Search Results (3,414)

Background/Objectives: Pneumonia remains a leading cause of emergency department visits worldwide, requiring rapid and objective risk stratification. While traditional scoring systems like CURB-65 and the Pneumonia Severity Index (PSI) are well-established, there is a constant need for dynamic biomarkers reflecting the underlying pathophysiology. This study aims to investigate the prognostic value of the hemoglobin-to-lactate ratio (HLR) in predicting mortality among pneumonia patients. Methods: This retrospective cohort study included 183 adult patients diagnosed with pneumonia at a tertiary training and research hospital between October 2024 and November 2025. Demographic data, clinical findings, laboratory parameters, and prognostic scores (CURB-65, PSI) were recorded. The impact of HLR on mortality was evaluated using univariate and multivariate logistic regression, while its predictive performance was assessed via Receiver Operating Characteristic (ROC) analysis and compared with clinical scores using DeLong’s method. Results: The overall mortality rate was 32.8%. HLR values were significantly lower in the exitus group compared to survivors (4.68 vs. 6.92, p < 0.001). Multivariate analysis revealed that an HLR ≤ 5.65 was an independent predictor of mortality, associated with a 10-fold increase in risk (OR: 10.0; 95% CI: 4.15–24.19; p < 0.001). HLR demonstrated high predictive power (AUC = 0.802), comparable to CURB-65 (AUC = 0.807) and PSI (AUC = 0.829). Notably, the combined HLR + CURB-65 model provided the highest diagnostic accuracy (AUC = 0.857, p = 0.037). Conclusions: HLR is a low-cost and easily accessible biomarker for predicting mortality in pneumonia. It effectively reflects the physiological balance between tissue oxygenation and metabolic failure. Integrating HLR into clinical practice, particularly when combined with traditional scores, can enhance risk (decision of discharge, admission unit [ward, ICU], evaluation of prognosis) in the emergency department. Full article

Metabolic reprogramming is closely linked to tumor proliferation, invasion, and immune escape. Despite its central role in amino acid metabolism, the regulatory mechanisms of asparagine metabolism in hepatocellular carcinoma (HCC) progression remain poorly characterized. Rather than focusing on canonical metabolic genes, prognostic markers were identified from co-expression modules associated with asparagine metabolism signatures. Using the TCGA database and asparagine metabolism-related gene sets, a prognostic risk-scoring model was developed through differential expression analysis, univariate Cox regression, and the LASSO algorithm and externally validated with the GEO dataset (GSE14620). Survival analysis, ROC curve evaluation, nomogram construction, scRNA-seq, GSEA, and drug sensitivity analysis were performed to systematically delineate the molecular mechanisms by which asparagine metabolism drives HCC progression. A three-gene signature comprising BOP1, SAC3D1, and PDE2A effectively stratified patients into high- and low-risk groups. High-risk patients exhibited markedly poorer overall survival, enrichment in tumor proliferation-associated pathways, increased tumor purity, reduced immune cell infiltration, and a substantially higher TP53 mutation rate (38% vs. 13%). In contrast, the low-risk group showed enrichment in pathways linked to hepatoblastoma suppression and liver function, alongside improved predicted response to immunotherapy. Single-cell analysis identified NK cells and endothelial cells as central mediators of asparagine metabolism-driven HCC progression, with BOP1, SAC3D1, and PDE2A displaying dynamic expression patterns during differentiation. Furthermore, the high-risk group was predicted to be more sensitive to chemotherapeutics such as cyclophosphamide and 5-fluorouracil. These findings highlight a potential interplay between nitrogen metabolism and asparagine metabolism in HCC and suggest mechanisms by which these pathways may influence NK cell and endothelial cell function to promote disease progression. This study establishes a novel prognostic model and identifies potential chemotherapeutic vulnerabilities in high-risk patients, warranting further experimental and clinical validation. Full article

