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PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP + AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗

PubMed2026

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines↗

PubMed2025

Q RadFusion: Hybrid Quantum Classical Radiogenomic Framework for Breast Cancer Diagnosis.

BACKGROUND AND PURPOSE: Breast cancer remains the most common cancer in women worldwide, with early and accurate diagnosis critical for patient survival. Radiogenomics integrates imaging phenotypes with genomic profiles, offering a pathway to precision diagnostics. However, existing classical machine learning models often struggle with the high dimensionality and heterogeneity of multimodal data, leading to issues in calibration and reproducibility. This study presents Q RadFusion, a hybrid quantum-classical framework designed to enhance breast cancer diagnosis by fusing mammography and genomics data. METHODS: Q RadFusion was implemented on two publicly available datasets: CBIS-DDSM (2,600 curated mammography cases, TCIA) and TCGA-BRCA (1,000 genomic profiles, GDC). Imaging preprocessing included bias-field correction, segmentation, and harmonization, while genomic data underwent normalization and imputation. Feature selection was performed using the Quantum Approximate Optimization Algorithm (QAOA), and features were mapped into a quantum Hilbert space using Variational Quantum Circuits (VQC). For multimodal fusion, ResNet encoded mammography features, and a Transformer encoded genomic features. Patient-level and site-held-out splits were used for evaluation. RESULTS: Q RadFusion achieved an AUC of 0.96 and accuracy of 94%, outperforming baselines including CNN-LSTM, ResNet + XGBoost, and multimodal Transformers. Ablation studies confirmed the contribution of quantum components, with optimal performance observed at circuit depth, qubits, and QAOA layers. The model also demonstrated improved calibration and ~ 80% fewer parameters compared to deep fusion networks. CONCLUSION: Q RadFusion demonstrates that hybrid quantum-classical radiogenomic integration can deliver accurate, reproducible, and clinically meaningful diagnostic support for breast cancer, with strong potential for future clinical translation.

Breast Cancer↗

PubMed2025

Use of IR Biotyper as a feasible methodology to type Klebsiella pneumoniae.

UNLABELLED: Klebsiella pneumoniae is one of the most frequently reported healthcare-associated pathogens. The current gold standard approach to perform the epidemiological typing of these bacteria is Whole Genome Sequencing (WGS), which is an expensive and challenging procedure. IR Biotyper (Bruker Daltonics, GmbH) is a new equipment based on Fourier transform infrared spectroscopy, which allows a rapid, low-cost, and user-friendly method to type bacterial isolates. However, there is a need for studies that evaluate the efficacy of the IR Biotyper. The aim of this study was to evaluate the capability of IR Biotyper to type K. pneumoniae according to sequence type (ST) and capsular type-using K locus (KL)-as well as to develop a classifier using machine learning. Seventy-three isolates of K. pneumoniae previously characterized by WGS were selected for IR Biotyper analysis using principal component analysis for dimensionality reduction, Euclidean, and unweighted pair group method with arithmetic mean (UPGMA) for clustering method, and spectra were analyzed in the 1,300-800 cm⁻¹ wavenumber range. Among these, 54 isolates were used to create a classifier, and 19 were used to validate the classifier. When considering the ST, ST307 was grouped in the same cluster as ST11. When KL was considered for the analysis, the clusters were 100% correctly grouped according to their KL type. Furthermore, the classifier developed was able to classify the isolates according to KL with a high concordance. This study showed that KL correlates well with KL for typing K. pneumoniae isolates using the IR Biotyper. Additionally, IR Biotyper demonstrated to be a cost-effective method and a promising tool to classify isolates within minutes. IMPORTANCE: Klebsiella pneumoniae is a major cause of severe hospital infections, and controlling its spread requires quick identification and comparison of bacterial strains. WGS is accurate but expensive, slow, and technically demanding. In this study, we evaluated the IR Biotyper, a device that uses infrared light to analyze bacteria and group them by capsule type-a key feature linked to their spread. The IR Biotyper matched WGS results with high accuracy, delivering results in minutes instead of days. This fast, affordable method can help hospitals detect outbreaks earlier and respond more effectively. Our findings suggest that the IR Biotyper is a valuable tool for routine use in microbiology laboratories, supporting epidemiological surveillance and outbreak control.

Klebsiella pneumoniae↗

PubMed2025

N6-methyladenine identification using deep learning and discriminative feature integration.

