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PubMed2026

The extracellular matrix in cancer-associated fibrosis: molecular mechanisms and clinical relevance.

The ECM is a dynamic component of the tumor microenvironment with a critical role in cancer progression, invasion, metastasis, immune exclusion, and response to therapy. Recent advances in proteomic analyses investigating the insoluble ECM fractions (termed "matrisome analysis"), along with single-cell RNA sequencing and spatial transcriptomics, have revealed cancer-specific patterns of ECM remodeling. These studies have identified a panel of recurrently upregulated ECM proteins, including annexin A1, fibrillin-1, fibronectin, periostin, and tenascin-C, actively contributing to tumor growth, invasion, angiogenesis, and immune exclusion. The expression of the cancer-associated ECM is largely driven by cancer-associated fibroblasts (CAFs), whose molecular diversity has been dissected through single-cell profiling and consolidated in emerging CAF atlases across cancers. By investigating the matrisome composition and CAF heterogeneity, these studies have unraveled the pivotal role of the stroma in shaping tumor biology. Based on these discoveries, ECM proteins and CAFs are now being explored as biomarkers and therapeutic targets. Future integration of multi-omics datasets with clinical outcomes will help to translate these insights into novel biomarkers for patient stratification and stroma-directed therapeutic interventions.

Humans↗

PubMed2025

Systems biology successes and areas for opportunity in prostate cancer.

Systems biology approaches have been applied to prostate cancer to model how individual cellular and molecular components interact to influence cancer development, progression, and treatment responses. The integration of multi-omic experimental data with computational models has provided insights into the molecular characteristics of prostate cancer and emerging treatment strategies that have the potential to improve patient outcomes. Here, we highlight recent advancements that have emerged from systems modeling in prostate cancer. These include descriptions of the molecular landscape of prostate cancer and how genomic alterations inform computational models of disease progression, how evolutionary processes give rise to mechanisms of therapeutic resistance, and the development of innovative treatment strategies such as adaptive therapy. We also highlight current challenges in prostate cancer that can be addressed through systems biology approaches. These include tumor heterogeneity, poor immunotherapy response, a paucity of experimental model systems, and the ongoing translation of computational models for clinical decision making. Leveraging systems biology approaches has the potential to lead to a better understanding of the disease and better patient outcomes in the treatment of prostate cancer.

Humans↗

PubMed2026

Amaryllidaceae Alkaloids and Isoquinoline Alkaloids: A Perspective on Historical Approaches to Pathway Elucidation.

Alkaloid biosynthesis is a central topic in plant specialized metabolism because many alkaloids have ecological, pharmacological, and biotechnological relevance. Isoquinoline alkaloids (IAs) and Amaryllidaceae alkaloids (AAs) are both connected to aromatic amino acid metabolism, but they differ in taxonomic distribution, scaffold-forming chemistry, pathway resolution, and biotechnological development. This review compares the historical and methodological trajectories that have shaped IA and AA pathway elucidation, from compound isolation, radiotracer experiments, and biochemical inference to transcriptomics, metabolomics, functional enzymology, isotope-guided active-tissue identification, regulatory studies, and heterologous pathway reconstruction. In IAs, especially benzylisoquinoline alkaloids, broad genomic and transcriptomic resources have supported candidate gene discovery and functional characterization of several branches, including morphinan, protoberberine, benzophenanthridine, and aporphine-related pathways. In contrast, AA biosynthesis has advanced more recently through function-driven approaches that clarified key steps such as N4OMT-mediated 4'-O-methylation, NBS/NR-mediated norbelladine formation, CYP96T-dependent regioselective oxidative coupling, and transient reconstruction of major scaffold-forming branches. Remaining gaps include the unresolved enzymatic formation of 3,4-dihydroxybenzaldehyde in AAs and incomplete functional validation across less-studied IA scaffold classes. By integrating biochemical logic, omics-guided discovery, enzyme evolution, tissue specificity, regulation, and synthetic biology, this review identifies priorities for future alkaloid pathway discovery and sustainable production.