Background/Objectives: Community-acquired pneumonia (CAP) is a leading cause of mortality in older adults. Traditional prognostic scores may underestimate risk in frail patients by assuming linear relationships between predictors and outcomes. This study aimed to develop and validate explainable machine learning models integrating the administrative Hospital Frailty Risk Score (HFRS) to predict in-hospital mortality in a nationwide cohort of older adults in Chile. Methods: A retrospective cohort study was conducted using anonymized hospital discharge records from the Chilean National Health Fund (FONASA), including 58,306 hospitalization episodes of adults aged ≥60 years across 72 public hospitals. Fourteen supervised machine learning algorithms were trained using five routinely collected predictors: age, sex, HFRS, Charlson Comorbidity Index, and length of stay. Model performance was evaluated on an independent test set using AUC-ROC. SHAP (SHapley Additive exPlanations) values were calculated to assess global and individual predictor contributions. Results: The Extra Trees classifier achieved the highest discriminative performance (AUC-ROC 0.862), outperforming logistic regression (0.642) and other linear models. SHAP analyses identified HFRS as the most influential predictor (mean |SHAP| = 0.66), followed by length of stay, age, and comorbidities. Conclusions: Ensemble tree-based models incorporating administrative frailty measures provide superior mortality prediction compared to traditional linear approaches. Frailty emerged as the primary driver of risk, supporting scalable early stratification using routinely available hospital data. Full article

Background: The pan-immune–inflammation (PIV) score is a hematological index derived from neutrophil, platelet, monocyte and lymphocyte counts. It has been demonstrated that it has high prognostic value in oncological patients. The aim of this study was to evaluate the association between PIV and 28-day mortality in elderly (≥65 years) critically ill patients admitted to the intensive care unit (ICU) with a diagnosis of sepsis. Methods: This single-centre retrospective study included 96 patients aged ≥65 years who were admitted to the ICU with a diagnosis of sepsis according to the Sepsis-3 criteria between 15 July 2024 and 15 July 2025. Patients were divided into low- and high-PIV groups based on the median PIV. Cox proportional hazards regression analysis and Kaplan–Meier survival analysis were performed. Results: The overall 28-day mortality rate was found to be 55.2% (n = 53). The median PIV was 866.58 (IQR: 497.34–1978.43). The PIV was shown not to be a significant predictor of 28-day mortality (AUC: 0.550; p = 0.400). No difference in survival was observed between the low- and high-PIV groups in the Kaplan–Meier analysis (log-rank p = 0.662). In multivariate Cox regression, high creatinine (HR: 2.683; p < 0.001), high calcium (HR: 2.312; p = 0.004), a low partial thromboplastin time (HR: 0.396; p = 0.005) and a requirement for vasopressors (HR: 2.225; p = 0.025) were identified as independent predictors of mortality. In the Kaplan–Meier analysis for 28-day survival, chronic obstructive pulmonary disease (p = 0.023) and chronic renal disease (p = 0.034) were found to be significantly associated with poorer survival. Conclusions: The PIV is unable to predict 28-day mortality in elderly critically ill patients diagnosed with sepsis. This finding suggests that immunosenescence and inflammaging reduce the predictive power of composite hematological indices. Markers of organ dysfunction, coagulopathy and hemodynamic instability remain more reliable prognostic indicators in geriatric patients with sepsis. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification. Full article