N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection and understand its biological significance. Despite the rapid progress of machine learning in bioinformatics, accurately detecting 6 mA sites remains a challenge due to the limited generalizability and efficiency of existing approaches. In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). To optimize computational efficiency and eliminate irrelevant or noisy features, an unsupervised Principal Component Analysis (PCA) algorithm is employed, ensuring the selection of the most informative features. A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. The robustness and generalizability of Deep-N6mA were rigorously validated using fivefold cross-validation on two benchmark datasets. Experimental results reveal that Deep-N6mA achieves an average accuracy of 97.70% on the F. vesca dataset and 95.75% on the R. chinensis dataset, outperforming existing methods by 4.12% and 4.55%, respectively. These findings underscore the effectiveness of Deep-N6mA as a reliable tool for early 6 mA site detection, contributing to epigenetic research and advancing the field of computational biology.

Deep Learning↗

PubMed2026

Predicting 5-Year Mortality in Non-Small-Cell Lung Cancer Using the Korean Central Cancer Registry: Model Development and Validation Study.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is one of the most common cancers and a leading cause of cancer-related mortality, making prognostic prediction clinically essential. Machine learning models are increasingly used to assess prognosis; however, developing systems that combine high discrimination with clear, clinically interpretable reasoning remains challenging. OBJECTIVE: This study aimed to develop deep learning models that predict 5-year mortality in NSCLC using data from the Korea Central Cancer Registry and quantify feature importance through permutation testing. METHODS: We identified 3144 patients diagnosed between 2014 and 2017 who had complete clinical data, pulmonary function test results, histological information, genomic data, and staging details. After preprocessing, the cohort was divided into stratified training, validation, and test sets in a 70%-15%-15% ratio. Five models were tuned using Hyperband across 10 predefined feature groups. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC); additional metrics included accuracy, F1-score, precision, and recall. Groupwise permutation importance was calculated for each model, and the concordance of importance rankings was assessed using the Friedman test. RESULTS: All 5 models yielded comparable discrimination values on the test set (AUC=0.875-0.879). Model A was selected as the primary model and achieved an AUC of 0.879, an accuracy of 0.806, an F1-score of 0.824, and a Brier score of 0.142. Permuting the stage resulted in the largest decrease in AUC (0.217), followed by the pulmonary function test (0.016). Gene mutation had a modest overall impact but became more influential within the adenocarcinoma subset. The Friedman test showed no statistically significant differences in importance rankings across the models (P=.93). CONCLUSIONS: A grouped-input deep learning framework achieved discrimination comparable to a conventional Cox proportional hazards model using the same routine clinical variables for 5-year mortality prediction in NSCLC. Group-level permutation importance provided stable and reproducible insights into the clinical factors influencing risk, which may guide future model refinement and clinical decision-making.

Humans↗

PubMed2025

MWENA: a novel sample re-weighting-based algorithm for disease classification and data interpretation using extracellular vesicles omics data.

BACKGROUND AND OBJECTIVE: Extracellular vesicles (EVs), considered as a form of liquid biopsy, have gained significant attention in recent years due to their stability and the preservation of disease markers. Research studies underscore the clinical significance of molecules found in EVs, highlighting their role as communicative mediators between cells. However, analyzing this data is challenging due to noisy measurements, having far more variables than samples, and some groups (e.g., disease subtypes or experimental conditions) having much less data than others. We therefore develop an algorithm to address aforementioned challenges for the classification of imbalanced EVs omics data. METHODS AND RESULTS: We propose the EV Meta-Weight Elastic Net Algorithm (MWENA), which utilizes logistic regression with elastic net regularization for the classification and identification of EV signatures, effectively addressing the challenges posed by high-dimensional small sample sizes. To mitigate issues related to class imbalance and high noise levels, MWENA incorporates an automatic sample re-weighting function, which uses a meta-net to adaptively learn generalizable patterns directly from the data itself. We validate the MWENA algorithm on both simulated data and EVs omics data, covering six classification tasks that involve four different types of diseases (pancreatic ductal adenocarcinoma, interstitial lung diseases, colorectal cancer, and ovarian cancer) and three clinical scenarios (disease diagnosis, disease-stage screening, and disease-subtype classification). Compared to other machine learning methods, MWENA demonstrates superiority in identifying small class samples and achieves the highest scores in both sensitivity and G-means. Biological analysis is also performed to further explore the significance of selected signatures as biological markers and their roles in disease mechanisms. CONCLUSIONS: We anticipate that our proposed approach will take a modest step in harnessing EV omics data to discover biomarkers, aiding researchers in gaining a comprehensive understanding of biological processes.

Extracellular Vesicles↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2025

Designing smart spatial omics experiments with S2Omics.

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

digital pathology↗

PubMed2026

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.