3,4-dihydroxybenzaldehyde↗

PubMed2026

Genome Editing in Solanaceae: Harnessing CRISPR-Cas Technology for Precision Crop Improvement.

Malnutrition and climate-induced stress remain major constraints to global food and nutritional security despite the yield gains of the Green Revolution. Solanaceae crops such as tomato, potato, brinjal, and pepper are key sources of vitamins, minerals, and bioactive compounds. Yet, their genetic improvement has been limited by narrow diversity and complex polygenic traits. The advent of CRISPR/Cas-mediated genome editing provides a transformative platform for precision crop improvement by enabling targeted modification of genes controlling stress tolerance, yield, and nutritional quality. In Solanaceae, CRISPR/Cas applications have successfully enhanced resistance against major pathogens (SlMlo1, SlPelo, SlDCL2), improved abiotic stress tolerance through editing of SlMAPK3, SlCBF1, and SlBZR1, and optimized fruit quality traits via modulation of Psy1, CrtR-b2, and fiAD2/3. Emerging innovations, such as base and prime editing, and RNP-mediated transgene-free delivery, are expanding the precision and scope of editing. However, challenges persist, including genotype-dependent transformation, low HDR efficiency, and incomplete understanding of off-target and epigenetic effects. Integrating CRISPR with omics-guided gene discovery, efficient transformation systems, and regulatory harmonization can accelerate the development of nutritionally enriched, stress-resilient, and sustainable Solanaceae varieties. This review synthesizes recent advances, identifies critical limitations, and outlines future opportunities for deploying CRISPR/Cas technology to achieve next-generation breeding and food system resilience.

CRISPR/Cas↗

PubMed2026

Human Wings Apart-Like Protein as a Serum Diagnostic Biomarker in Cervical Cancer: An Integrative Bioinformatics Analysis with Serum Validation.

Cervical cancer remains a major threat to women's health worldwide, and reliable serum biomarkers for early detection and therapeutic stratification remain limited. Human wings-apart-like (hWAPL) protein has been implicated in cervical carcinogenesis, but its diagnostic and clinical value has not been fully elucidated. To address this gap, this study integrated public multi-omics datasets, including The Cancer Genome Atlas, GEPIA2, the Human Protein Atlas, and single-cell transcriptomic data, to characterize hWAPL expression, clinicopathological associations, immune infiltration, co-expression networks, post-translational modifications, and drug sensitivity predictions. These findings were evaluated in an independent single-center serum cohort comprising 89 patients with histologically confirmed cervical squamous cell carcinoma and 89 healthy female controls. Serum hWAPL and squamous cell carcinoma antigen (SCC) levels were measured, and diagnostic performance was assessed by receiver operating characteristic curve analysis. In silico, hWAPL was broadly upregulated across multiple malignancies, particularly cervical cancer, enriched in malignant epithelial cells and monocytes/macrophages, and associated with shorter progression-free interval, predicted reduced sensitivity to cisplatin, paclitaxel, and 5-fluorouracil, and predicted sensitivity to MCL-1 and Wee1 inhibitors. In the serum cohort, hWAPL levels were significantly higher in patients than controls and discriminated cervical cancer with an area under the curve of 0.961, exceeding SCC alone. Combining hWAPL with SCC further improved diagnostic performance (area under the curve, 0.974; sensitivity, 93.3%; specificity, 95.5%). These findings suggest that serum hWAPL is a potential novel diagnostic biomarker for cervical squamous cell carcinoma whose performance is enhanced by SCC, whereas the observed associations with chemoresistance and immune microenvironment remodeling are hypothesis-generating and require experimental confirmation.

Humans↗

PubMed2026

Engineering Bacillus Subtilis for Efficient Biosynthesis of Riboflavin: Current Knowledge and Future Perspectives.