Trauma is a major cause of emergency presentation in small animal practice, and accurate early assessment is essential for prognosis. The Animal Trauma Triage (ATT) score is widely applied in Western veterinary settings but has been less frequently evaluated in Asian veterinary institutions. This prospective observational study assessed the prognostic value of the ATT score and of relevant clinical variables in 184 dogs and cats presenting with traumatic injuries to a university veterinary teaching hospital in Thailand. ATT scores, clinicopathological parameters, and management variables were analyzed in relation to survival outcome. The overall mortality rate was 35.3%. Higher ATT scores, lower blood pH, lower ionized calcium concentrations, and increasing age were independently associated with non-survival (p < 0.05). An ATT score ≥ 5 was associated with increased odds of non-survival (OR = 4.207, 95% CI: 1.903–9.301), yielding a sensitivity of 86.2% and specificity of 40.3% for identifying high-risk patients. Among animals with documented surgical indications, those that did not undergo surgery demonstrated higher mortality than those receiving surgical intervention; however, this finding should be interpreted cautiously because treatment allocation was influenced by clinical stability and owner-related factors. These results demonstrate the clinical usefulness of the ATT score as a triage instrument when interpreted in context with clinical laboratory parameters, age, and treatment responses. Full article

Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a major cause of cancer death. Zinc finger proteins (ZNFs) have been implicated in LUAD progression, functioning either as oncogenes or tumor suppressors. Therefore, an in-depth investigation of ZNFs may contribute to the development of novel diagnostic and therapeutic strategies for LUAD. Methods: Transcriptomic and clinical data were obtained from the TCGA and GEO databases. Prognosis-related ZNF genes were identified using univariate Cox, LASSO, and multivariate Cox regression analyses. An eight-gene ZNF-based prognostic signature was constructed and validated in two independent external cohorts (GSE50081 and GSE26939). A nomogram integrating independent prognostic factors was developed. Immune infiltration, somatic mutation profiles, and drug sensitivity were systematically analyzed. We further focused on FGD3, a key gene from the signature, examining its expression in LUAD cells and tissues, including lorlatinib-resistant models. Results: The prognostic signature comprising TRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2, and ZNF322 effectively stratified patients into distinct risk groups with significantly different overall survival (p < 0.05). The risk score, together with T and N stage, served as independent prognostic predictors (n = 500, p < 0.05). High-risk patients exhibited an immune-desert phenotype, increased tumor mutational burden, and distinct drug sensitivity patterns. Notably, FGD3 expression was downregulated in LUAD tissues (n = 14, p < 0.0001) and lorlatinib-resistant cells, and its restoration suppressed resistant cell proliferation and partially reversed drug resistance. Conclusions: This study establishes a promising ZNF-based prognostic model for LUAD, providing a potential tool for risk stratification and individualized therapeutic decision-making. The identification of FGD3 as a potential mediator of drug resistance highlights its promise as a candidate biomarker and therapeutic target in LUAD. Full article

Background/Objectives: Lung cancer remains a leading cause of cancer-related death, and prognostic assessment relies mainly on TNM staging, which incompletely captures patient heterogeneity. Machine learning (ML) methods may improve survival prediction, but their use in real-world national registries with rigorous validation remains limited. This study aimed to develop ML-derived phenotypes and 1-year mortality risk groups and to evaluate their performance and clinical utility in a national lung cancer cohort from Kazakhstan. Methods: We conducted a retrospective study using a national registry including 13,685 patients. Eight routinely collected predictors were analyzed. K-means clustering was used for exploratory phenotyping. A random survival forest (RSF) model estimated 1-year mortality risk and defined low, intermediate, and high risk groups. Performance was evaluated using temporal validation, cross-validation, and bootstrap correction. Discrimination was assessed using the concordance index, prediction accuracy using the Brier score, and calibration using risk group comparisons. Comparator models included penalized Cox and TNM-only models. Clinical utility was assessed using decision-curve analysis. Results: Two phenotypes showed distinct survival outcomes, although cluster separation was modest. The RSF model showed stable performance (C-index 0.679; corrected 0.663). Risk groups demonstrated strong survival separation (high vs. low: HR 5.66). The RSF model outperformed the penalized Cox (C-index 0.544) and TNM (0.606), with improved accuracy (Brier 0.169 vs. 0.212). Calibration was generally good. Decision-curve analysis showed greater net benefit. Conclusions: An RSF-based model using routine registry data provided robust internally validated risk stratification and improved predictive performance. Clustering results were exploratory. External validation is re-quired before clinical implementation. Full article

