Search Results (2,215)

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic multifactorial metabolic disorder requiring multi-target therapeutic strategies. This study aimed to predict the potential material basis, key targets and molecular mechanisms by which PuRenDan (PRD) may act against T2DM through an integrated network pharmacology and molecular simulation approach. Methods: Active compounds of PRD were screened from TCMSP, HERB 2.0 and the literature, and compound-related targets were predicted using TCMSP, SwissTargetPrediction and PharmMapper. T2DM-associated targets were collected from OMIM, DrugBank, DisGeNET, HPO, ClinPGx and GeneCards to obtain drug–disease intersection targets. Cytoscape was used to construct herb–compound–target and protein–protein interaction (PPI) networks, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was performed using AutoDock Vina1.1.2, and representative ligand–receptor complexes were further assessed by 100 ns molecular dynamics (MD) simulations and molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) binding free-energy analysis. Results: A total of 163 active compounds, 597 PRD-related targets, 9138 T2DM-associated targets and 483 intersection targets were identified. β-sitosterol, emodin, quercetin, kaempferol and formononetin were predicted as major active compounds, whereas AKT1, TP53, SRC, IL6, TNF, EGFR and ESR1 were identified as disease-related network hubs. KEGG enrichment highlighted the PI3K-Akt, MAPK, HIF-1, FoxO, mTOR, AGE-RAGE and TNF signalling pathways. Docking predicted a comparatively favourable multi-target binding tendency for β-sitosterol. MD and MM/PBSA analyses further suggested favourable dynamic stability for β-sitosterol-TNF, β-sitosterol-AKT1, β-sitosterol-SRC and emodin-EGFR complexes, with β-sitosterol-TNF showing the lowest predicted binding free energy among the simulated systems. Conclusions: These in silico findings suggest that PRD may regulate T2DM-related inflammatory, insulin-signalling, oxidative-stress and metabolic networks through coordinated multi-compound, multi-target and multi-pathway actions. β-sitosterol may represent an important candidate material basis of PRD, with TNF, AKT1, SRC and EGFR as potential key targets. These conclusions remain predictive and require validation in biochemical, cellular and animal experiments. Full article

Objective: Impaired fatty acid oxidation (FAO) is considered an important metabolic mechanism underlying skeletal muscle aging and sarcopenia; however, the key regulatory molecules involved in this process remain incompletely defined. This study aimed to identify candidate biomarkers associated with impaired FAO in aged skeletal muscle, characterize their potential biological functions and regulatory features through integrated bioinformatics and machine learning analyses, and preliminarily validate their expression patterns in in vivo and in vitro aging models. Methods: Skeletal muscle aging transcriptomic datasets GSE1428 and GSE674 were obtained from the Gene Expression Omnibus database. FAO-related genes were retrieved from GeneCards. Differentially expressed FAO-related genes (DE-FAOGs) were identified through differential expression analysis and were further analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Random forest, Boruta, and protein–protein interaction (PPI) network analyses were used to screen hub genes, and an artificial neural network (ANN) model was constructed. Single-cell RNA sequencing analysis, gene set enrichment analysis, ceRNA network construction, drug prediction, molecular docking, and molecular dynamics simulation were further performed. Hub gene expression was validated by qRT-PCR in naturally aged mice and D-galactose-induced senescent C2C12 cells. Results: A total of 69 DE-FAOGs were identified and were mainly enriched in mitochondrial function, electron transport chain, and energy metabolism-related pathways. Three hub genes, creatine kinase, mitochondrial 2 (CKMT2), actin alpha cardiac muscle 1 (ACTC1), and forkhead box O3 (FOXO3), were identified by random forest, Boruta, and PPI analyses. Receiver operating characteristic (ROC) analysis showed good discriminatory performance for these genes. The three-gene ANN model achieved area under the curve (AUC) values of 0.992 and 0.964 in the training and validation datasets, respectively. Gene set enrichment analysis (GSEA) suggested that the hub genes were closely associated with mitochondrial energy metabolism, lipid metabolism, and stress regulation. qRT-PCR confirmed decreased Ckmt2 expression and increased Actc1 and Foxo3 expression under aging conditions, consistent with the bioinformatics results. Conclusions: CKMT2, ACTC1, and FOXO3 are potential biomarkers associated with impaired FAO in aged skeletal muscle. The ANN model based on these three genes showed good predictive performance and may provide new insights into the metabolic mechanisms and therapeutic targets of sarcopenia. Full article