Riboflavin is an essential water-soluble vitamin that serves as a precursor for the biosynthesis of the flavin cofactors FMN and FAD, which play pivotal roles in numerous redox and energy metabolism reactions. With the growing global demand for sustainable vitamin production, microbial fermentation has become an attractive alternative to chemical synthesis due to its environmental and economic advantages. Among microbial hosts, Bacillus subtilis has emerged as a leading cell factory for riboflavin production owing to its GRAS status, well-characterized genetics, and efficient protein secretion system. This review provides a comprehensive overview of recent advances in metabolic engineering strategies to enhance riboflavin biosynthesis in B. subtilis. Key topics include strengthening biosynthetic and precursor pathways, relieving feedback inhibition, balancing metabolic flux and cell growth, employing adaptive laboratory evolution, and utilizing omics-guided optimization and 13C metabolic flux analysis. Moreover, the integration of synthetic biology tools such as riboswitch engineering, regulatory element design, and high-throughput screening has significantly accelerated strain improvement. Despite remarkable progress, challenges remain in achieving precise regulatory control, optimizing multi-gene expression, and enhancing genome integration efficiency. Future research combining multi-omics data, synthetic regulatory design, and machine learning-driven predictive modeling is expected to further advance the development of intelligent B. subtilis cell factories. However, the practical implementation of these systems remains constrained by the metabolic burden of overproduction and the lack of universal regulatory models that can predict strain performance across varying industrial scales.

Bacillus subtilis↗

PubMed2026

Bridging Organ-on-a-Chip and Omics: A Multi-Dimensional Frontier in Biomedical Research.

Organ-on-a-Chip (OOC) technology offers a powerful platform for replicating human tissue-specific microenvironments, thereby narrowing the translational gap between conventional biomedical models and actual human physiology. Concurrently, omics technologies deliver comprehensive molecular-level insights into biological systems. This review highlights the transformative potential of integrating OOC platforms with high-throughput omics methodologies. We systematically examine the classification, structural configurations, and engineering principles underlying OOC systems, alongside the defining attributes of key omics domains-genomics, transcriptomics, proteomics, and metabolomics. The convergence of dynamic OOC models with advanced omics technologies enables high-resolution, multi-dimensional analyses across numerous biomedical applications, including drug metabolism, disease mechanisms, environmental toxicity assessments, and host-microbiome interactions. This interdisciplinary integration is driving a paradigm shift in precision and translational medicine. However, several challenges remain to be addressed, such as the development of whole-organ mimetics, adaptation of sample collection techniques, and real-time artificial intelligence-based integration of biosensor data with multi-omics datasets. Addressing these hurdles will be vital for unlocking the full potential of this technological synergy in biomedical science.

Multiomics↗

PubMed2026

Cross-Kingdom Siderophores: Biosynthesis, Ecology, and Biotechnological Applications.

Microbial siderophores are high-affinity iron-binding compounds which are produced by bacteria, fungi, and actinomycetes to obtain iron and survive and interact with different species in an iron-deficient environment. While the conventional research on siderophore systems deals mainly with the study within the same taxa, modern researchers have increased their inclination toward cross-kingdom integration of siderophore behavior and their impact on host-associated environments. This can be largely attributed to differences in biosynthetic gene clusters, receptor systems, and regulatory networks, which produce distinct genotype-to-phenotype results determining microbial cooperation and competition. Current advancements in genomic research, together with omics studies like transcriptomics, proteomics, and metabolomics, have created newer insights into how siderophores function. However, the present literature evidences multiple major gaps in multi-omics data because the link between genomes and metabolomes remains weak due to inconsistent regulatory data sets and failure in identifying producer-consumer relationships in polymicrobial systems. Additionally, major constraints like molecular instability, delivery system limitations, host toxicity, limitations in upscaling, and regulatory issues delimit the use of siderophores in medical treatment, agricultural practices, and environmental biotechnology. This review aims to bridge the existing knowledge about siderophore biochemistry, biosynthesis, ecological functions, and genetic regulation across kingdoms while integrating multi-omics outlook with translational considerations. Thus, by connecting molecular mechanisms with evolutionary cross-talk, this study aims to provide a system-level framework in the world of siderophore-mediated iron uptake and therefore shapes future directions in emerging fields of microbial engineering, precision therapies, and sustainable biotechnology.