Rising temperatures in industrial processes are a serious alert that the system can be shifting from an In Control (InC) to an Out of Control (OutC) state, causing waste, financial losses and, eventually, disaster. Consultation in a case study analyzing the Statistical Quality Control (SQC) routines in a potato chip factory revealed that laymen dealing with data may naively spoil and misuse traditional SQC tools, downgrading the interval-scale temperature data to a simple nominal classification, true or false Negative (N) or Positive (P) symptoms that the production line is InC or OutC. Appropriate scores, negative for true N and false P, and positive for false N and true P, were designed so that their moving averages upcrossing 0 detect clusters of suspicious temperature deregulation, in order to effectively salvage the InC/OutC prognostic value of data. Full article

Background: Clinically actionable biomarkers that reliably distinguish glioblastoma (GBM) from lower-grade glioma (LGG) across expression platforms remain an unmet need. Existing transcriptomic signatures are frequently confounded by batch effects, platform heterogeneity, and the inability to translate to single-patient clinical workflows. Methods: We developed a topology-aware biomarker discovery framework in which analysis-of-variance ranking defines a candidate gene pool, hypergraph co-expression analysis at correlation threshold $\tau =0.75$ identifies densely connected hubs within this pool, rough set reduct optimisation selects a minimal sufficient subset of these hubs, and a Random Forest classifier with stratified cross-validation performs the final discrimination. The pipeline was trained exclusively on GSE16011, a single-platform single-institution Affymetrix microarray cohort free from batch-class confound, and validated on two independent RNA-sequencing cohorts (CGGA-325 and CGGA-693). Robustness was further assessed through bootstrap optimism correction, DeLong cross-cohort equivalence testing, leave-one-gene-out analysis, and a sensitivity analysis under WHO CNS5 (2021) class definitions. Results: The pipeline identified a ten-gene biomarker panel (CSMD3, CHI3L1, PLP2, FRY, FCHSD2, ADM, MCUB, ANXA1, DUSP26, and HK2), achieving a fivefold cross-validation AUROC of $0.906\pm 0.029$ and a held-out AUROC of $0.831$. External validation yielded AUROC $=0.838$ in CGGA-325 and AUROC $=0.836$ in CGGA-693. The biomarker-derived risk score demonstrated independent prognostic value in CGGA-693 (multivariate Cox hazard ratio $=9.195$; $p<0.001$) after adjustment for WHO histological grade, with Kaplan–Meier analysis confirming highly significant survival separation (log-rank $p=4.60\times {10}^{-37}$). Class definitions in the present work follow the histology-based pre-2021 WHO classification used in the source datasets and do not directly incorporate WHO CNS5 (2021) molecular criteria, such as IDH mutation status, that distinguish IDH-wild-type glioblastoma from IDH-mutant grade-IV astrocytoma. After excluding IDH-mutant grade-IV cases from the CGGA cohorts, the classification AUROCs increased to $0.906$ in CGGA-325 and $0.872$ in CGGA-693, with a Cox risk-score hazard ratio of $8.57$ ($p=1.4\times {10}^{-13}$) and log-rank $p=1.4\times {10}^{-32}$ retained on the CNS5-aligned cohort. Conclusions: The methodological contributions introduced in this study, namely, the topology-aware hypergraph candidate pool construction, the rough set combinatorial reduct selection, the fixed-reference single-sample normalisation protocol, and the nested validation regime combining bootstrap optimism correction with cross-platform DeLong testing, are platform agnostic and directly applicable to future CNS5-aligned cohorts as such resources become publicly available, supporting the prospective re-derivation of molecularly defined glioma signatures within the integrated histopathological and molecular frameworks of contemporary neuro-oncology. Full article