Helicoverpa armigera is one of the most destructive polyphagous pests, yet the transcriptional organization underlying its digestive capacity remains poorly resolved. Here, we compiled 579 publicly available RNA-seq libraries representing 54 independent experiments and quantified transcript abundance across tissues and developmental stages. This complete dataset was used to support broader tissue-level expression profiling. After metadata harmonization and quality filtering, a subset of 130 biologically comparable libraries from five tissue/developmental categories was retained for weighted gene co-expression network analysis. WGCNA identified four biologically informative modules, among which the turquoise module was positively associated with fourth- and fifth-instar larval midgut samples. Independent expression profiling revealed strong midgut-biased expression of several trypsin- and chymotrypsin-like serine proteases, although only a subset of these genes was assigned to the turquoise module. Descriptive functional annotation of this module identified 202 co-expressed loci, including digestive enzymes, nutrient transporters, detoxification-related proteins, epithelial components and putative transcriptional or signaling-associated genes. Phylogenetic analyses and manual inspection of genomic locations further showed that several digestive protease genes occur in local clusters and have closely related counterparts in H. zea, suggesting partial conservation of local genomic organization. Collectively, these results describe a midgut-associated co-expression module containing genes associated with digestive, absorptive and protective functions and provide candidate genes for future functional studies. Full article

Background/Objectives: N6-methyladenosine (m⁶A) is a prevalent RNA modification that significantly influences various biological processes. AlkB homologs (ALKBHs) belong to the family of specific demethylases and, by regulating m⁶A methylation, are known to be involved in the modulation of plant stress responses. However, the ALKBH gene family has not been systematically characterized in ginseng. Methods: A genome-wide identification and characterization of the ALKBH gene family in ginseng were performed using a telomere-to-telomere reference genome. Phylogenetic relationships, gene structures, conserved motifs, 3D structures, chromosomal distribution, syntenic relationships, cis-acting regulatory elements, protein-protein interaction (PPI) networks, and expression profiles were analyzed. Transcriptome datasets covering multiple tissues, developmental stages, cultivars, and abiotic stress treatments were examined. Candidate stress-responsive genes were further validated by qRT-PCR. Results: A total of 17 PgALKBH genes were identified and classified into seven subfamilies. Structural analyses revealed conserved motifs, exon–intron organization, and 3D structures among members within the same subfamily. Chromosomal localization and synteny analyses suggested that the PgALKBH family has been evolutionarily conserved between ginseng and Arabidopsis and has primarily undergone purifying selection during its expansion. Promoter analysis identified abundant light-, hormone-, and stress-responsive cis-elements. Expression profiling revealed distinct tissue- and developmental stage-specific patterns. The PPI analysis suggested that PgALKBH proteins, especially PgALKBH10, may play a central role in m⁶A-mediated RNA regulation in ginseng. Transcriptome and qRT-PCR analyses further showed that PgALKBH genes respond differentially to drought, cold, and salt stresses. Notably, PgALKBH10 was induced under all three stress conditions. Conclusions: This study provides a comprehensive characterization of the ALKBH gene family in ginseng and identifies PgALKBH10 as a promising candidate involved in multiple abiotic stress responses. These findings establish a foundation for elucidating the roles of RNA m⁶A demethylation in ginseng and provide valuable genetic resources for developing stress-tolerant ginseng cultivars. Full article

Background/Objectives: Rice (Oryza sativa) is the staple food for over half the global population, and nitrogen availability is the primary limiting factor determining rice yield. As the rate-limiting enzyme in nitrogen assimilation and allocation, glutamate synthase (GOGAT) plays an irreplaceable role throughout the plant life cycle. The evolutionary history, natural genetic variation, and regulatory networks of the GOGAT family in rice source–sink tissues during grain filling remain largely elusive. Methods: Here, we combined comparative genomics, population genetics, transcriptomic and biochemical approaches to systematically characterize the GOGAT gene family. Genome-wide identification was performed across 12 angiosperm species, followed by haplotype analysis using resequencing data from ~2000 rice accessions. Transcriptomic, enzymatic activity and metabolite content determination were integrated to investigate their responses to three nitrogen gradient treatments in source (roots, flag leaves) and sink (developing embryos) tissues. Results: A total of 48 GOGAT genes were identified, clustered into two ancient subfamilies (GLU/GLT), with a Poaceae-specific duplication event generating GLT1 and GLT2 subgroups. Specifically, three rice GOGAT genes exhibited distinct domestication signatures: Fd-GOGAT showed strong indica-japonica subspecific differentiation, while NADH-GOGAT2 harbored tropical japonica-specific haplotypes. Furthermore, tissue-specific and developmental stage-dependent nitrogen response patterns were revealed, identifying 5 days after pollination as the critical metabolic switch point. OsGOGAT promoters are enriched with light-, ABA- and stress-responsive cis-elements, suggesting coordinated hormonal and environmental regulation. Conclusions: This study provides comprehensive insights into the functional divergence of the plant GOGAT gene family and coordinated strategies that rice employs under exogenous nitrogen stress, and identifies elite haplotypes for nitrogen-efficient rice breeding. Full article