Fur regulation↗

PubMed2025

A pancreatic cancer organoid biobank links multi-omics signatures to therapeutic response and clinical evaluation of statin combination therapy.

Chemotherapy remains the primary treatment for pancreatic ductal adenocarcinoma (PDAC), but most patients ultimately develop resistance. Here, we established 260 pancreatic cancer organoid lines, followed by extensive multi-omics profiling and therapeutic sensitivity assessments. Integrated analyses uncovered 6 novel coding and 35 noncoding driver candidates. We discovered 2,794 multi-omics features associated with drug sensitivity and 322 features linked to radiation sensitivity. Pharmacogenomic analyses revealed that chemoresistant organoids exhibited enrichment in protein glycosylation and cholesterol metabolism pathways. Notably, statins effectively targeted chemoresistant PDAC organoids. Statin treatment attenuated protein glycosylation, cholesterol levels, and the epithelial-to-mesenchymal transition (EMT) signature in PDAC organoids. We conducted a single-center, single-arm, phase 2 clinical trial (NCT06241352) combining atorvastatin with chemotherapy in patients with advanced pancreatic cancer. Among 37 patients, 26 (70.3%) demonstrated a response, with tumor markers decreasing by more than 20%, suggesting durable responses and potential clinical benefits in this challenging patient population.

Humans↗

PubMed2026

Toward large-scale mass spectrometry-based omics for clinical applications.

INTRODUCTION: As healthcare advances toward personalized medicine, mass spectrometry-based research is advancing our understanding of cellular biology and disease states, and translating these findings into clinical applications. This review highlights recent advances in methodology and technology that demonstrate the capabilities of mass spectrometry-based proteomics, lipidomics, and metabolomics in clinical practice. AREAS COVERED: The ability to directly analyze functional molecules with mass spectrometry uncovers crucial clinical information. Each data modality (proteins, lipids, and metabolites) provides essential insight into healthy and disease states. As technology advances, integrating data from different modalities unlocks new possibilities for clinical research. To gain the most from this multi-omic data, unsupervised integration methods can provide detailed insights into complex biological processes. As the field applies this knowledge, healthcare could experience significant leaps in the near future. This review examines recent advancements in mass spectrometry-based proteomics, lipidomics, and metabolomics, focusing on how improvements in sample preparation, automation, and multi-omics data integration are making large-scale clinical studies more accessible. EXPERT OPINION: Recent technical and methodological advancements in mass spectrometry analysis have propelled healthcare toward a tipping point, shifting from traditional RNA- and DNA-based research to downstream analysis of protein, lipid, and metabolite effectors.

Humans↗

PubMed2006

How omics technologies can contribute to the '3R' principles by introducing new strategies in animal testing.

In Europe, in light of ethical, political and commercial pressure, every effort should be made to replace animals with alternatives (e.g. in vitro models), to reduce the number of animals used in experiments to a minimum and to refine current testing strategies in a way that ensures animals undergo minimum pain and distress. Methods currently used in toxicology for mandatory safety tests rely heavily on the dosing of animals, followed by the detection and pathological evaluation of manifested toxic lesions. Through the integration of so-called 'omics' technologies, a global analysis of treatment-related changes on the molecular level becomes feasible and therefore might provide a means for predicting toxicity before classical toxicological endpoints. This Opinion article summarizes the key features of pushing the '3R' principles in animal testing, discusses the possible impact on safety testing in toxicology and describes the potential of using omics technologies for improved toxicity prediction to meet ethical, political and commercial expectations.