BACKGROUND: Recent studies have revealed valuable prognostic insights in haematoxylin and eosin (H&E)-stained histological sections and transcriptomic profiles, suggesting potential applications in machine learning. However, existing methods lack sufficient intra- and inter-modal interactions, and face challenges in clinical validation due to incomplete multimodal data. METHODS: We proposed PathoGems (PathoGenomics-based integrative survival prediction), a weakly-supervised, interpretable multimodal learning framework that integrates histology and genomic profiles for precise cancer prognosis prediction. To evaluate the robustness of PathoGems, we initially curated a dataset of 1965 cases across four cohorts from The Cancer Genome Atlas (TCGA), including breast, colorectal, glioblastoma, and esophageal cancers. For external validation, PathoGems was further evaluated on four independent cohorts, consisting of 76 breast cancer and 41 esophageal squamous cell carcinoma cases from Zhejiang Cancer Hospital, as well as 102 colorectal cancer and 58 glioblastoma cases from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). RESULTS: PathoGems effectively stratified patients into favorable and unfavorable risk groups, revealing significant differences in histological patterns, genomic features, and overall survival (log-rank test, p < 0.05). Moreover, the model’s predictions are further supported by visualization and transcriptomic analysis, enhancing interpretability and reliability. CONCLUSIONS: By fusing histological and clinicogenomic multimodal models, PathoGems will provide a solid foundation for developing an innovative tool that aids clinicians in making informed decisions and selection personalized treatment strategies for cancer patients.

Humans↗

PubMed2026

Transcriptome-based high-frequency recurrence index predicts frequent recurrence in non-muscle-invasive bladder cancer after Bacillus Calmette-Guérin therapy.

BACKGROUND: High-frequency recurrence (HfR, ≥ 2 recurrences) in non-muscle-invasive bladder cancer (NMIBC) poses a significant clinical burden. Current risk models, such as the European Organization for Research and Treatment of Cancer (EORTC), the European Association of Urology (EAU), and the UROMOL classification, offer limited predictive accuracy for identifying patients at risk for frequent recurrence despite appropriate treatment. METHODS: A 75-gene high-frequency recurrence index (HfRI) was constructed by selecting recurrence-associated genes using differential expression and Cox regression analyses. The HfRI was computed as a weighted sum of normalized gene expression values. The model was trained on a discovery cohort and validated in multiple cohorts (n = 1379) using machine-learning approaches. Clinical relevance was assessed using recurrence-free survival (RFS) and Cox models, and predictive performance was compared with that of the EORTC, EAU, and UROMOL classifications using the area under the curve (AUC) and the concordance index (c-index). RESULTS: The HfRI robustly stratified patients into high-risk and low-risk groups across six independent NMIBC cohorts. Patients classified as HfRI-high had a significantly greater likelihood of experiencing ≥ 2 recurrences (χ2, p = 0.001) and showed markedly reduced RFS (log-rank test, p < 0.001). The adverse prognostic effect of the HfRI persisted even among patients treated with BCG therapy (log-rank test, p = 0.02). Multivariate analysis revealed that the HfRI was an independent predictor of HfR (HR = 2.82, 95% CI = 1.89-4.20, p < 0.001). Compared with established clinical risk classifiers, the HfRI demonstrated superior predictive performance (AUC = 0.736, c-index = 0.673) in terms of the EORTC (AUC = 0.594), EAU (AUC = 0.557) risk groups, and UROMOL2021 (AUC = 0.596) classification. Pathway analysis revealed that HfRI-high tumors were characterized by upregulation of cell cycle progression and DNA replication pathways, accompanied by suppression of immune signaling pathways. These biological features provide a mechanistic explanation for the reduced responsiveness to intravesical BCG therapy, underscoring the role of HfRI not only as a predictor of recurrence risk but also as a biomarker capable of identifying patients unlikely to benefit from standard BCG treatment. CONCLUSIONS: HfRI represents a robust, transcriptome-based tool for predicting frequent recurrence in NMIBC patients. The HfRI supports earlier identification of patients at risk of high-frequency recurrence, thereby supporting personalized treatment strategies.

Humans↗

PubMed2026

Multi-omics identification and functional validation of signal regulatory protein gamma as a prognostic biomarker and immune regulator in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) comprises biologically diverse tumors, and durable responses to immune-checkpoint blockade are achieved by only a subset of patients. There remains a need for markers that connect clinical outcome with malignant-cell phenotypes and tissue-level immune organization. METHODS: We integrated The Cancer Genome Atlas HNSCC cohort (TCGA-HNSC), five Gene Expression Omnibus (GEO) validation cohorts, single-cell RNA sequencing, Visium spatial transcriptomics, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq)-informed protein-potential inference, pharmacogenomic screening, genetic-risk analysis and experimental validation. A reconstructed 296-pipeline survival modelling framework was used to prioritize prognostic hub genes across validation-cohort-specific analyses. RESULTS: SIRPG was repeatedly ranked among the top ten selected genes in all five validation cohorts. At single-cell resolution, SIRPG-high tumor cells showed stronger malignant-cell features, immune-inhibitory and metabolic programs, Scissor-positive risk association, CLCA2/P53-related perturbation signals and inferred SIRPG-CD47/signal regulatory protein (SIRP) communication. Spatial analyses placed this axis within an immune-checkpoint-coupled niche, supported by Maxspin/multiview intercellular spatial modelling (MISTy) spatial coupling, communication analysis by optimal transport (COMMOT)-inferred CD47-SIRPG communication and scProTrans-inferred CD47/SIRPG protein-potential overlap. Functionally, SIRPG knockdown reduced HNSCC cell viability and increased apoptosis, whereas re-expression of short hairpin RNA (shRNA)-resistant SIRPG restored the CLCA2-BAX/BCL2 protein response. CONCLUSION: Together, these findings identify SIRPG as an immune-related prognostic hub and context-dependent tumor-cell regulator associated with apoptosis, immune communication and spatial microenvironmental organization in HNSCC.