Background/Objectives: Sepsis is a dynamic clinical syndrome characterized by a rapidly evolving inflammatory response, where early identification of patients at risk for adverse outcomes remains a major challenge. While inflammatory biomarkers are widely used, their prognostic value at baseline is limited. This study aimed to evaluate whether early changes in inflammatory biomarkers, particularly the neutrophil-to-lymphocyte ratio (ΔNLR), provide additional prognostic value in predicting short-term outcomes in patients with sepsis. Methods: A retrospective longitudinal observational study was conducted, including 168 adult patients admitted with sepsis at a tertiary infectious diseases hospital. Inflammatory biomarkers (CRP, procalcitonin, leukocyte subpopulations, and NLR) were assessed at admission and at 48–72 h. Early changes (Δ values) were calculated and analyzed in relation to a composite adverse outcome, including ICU admission, vasopressor requirement, mechanical ventilation, or in-hospital mortality. Logistic regression and ROC curve analyses were used to evaluate predictive performance. Results: Patients with adverse outcomes had significantly higher baseline inflammatory markers and severity scores. Early reductions in CRP and NLR were more pronounced in survivors, whereas non-survivors showed persistently elevated or minimally decreasing values. In multivariate analysis, ΔNLR remained independently associated with in-hospital mortality (OR 0.91, 95% CI 0.84–0.98, p = 0.015), alongside Sequential Organ Failure Assessment (SOFA) score and septic shock. ΔNLR demonstrated better discriminative performance (AUC 0.74) compared to baseline markers and improved predictive accuracy when combined with SOFA score (AUC 0.81). Higher baseline NLR quartiles were associated with a stepwise increase in adverse outcomes. Conclusions: Early changes in inflammatory biomarkers, particularly ΔNLR, provide clinically relevant prognostic information beyond baseline measurements and severity scores in sepsis. Dynamic assessment of immune response may improve early risk stratification and support more individualized clinical decision-making. Full article

Background and Objectives: Transcatheter edge-to-edge repair (TEER) has emerged as an effective treatment option for patients with severe mitral regurgitation who are at high surgical risk. However, clinical outcomes after TEER remain heterogeneous and are influenced not only by cardiac parameters but also by systemic comorbidities and multiorgan dysfunction. The albumin–bilirubin (ALBI) score, derived from serum albumin and bilirubin levels, has recently been proposed as a simple marker of hepatic dysfunction and cardio-hepatic interaction. This study aimed to evaluate the prognostic value of baseline ALBI score in predicting long-term mortality after TEER. Materials and Methods: In this single-center retrospective cohort study, 106 consecutive patients with symptomatic moderate-to-severe or severe mitral regurgitation who underwent TEER between January 2019 and December 2025 were included. Baseline ALBI score was calculated using pre-procedural serum albumin and bilirubin levels. Cox proportional hazards regression analysis was used to identify predictors of long-term mortality. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by ridge-penalized multivariable Cox modeling to minimize overfitting. The incremental prognostic value of ALBI was assessed using concordance index (C-index) comparison between predictive models. Receiver operating characteristic (ROC) analysis and Kaplan–Meier survival analysis were also performed. Results: During a median follow-up of 17.9 months, 30 patients (28.3%) died. Higher baseline ALBI scores were significantly associated with increased mortality risk. In multivariable analysis, ALBI score (HR 3.35, 95% CI 1.46–7.71; p = 0.004), left atrial volume index (LAVI) (HR 1.02, 95% CI 1.01–1.03; p = 0.005), and log-transformed B-type natriuretic peptide (BNP) (HR 1.37, 95% CI 1.02–1.86; p = 0.039) remained independent predictors of mortality. Addition of the ALBI score improved model discrimination, increasing the C-index from 0.845 to 0.886. ROC analysis demonstrated good predictive performance of the ALBI score (area under the curve [AUC] = 0.877), with an optimal cut-off value of −1.67. Conclusions: Baseline ALBI score is independently associated with long-term mortality after TEER and may provide potential incremental prognostic information. However, the observed improvement is modest and should be interpreted cautiously. These findings support a potential role of ALBI as a complementary marker, which requires validation in larger prospective studies. Full article