Animal Testing Alternatives↗

PubMed2024

Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Humans↗

PubMed2026

[Applications and Challenges of Deep Learning in Human Genome Research].

In recent years, the advent of high-throughput omics technologies has fueled an explosive growth in human genomic data. Uncovering the latent functions within this vast data has become a significant challenge in functional genomics research. While traditional statistical methods have proved successful for analyzing smaller-scale datasets in the past, they exhibit clear limitations in analytical efficiency and integrating multi-dimensional data, struggling to meet the escalating demands of contemporary genomic analysis. The introduction of deep learning (DL) technologies offers a novel paradigm for this field. This review systematically examines the advances in applying deep learning to human genomics research. Studies demonstrate that when ample labeled data is available, discriminative DL computational methods-such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs)-achieve high accuracy and efficiency in genomic variant discovery tasks. Furthermore, generative DL methods, particularly Large Language Models (LLMs) leveraging self-supervised pre-training strategies, effectively integrate complex genomic information and exhibit superior performance in functional genomic sequence annotation and gene regulation studies. This review also explores the application of LLMs in multi-omics data integration and prediction. Looking ahead, the continued accumulation of long-read sequencing and high-dimensional data is expected to enable DL technologies to integrate increasingly complex and heterogeneous genomic information, playing an increasingly crucial role in human genomics research.

Deep Learning↗

PubMed2026

Population-scale detection of methylation outliers from long-read genome sequencing.

BACKGROUND: Aberrant DNA methylation can mediate the functional effects of rare genetic variation and contribute to imprinting disorders, repeat expansion diseases, and other pathogenic regulatory mechanisms. Long-read sequencing technologies now enable genome-wide detection of CpG methylation alongside genetic variation from a single assay. However, methods for systematic identification and interpretation of methylation outliers from long-read sequencing data remain limited. METHODS: We developed METAFORA, a computational workflow for detecting methylation outlier regions from PacBio and Oxford Nanopore long-read sequencing data. METAFORA constructs population-level methylation references, segments the genome into correlated CpG blocks, infers technical and biological sources of variation through hidden factor estimation, models uncertainty due to variable depth sequencing, and computes covariate-adjusted methylation outlier scores for individual samples. We applied METAFORA across large long-read sequencing cohorts and integrated methylation outliers with multi-omic data. METAFORA is implemented as a snakemake workflow available at https://github.com/tjense25/METAFORA. RESULTS: METAFORA identified methylation outlier regions associated with rare structural variants, tandem repeat expansions, and imprinting abnormalities. We found outlier regions were enriched for molecular outliers across transcriptomic and chromatin accessibility datasets, supporting their functional relevance in gene regulation. In a representative case, METAFORA identified an imprinting defect affecting the GNAS locus associated with an STX16 deletion. CONCLUSIONS: METAFORA enables scalable detection and interpretation of methylation outliers from long-read sequencing data and provides a framework for integrating epigenetic outliers with genomic and multi-omic analyses. These approaches may improve interpretation of rare regulatory variation and support discovery of clinically relevant epigenetic abnormalities in genomic medicine.

DNA methylation↗

PubMed2025

Harnessing fern stress adaptations: From evolution and ecophysiology to molecular biology.

Ferns are the second most diverse vascular plant lineage after angiosperms and have been a key ecological component of Earth's biodiversity for more than 380 million years. Importantly, ferns are sister to seed plants, providing a critical outgroup for understanding the evolution of seed plant features. Ferns are remarkably resilient to abiotic and biotic stresses due to a long evolutionary history with adaptations to diverse habitats, stresses, and herbivores. As a result, ferns produce a multitude of secondary metabolites with unique bioactivities; these chemicals are potentially linked to the adaptation of ferns to herbivory, various abiotic and biotic stresses, and changing environments. Assembled reference genomes and the identification of key metabolic compounds of multiple ferns have already made significant contributions to human health and well-being. Here, we review the recent scientific advances in fern research, including evolution, stress resistance, metabolites and medicinal utilization, and comparative multi-omics applications. We propose that integrated investigations involving ecological, physiological, and molecular techniques will facilitate the future research translation of fern resources in diverse areas including soil remediation, biopesticides, and medicine. Advances in our understanding of fern molecular biology will provide new insights into the evolution of land plants and promote the utilization of ferns for heightened environmental restoration, crop protection and human health.

