriptome profiling identified common differentially expressed genes (DEGs), including fos, hlf, and hmgb1, as well as condition-specific DEGs across distinct low-temperature stress groups. Based on cluster analysis, all DEGs were classified into five distinct expression patterns, reflecting diversified regulation of expression in golden pompano during low-temperature stress. Furthermore, condition-specific regulatory modules were explored via weighted gene co-expression network analysis (WGCNA), highlighting that the two module hub genes, serbf2 and lipc, might respond to low-temperature stress by regulating the lipid catabolic process. Subsequently, untargeted metabolomic analysis revealed that glycerophospholipid metabolism was a significantly enriched common pathway, highlighting its crucial role in mediating the response to low-temperature stress. Finally, by integrating transcriptomic and metabolomic analyses, a gene-metabolite interaction network associated with glycerophospholipid metabolism under low-temperature stress was established. These findings underscore the significance of multiple candidate genes and glycerophospholipid metabolism in golden pompano's response to low-temperature stress, thereby laying a solid molecular foundation for the development of low-temperature-tolerant fish strains.

Animals↗

PubMed2026

Transcriptomics reveals species-specific adaptive strategies to calorie restriction in two Argopecten scallops with distinct lifespans.

Calorie restriction (CR) is a well-established non-genetic intervention for lifespan extension in multiple model organisms. Seasonal food shortage in cold and temperate seas may mimic CR, inducing in bivalves a response similar to that in vertebrates and thereby prolonging life expectancy. However, the relationship and the mechanism underlying the food availability and lifespan in bivalves remain largely unexplored. Two closely related scallop species the short-lived warm-water Argopecten irradians (lifespan <2 years) and the longer-lived cold-water Argopecten purpuratus (7-10 years) provide an ideal comparative system to investigate species-specific adaptive strategies. In this study, we subjected both species to CR for 30 and 56 days and performed comparative transcriptomic profiling, weighted gene co-expression network analysis (WGCNA), and physiological assays to elucidate their distinct molecular responses. Transcriptomic analysis revealed that A. purpuratus exhibited substantially more DEGs than A. irradians at both time points under CR, with both species showing downregulation of metabolic pathways but to different extents. A. irradians mounted an early nutrient-sensing response at 30 days (IGF1R, PIK3R3, INSR suppression), indicating acute sensitivity to limitation; by contrast, A. purpuratus displayed delayed FoxO activation at 56 days, along with its downstream effectors NFKBIA, CREB3L4, and SMAD4, suggesting a gradual adaptive program may link to its extended lifespan. WGCNA identified three negatively correlated modules in each species, with coral2 being the most prominent in A. irradians and darkolivegreen in A. purpuratus. The former was dominated by ciliary motility genes, whereas the latter featured coordinated repression of oxidative phosphorylation. Additionally, both species exhibited conserved suppression of mTOR/S6K growth signaling and activation of cellular maintenance programs. Collectively, these findings expand the understanding of CR-mediated longevity regulation in bivalves and provide candidate gene resources for future functional studies and breeding programs.

Pectinidae↗

PubMed2026

Multi-omics analysis reveals coordinated epigenetic dysregulation in atrazine-induced dopaminergic neurotoxicity.

Atrazine (ATR), a widely used triazine herbicide, has been linked to neurotoxicity, yet the epigenetic mechanisms underlying its dopaminergic effects remain unclear. This study investigated whether coordinated miRNA dysregulation and DNA methylation alterations contribute to ATR-induced Parkinson's disease (PD)-like neurotoxicity. Male Sprague-Dawley rats were administered ATR (50 mg/kg/day) for 90 days, resulting in motor and cognitive deficits with dopaminergic dysfunction, including increased α-synuclein and reduced tyrosine hydroxylase expression. Small RNA sequencing identified 72 differentially expressed miRNAs in the substantia nigra, enriched in PI3K-Akt, MAPK, and Ras signaling pathways. In a cohort of six PD patients and six matched controls, genome-wide DNA methylation profiling revealed 4694 differentially methylated positions, predominantly hypomethylated, with overlapping enrichment in neuronal signaling pathways. Weighted gene co-expression network analysis identified a PD-associated module strongly correlated with disease status (r = -0.95, P < 0.001). Multi-omics integration identified CASP3 as a central hub gene. External validation supported CASP3 relevance in PD (AUC = 0.833), and molecular docking suggested potential ATR-CASP3 interaction. Further analysis predicted upregulated miR-3552 as a potential upstream regulator of CASP3. These findings indicate that ATR-induced neurotoxicity may be mediated through the miR-3552/CASP3 signaling axis, ultimately regulating apoptosis and contributing to neurodegeneration.

Animals↗

PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans↗

PubMed2026

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗

PubMed2026

Comparative transcriptomic and physiological analyses uncover key regulatory pathways associated with drought tolerance in wheat.

Drought severely limits wheat yield, yet its molecular basis remains incompletely understood. We compared a drought-tolerant line (A25) and a drought-sensitive line (A8) under water deficit across three developmental stages using physiological assays and transcriptomics. A25 exhibited stronger osmotic adjustment and antioxidant defense, with higher proline accumulation and enhanced activities of ascorbate peroxidase, catalase, and other ROS-scavenging enzymes. RNA-seq revealed distinct drought-responsive expression patterns, with differentially expressed genes enriched in MAPK signaling and ABA-dependent pathways. ABA-responsive genes were more abundant and strongly induced in A25, suggesting enhanced ABA signal transduction as a key mechanism. Weighted gene co-expression network analysis identified a drought-associated purple module positively correlated with physiological resistance, from which six hub genes (MAPKKK17, Avr9/Cf-9, RPPL1, RGA1, UBC28, AGPs5) were highlighted as potential regulators. Collectively, coordinated activation of ABA signaling and MAPK cascades, mediated by these hub genes, underlies the robust drought tolerance of A25, providing promising molecular targets for wheat breeding and improvement.

Triticum↗

PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗

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

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of β = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-κB, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 ± 0.83 kcal·mol-¹) and HCAR3 with 3-Indolepropionic Acid (-6.35 ± 0.70 kcal·mol-¹), both below -5.0 kcal·mol-¹. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