2,4-Dichlorophenoxyacetic acid (2,4-D), originally developed as a synthetic auxinic herbicide, is the most widely used chemical inducer of somatic embryogenesis (SE) in plants. Despite extensive use of 2,4-D in plant regeneration, the systems-level regulatory mechanisms connecting hormonal signaling, metabolic reprogramming, translational control, and embryogenic competence remain poorly resolved. Here, we hypothesize that TOR signaling functions as an integrative molecular hub coordinating transcriptional, metabolic, and developmental reprogramming during somatic embryogenesis induction. To investigate the molecular regulatory landscape associated with 2,4-D-induced SE, we performed a systems-level analysis integrating publicly available transcriptomic data from Arabidopsis thaliana with high-confidence protein–protein interaction (PPI) network analyses using STRING v12.0 (confidence score ≥ 0.900). Using a previously published transcriptomic dataset, we identified 1927 upregulated genes associated with SE induction, which were organized into 34 functional modules related to transcriptional regulation, translation metabolism, hormone signaling and cellular homeostasis. Within this interactome, TARGET OF RAPAMYCIN (TOR) kinase emerged as an integrative regulatory hub associated with multiple pathways involved in embryogenic reprogramming. Network analyses revealed three major TOR-associated regulatory axes: (1) the TOR–FKBP12–RPS6A axis, associated with ribosome biogenesis and translational regulation; (2) the TOR–CBP20 axis, connected with transcriptional reprogramming; SE master regulators (LEC1, LEC2, and FUS3); and lipid, sterol, brassinosteroid (BR), and auxin-associated pathways; and (3) the TOR–TAP46 axis, linked with one-carbon metabolism, nucleotide biosynthesis, DNA replication and repair, and genome-stability pathways. Additionally, the network contained 411 embryo-lethal (EMBL) genes distributed across multiple regulatory modules, reinforcing the biological relevance of the identified interactome and highlighting the importance of coordinated developmental, metabolic, and transcriptional regulation during embryogenesis induction. These findings support a systems-level TOR-associated regulatory framework involved in the integration of transcriptional, translational, metabolic, hormonal, and genome-maintenance pathways during embryogenesis. This interactome model provides a foundation for functional studies aimed at dissecting the molecular mechanisms underlying SE and identifying candidate targets to improve regeneration and biotechnological application and crop genetic engineering. Collectively, this study proposes a mechanistic framework in which TOR signaling integrates developmental, metabolic, translational, and genome-stability pathways to orchestrate embryogenic competence, providing candidate molecular targets for improving plant regeneration and genome engineering platforms. Full article

Background: Gastric cancer remains a leading cause of cancer-related deaths worldwide. Although significant progress has been made in clinical diagnosis and treatment, the molecular mechanisms underlying gastric cancer have not yet been fully elucidated. To address this, this study employs a multi-omics approach to systematically analyze the molecular characteristics of gastric cancer. Methods: This case–control study enrolled 218 GC patients and 218 healthy controls, and adopted a multi-omics strategy combining inductively coupled plasma mass spectrometry (ICP-MS), element-related genome-wide association study (eGWAS), and untargeted metabolomics to explore the element-gene-metabolite regulatory axis in GC. Results: A total of nine plasma differential elements associated with gastric cancer were identified, with a combined diagnostic accuracy of 0.918. Specifically, elements such as Fe, Co, and Li showed significant correlations with 63 genes involved in key signaling pathways, including MAPK, SMAD, and Wnt. Genome-wide association studies (GWAS) revealed that gastric cancer-related genes were significantly enriched in cancer-associated pathways and signaling cascades such as Rap1. Metabolomic analysis further demonstrated that 20 elements in the gastric cancer cohort correlated with 94 metabolites, predominantly enriched in pyrimidine and glutathione metabolism pathways. Conclusions: These nine plasma differential elements showed high combined diagnostic efficacy and were associated with genes and metabolites enriched in cancer-related signaling, metabolic reprogramming, and DNA damage response pathways. Together, these findings suggest potential multi-level associations among plasma elemental alterations, genetic variation, and metabolic dysregulation in GC, providing candidate circulating biomarkers and mechanistic clues for future investigation. Full article