Ferns↗

PubMed2026

Multi-omics reveal microbial functional traits and antifungal metabolites associated with lower Pseudogymnoascus destructans loads in bat cave soils.

White-nose syndrome, caused by Pseudogymnoascus destructans (Pd), is a major fungal disease threatening hibernating bats. Cave soils can serve as environmental reservoirs for Pd, yet the microbial and biochemical mechanisms underlying naturally low Pd burdens in some cave environments remain poorly understood. Here, we integrated soil microbiome profiling, metagenomics, metabolomics, multi-omics network analysis, and in vitro validation to investigate the ecological and functional basis of differential Pd loads in hibernating bat caves in Northeast China. The three caves shared cold, humid, and weakly acidic microenvironments, but differed significantly in electrical conductivity, soil water content, nutrient availability, and extracellular enzyme activities. Soil microbial communities showed significant inter-cave variation in composition, diversity, and niche breadth, with stochastic processes contributing substantially to community assembly. Environmental variables, particularly pH and Pd load, were important predictors of microbial community structure. Functional analyses revealed that the low-Pd Gezi Cave was enriched in genes associated with organic carbon degradation, nitrogen input and retention, and secondary metabolism. Metabolomic profiling further identified cave-specific metabolite signatures, among which Biochanin A, 4-Hydroxybenzaldehyde, Vanillin, and Arachidonic acid were negatively correlated with Pd loads. Integrated pathway and network analyses showed that differential genes and metabolites jointly mapped to secondary metabolite biosynthesis, aminobenzoate degradation, and flavonoid degradation pathways, forming a microbe-metabolite-functional gene coupling network involving key taxa such as Rhodococcus, Pseudorhodoplanes, and Rhodoplanes. In vitro assays confirmed that 4-Hydroxybenzaldehyde, Coumarin, and Vanillin inhibited Pd growth. Structural equation modelling further indicated that environmental heterogeneity was associated with variation in Pd loads through microbial functional attributes and metabolite profiles. These findings suggest that naturally low-Pd cave soils are associated with coordinated environmental filtering, microbial functional specialization, and antifungal metabolite production, providing mechanistic insight into microbial and biochemical constraints on Pd persistence in cave reservoirs.

Animals↗

PubMed2005

Bioinformatics challenges in proteomics.

A little after the genomic revolution had been celebrated, it seemed as if a competition began to found new -omics disciplines that ultimately all have the same goal, the understanding of biological function. There are many similar definitions for proteomics that can be summarized as follows: proteomics is a large-scale study of structure and function of proteins in an organism or cell. Importantly, the proteome is much more variable than the genome through its interactions with the genome and secondary modifications. It differs depending on the tissue and stage in life-cycle. Hence, proteomics is a very diverse discipline that uses a variety of experimental set-ups and targets in order to elucidate function. Its dissociation from other disciplines can only remain artificial. The bioinformatics applied to proteomics are equally varied. In this review we will focus mainly on a few areas of bioinformatics that seem to us as particularly noteworthy or characteristic for proteomics research, for example in 2DE analysis or mass spectrometry. Another important task of bioinformatics is the prediction of functional properties. We will summarize the approaches taken in order to predict protein networks, which are based on the extensive integration of several kinds of -omics data. We will give a short overview of a demanding field in computational biology, the analysis and prediction of protein 3D structures. In order to provide a broader perspective we will close this review with a generalized description of activities and databases in the realm of proteomics.

Animals↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