Humans↗

PubMed2026

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

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

Ferroptosis↗

PubMed2026

Transcriptome Analysis and Experimental Validation of Palmitoylation- Related Biomarkers in Atherosclerosis.

INTRODUCTION: Protein palmitoylation contributes to membrane localisation, signal transduction, and cell-fate regulation. It is closely associated with lipid metabolic dysfunction, immune inflammation, and vascular remodelling in atherosclerosis (AS). However, key palmitoylation-related transcriptomic markers and their potential causal associations with AS remain incompletely defined. METHODS: The Gene Expression Omnibus (GEO) dataset GSE100927 was used as the training cohort, and GSE43292 was used as an external validation cohort. Differentially expressed genes were identified using limma and intersected with palmitoylation-related genes to obtain palmitoylation-related differentially expressed genes (PRDEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed using clusterProfiler. Two-sample Mendelian randomisation was used to evaluate potential causal relationships between characteristic genes and AS. Feature selection was conducted using random forest and support vector machine recursive feature elimination (SVM-RFE), and the overlapping genes selected by both methods were retained. Receiver operating characteristic (ROC) curves were used to assess diagnostic performance. A five-gene nomogram was constructed, and its clinical utility was evaluated using calibration curves and decision curve analysis (DCA). Gene set variation analysis (GSVA) was applied to compare pathway activity between high- and low-expression groups for each core gene. Single-cell analysis using Seurat and expression-based cell-cell communication analysis using CellChat were conducted with GSE159677, and upstream transcription factors were predicted using NetworkAnalyst. For in vivo validation, an AS model was established in ApoE⁸/⁸ mice fed a high-fat diet, and aortic gene and protein expression were assessed by RT-qPCR and western blotting. RESULTS: In GSE100927, 51 PRDEGs were identified. GO and KEGG enrichment analyses highlighted pathways associated with regulation of monoatomic ion transport, sarcomere and myofibril organisation, and immune inflammation. Mendelian randomisation suggested a potential protective causal association between SLC7A7 and AS. By integrating MR with random forest and SVM-RFE feature selection, we prioritised five core genes: PLCB2, GMIP, NEXN, PLN, and SLC7A7. These genes showed good diagnostic performance in GSE43292. The resulting nomogram was well calibrated and demonstrated stable net benefit in decision curve and clinical impact curve analyses. Single-gene GSVA identified consistently activated pathways across multiple genes, including innate and adaptive immune recognition, calcium signalling and myocardial contraction/cardiomyopathy, extracellular matrix-receptor interaction, cell junction pathways, autophagy-lysosome pathways, and several metabolic programmes. At the single-cell level, PLCB2 and GMIP were predominantly expressed in T cells and macrophages, NEXN and PLN were enriched in vascular smooth muscle cells, and SLC7A7 was mainly expressed in macrophages. CellChat analysis indicated increased signals for immune-related ligand-receptor interactions. In ApoE⁸/⁸ mice fed a high-fat diet, PLCB2, GMIP, and SLC7A7 were upregulated, whereas NEXN and PLN were downregulated; protein-level changes were concordant with the transcriptomic trends. DISCUSSION: These findings indicate that palmitoylation-related dysregulation in AS converges on immune inflammation, calcium signalling/contractile programmes, ECM remodelling, and autophagy-linked metabolism. The five-gene panel is supported by external validation, single-cell localisation to immune and vascular compartments, and concordant results in ApoE⁸/⁸ mice. CONCLUSION: This study identified and validated five palmitoylation-related genes associated with AS. SLC7A7 showed a potential protective causal signal in MR analysis. The enriched pathway patterns linked these genes to immune inflammation, calcium signalling-contraction coupling, ECM remodelling, cell adhesion, and autophagy- associated metabolic reprogramming. The five-gene nomogram showed potential utility for diagnostic classification and decision support, nominating candidate biomarkers and pathway targets for AS molecular subtyping, diagnosis, and mechanistic investigation.

Atherosclerosis (AS)↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