Transcription Factor Binding Site (TFBS) prediction is central to understanding gene regulation and various biological processes. This study introduces HetMoE, a heterogeneous, embedding-gated Mixture-of-Experts for TFBS prediction. A gating network operates on the embeddings produced by a pool of complementary expert backbones (a modified-DeepBIND convolutional network, DeepSEA, and DanQ, with a fine-tuned DNABERT-6 genomic language model as an optional expert), so that models of different architectures are combined and weighted on a per-input basis. Models are trained against GC- and repeat-matched real genomic negatives, a fair protocol that avoids the dinucleotide-shuffle artifact, and evaluated with a balanced-test-set protocol (deterministic inference, $B=1000$ paired bootstrap and Analysis of Variance (ANOVA)) on in-distribution and out-of-distribution (OOD) factors. HetMoE attains the best in-distribution performance (mean Area Under the Curve (AUC) $0.881$) and, on a held-out set stratified by DNA-binding-domain family, surpasses fine-tuned DNABERT-6 on the motif-bearing OOD mean across three random seeds ($0.821\pm 0.005$ vs. $0.799\pm 0.008$, a gain present in every seed), most strongly on the sequence-specific and within-family factors. The advantage comes from the gating mechanism rather than from ensembling: input-dependent gating exceeds a static average of the same experts by $0.073$ AUC and the best single expert by $0.088$, and the configuration selected on in-distribution data is a pretraining-free pool of convolutional experts. We further show that the common dinucleotide-shuffle negative protocol inflates the apparent margin (to a mean of $0.864$), which shows the importance of fair, genomically matched negatives. We also introduce an attribution method (ShiftSmooth) that improves interpretability by averaging the gradient over small shifts of the input sequence, giving more reliable attribution for motif discovery and localization than the Vanilla Gradient. Together these provide an efficient and interpretable approach to TFBS prediction that can support further study of genome regulation. Full article

Background: The high costs, long development timelines, and low clinical success rates in oncology highlight an urgent need for reliable computational strategies for drug repositioning. Current machine learning approaches often integrate heterogeneous pharmacogenomic datasets, which may lose biological specificity and limit model interpretability. Methods: In this study, we propose Separate XAI, an explainable artificial intelligence framework that retains dataset-specific biological features by adopting separate preprocessing and training pipelines for the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. Different deep learning architectures such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) were used to predict the drug response in the cancer cell lines. We also used SHapley Additive exPlanations (SHAP) to improve interpretability and identify biologically relevant features. Results: The developed framework showed good predictions with 94.49% accuracy in the CCLE dataset and a mean squared error of 0.0725 in the GDSC dataset. Explainability analysis identified important biomarkers and signaling pathways such as TP53 and KRAS, providing mechanistic insights into drug sensitivity and therapeutic response. Conclusions: The distinct XAI presented here offers an interpretable, biologically grounded framework for cancer drug repositioning by integrating dataset-specific modeling and explainable artificial intelligence. However, integration-based approaches often suffer from confounding effects of experimental and biological heterogeneity, but the proposed framework explicitly preserves dataset-specific characteristics, which potentially could lead to more robust predictions and higher interpretability for precision oncology and translational cancer research. Full article

Genomic testing is a key component of precision oncology; however, Black patients receive genomic testing at lower rates. The purpose of this qualitative study was to identify individual and health system drivers of genomic testing disparities at a National Cancer Institute-designated comprehensive cancer center. We conducted interviews with 15 oncology providers and 11 Black cancer patients between September 2023 and October 2024. These patients were eligible for genomic testing based on National Comprehensive Cancer Network (NCCN) guidelines, being diagnosed within last 10 years (2014–2023), at least 18 years old, and English-speaking. Providers included oncologists and oncology patient navigators. Topics included motivators, barriers, and knowledge of genomic testing and factors influencing decision-making. The Penchansky and Thomas theoretical framework of healthcare access (e.g., availability, accessibility, accommodation, affordability, and acceptability) guided thematic analysis. Among patients eligible for genomic testing, most participants (n = 7) received genomic testing as part of their cancer treatment based on EMRs, however many patients (n = 7) could not recall discussing genomic testing with their oncologist. Most patients and all providers highlighted affordability as a challenge: patients were concerned about unexpected costs associated with testing, while providers were concerned about costs of matched molecular targeted therapy. Both patients and providers highlighted patient-centered communication to mitigate mistrust and promote patient engagement in care. Despite limited awareness, Black patients view genomic testing positively. Addressing multiple dimensions of access is key to improving system-level processes and ensuring that more patients benefit from lifesaving targeted therapy. Full article

Artificial feeds are considered a sustainable alternative to natural live feeds for mandarin fish (Siniperca chuatsi) aquaculture, but their impacts on hepatic metabolism and growth remain unclear. In this study, a total of 800 adult mandarin fish with an initial mean body weight of 152.4 ± 8.7 g were reared for 150 days, and we compared growth performance, liver histology and liver metabolomics of fish fed artificial (AF) or natural live feeds (NF). No significant differences were observed in body length, weight, or condition factor, but the hepatosomatic index (HSI) was significantly higher in the AF group (p < 0.01), accompanied by visible hepatomegaly, pale liver color and severe hepatic steatosis. Partial least squares-discriminant analysis (PLS-DA) showed clear separation of liver metabolomes between groups. Metabolic correlation network analysis revealed tightly connected functional modules of amino acids and lipids, and key metabolites demonstrated significant group-specific changes: energy metabolism intermediates (L-alanine, α-ketoglutarate, phosphoenolpyruvate) and stress-related indicators (cortisol, γ-aminobutyric acid) were significantly upregulated in the NF group, whereas lipid metabolites (cholesterol, phosphatidylcholine, ceramide) and progesterone were remarkably elevated in the AF group. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed upregulation of lipid-related pathways in the AF group and FoxO signaling pathway in the NF group. These findings confirm that artificial feeds drive hepatic lipid metabolism reprogramming without altering growth, but induce obvious hepatic steatosis in mandarin fish. Our findings provide a metabolic foundation for optimizing artificial feed formulations to improve hepatic health and sustainable culture of mandarin fish. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the most prevalent chronic liver disease worldwide, imposing a severe public health burden. Its core pathological hallmark is excessive hepatic lipid accumulation driven by systemic metabolic dysregulation. Concomitant hepatocellular injury impairs hepatic lactate clearance, leading to aberrant lactate buildup in the liver microenvironment. However, the causal role of lactate in exacerbating liver lipid metabolism dysfunction and driving the progression of MASLD remains unclear. Methods: First, we performed a comprehensive bioinformatic analysis of publicly available transcriptomic datasets. Mining of the Gene Expression Omnibus (GEO) database showed that lactate dehydrogenase (LDH) expression was significantly upregulated in liver tissues from both metabolic dysfunction-associated fatty liver disease (MASLD) patients and MASLD mouse models. Next, network pharmacology approaches were employed to predict putative molecular targets that could mediate lactate’s biological effects. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that these candidate targets were predominantly enriched in pathways governing fatty acid metabolism and long-chain fatty acid transport. Molecular docking and molecular dynamics simulations further suggested possible interactions and supported the prioritization of cluster of differentiation 36 (CD36) as candidate lipid metabolism regulators potentially involved in lactate-mediated effects. Finally, liver-specific Ldha knockdown mice (AAV8-TBG-shRNA) and free fatty acid-induced steatotic AML12 hepatocytes were used to investigate the functional relevance of these findings in vivo and in vitro. Results: Network pharmacology analyses preliminarily identified the PPAR signaling pathway as a candidate pathway potentially linking lactate to MASLD. Experimental results showed that exogenous lactate administration was associated with significantly increased lipid accumulation in steatotic AML12 hepatocytes and the livers of MASLD mice, manifested as elevated triglyceride levels and enhanced lipid droplet formation, accompanied by upregulated expression of PPARγ and CD36. Conversely, inhibiting endogenous lactate production or silencing PPARγ or CD36 attenuated this lipid-accumulation phenotype and significantly reduced intracellular triglyceride levels. Conclusions: In conclusion, these findings indicate that lactate exposure is associated with hepatic lipid accumulation and upregulation of the PPARγ/CD36 axis. Pharmacological inhibition or silencing of PPARγ or CD36 attenuates this phenotype, suggesting that this pathway may contribute to lactate-associated hepatic steatosis and potentially accelerate MASLD progression. Full article

Tubulins are essential cytoskeletal components involved in plant cell division and expansion, yet their evolutionary dynamics across plant lineages and potential roles in horticultural seed/kernel development remain insufficiently understood. Here, we identified 2535 tubulin-related genes from 97 plant genomes and performed an integrated phylogenomic analysis. Phylogenetic and synteny network analyses resolved four ancient clades, including α-, β-,γ-tubulin and FtsZ, all of which were predominantly subjected to purifying selection. The α- and β-tubulin subfamilies exhibited lineage-specific expansion in angiosperms, particularly in eudicots, and these expansions were associated with ancient WGD and WGT events while retaining relatively conserved chromosomal contexts. By employing a pyramid-structured microsynteny framework across 12 Rosaceae genomes, we further traced the orthologous conservation and lineage-specific rearrangements of tubulin loci, with Prunus armeniaca as a reference. Spatiotemporal transcriptome profiling of Siberian apricot and kernel apricot revealed a group of tubulin genes highly expressed during key stages of kernel development, highlighting PaTUA1 as a priority candidate gene. Transient overexpression of PaTUA1 in wounded developing apricot kernels was associated with short-term increases in average phytohormone concentrations, including IAA, GA₃, BR, and cytokinins. Together, these results suggest that PaTUA1 represents a promising candidate gene associated with hormone-related responses during apricot kernel development, providing a basis for future functional validation rather than direct evidence of kernel-size determination. Full article

Background: Colorectal cancer (CRC) remains a heterogeneous disease, and improved biomarkers are needed to support prognostic assessment. This study aimed to characterize hub genes in CRC and evaluate whether a gene signature provides biologically meaningful and prognostic information in clinical–genomic models. Methods: We integrated three GEO microarray datasets (GSE110223, GSE110224, and GSE23878) to identify common differentially expressed genes using adjusted $p<0.05$ and $\mid {\mathrm{l}\mathrm{o}\mathrm{g}}_{2}FC\mid >1$. Hub genes and protein expression were identified through protein–protein interaction network analysis using maximal clique centrality and Human Protein Atlas, respectively. Prognostic relevance was evaluated in TCGA-COAD/READ using Kaplan–Meier analysis, multivariable Cox regression, Cox-derived prognostic indices, time-dependent ROC analysis, and regression-based machine learning for internal robustness. Principal component analysis (PCA) was used to derive a standardized PC1-based score from the 10-hub gene signature. Results: A ten-gene mitotic hub signature (TPX2, UBE2C, AURKA, NEK2, PRC1, CCNB1, CDK1, CEP55, FOXM1, and RRM2) was consistently upregulated across the three datasets and enriched for cell-cycle and mitotic pathways. Protein-level and survival analyses supported the biological relevance of several hub genes. In TCGA-COAD/READ, the signature showed limited standalone prognostic value and did not retain independent significance after adjustment for clinical variables, although it contributed modestly in integrated clinical–genomic models. PCA showed a one-dimensional signature, with PC1 capturing the dominant shared expression pattern. Gradient Boosting Regressor (R² = 0.8035, MSE = 0.0473) supported the internal robustness of the DEG-based expression pattern. Conclusions: The ten-gene mitotic hub signature represents a coherent CRC-related proliferative program with limited value as an isolated prognostic marker, but it may still be useful as part of integrated risk models that require external validation. Full article

Bovine Mycoplasma bovis pneumonia (MBP) is an important component of bovine respiratory disease, and its management is complicated by persistent infection and antimicrobial stewardship concerns. Houttuynia cordata Thunb. has reported anti-inflammatory and immunomodulatory activities, but its potential host-directed mechanisms in MBP remain unclear. This in silico study used network pharmacology and molecular docking to identify candidate compounds, common drug–disease targets, enriched biological functions, and predicted ligand–target interactions. A total of 145 putative targets of H. cordata and 474 MBP-associated disease targets were obtained from TCMSP, GeneCards, OMIM, and CTD, yielding 43 common drug–disease targets. Dual-confidence STRING analysis, cytoHubba ranking, and MCODE module analysis prioritized TNF, IL6, IL1B, PTGS2, PPARG, IFNG, CASP3, and MMP9 as candidate core targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated convergence on cytokine-mediated signaling, inflammatory response, immune regulation, oxidative stress, IL-17 signaling, and TNF signaling. Molecular docking suggested favorable predicted interactions for quercitrin–PTGS2, quercetin–TNF, quercetin–IL6, and quercitrin–CASP3. These computational findings suggest that H. cordata may be associated with host inflammatory and immune-response modulation in MBP, mainly through flavonoid-related interactions with inflammation- and apoptosis-related targets. Further bovine-specific experimental validation is required before biological activity or practical application can be inferred. Full article