Background and Objectives: The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is an immunonutritional biomarker reflecting systemic inflammation, nutritional status, and immune competence. Its dynamic behavior and prognostic relevance in multiple myeloma (MM) following autologous stem cell transplantation (ASCT) remain insufficiently characterized. Materials and Methods: In this retrospective cohort study, 95 MM patients undergoing ASCT were analyzed. HALP scores were calculated at diagnosis and at post-transplant day +100, and dynamic changes (ΔHALP) were assessed. Associations with overall survival (OS) and progression-free survival (PFS) were evaluated using Kaplan–Meier and multivariable Cox regression analyses, with maintenance therapy incorporated as a covariate. Results: During a median follow-up of 50 months (range: 11.0–144.0), 36.8% of patients progressed or relapsed, and 23.2% died. Baseline HALP was associated with both OS and PFS in unadjusted analyses; however, the inverse association at diagnosis was substantially attenuated after adjustment for maintenance therapy (HR: 0.71, 95% CI: 0.28–1.80; p = 0.466). HALP at day +100 showed a robust association with PFS that strengthened after adjustment (HR: 3.27, 95% CI: 1.45–7.38; p = 0.004). The discriminative performance for treatment response was limited, with 95% CIs encompassing AUC = 0.50. Conclusions: Post-transplant HALP at day +100 emerged as the most robust HALP-based prognostic indicator for PFS. Given the small sample size, limited OS events (n = 22), use of outcome-driven cut-offs, and absence of cytogenetic and minimal residual disease data, dynamic HALP assessment may provide exploratory prognostic information warranting validation in larger, prospective, multi-center cohorts. Full article

Canine leishmaniosis can cause a variety of signs. The detailed assessment of disease severity lacks a standardized, validated scoring system. This prospective study aimed to develop and validate an objective scoring system (“Total Leishmania Score”, TLS) combining clinical and laboratory parameters for monitoring dogs with Leishmania (L.) infantum infection. Fifty-one L. infantum-infected dogs were examined every 3 months over 1 year. Evaluations included physical examination, complete blood count, serum biochemistry, urinalysis including protein-to-creatinine ratio, and a L. infantum antibody Enzyme-Linked Immunosorbent Assay (ELISA). At each visit, 2 veterinarians applied the TLS, comprising 10 clinical and eight laboratory parameters graded on a four-point severity scale (0–3) and weighted according to their estimated prognostic relevance values. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland–Altman analysis. Longitudinal changes were analyzed using robust linear mixed-effects models. In total, 488 scores were evaluated. Interobserver reliability was excellent (ICC: 0.998; CI_95%: 0.997–0.998; p < 0.001) with no relevant systematic bias. Reliability remained excellent at all time points (ICC: 0.996–0.999). The TLS increased significantly before and during relapse (p < 0.001) and decreased significantly within 3 months after leishmanicidal treatment (p < 0.001). The TLS demonstrated excellent reliability and responsiveness, supporting its use for the longitudinal monitoring of dogs with leishmaniosis. Full article

Synaptogenesis-related neuron–glioma interactions are increasingly recognized in glioma, yet it remains unclear whether routine H&E morphology can capture these programs and improve prognostic stratification. We integrated H&E whole-slide images, transcriptomes, and clinical data from 434 TCGA gliomas. Deep learning and quantitative pathology yielded an integrated histomorphologic feature set of 2678 features. Synaptogenesis-related activity was quantified using ssGSEA for ninety-eight synaptogenesis-related genes. In the training cohort, Spearman analysis identified 149 correlated histomorphologic features, which were refined to thirty-five by elastic net regularization. Seventeen prognostic candidates were entered into the MIME1 framework, and the most parsimonious model, Enet[0.1], retained fourteen non-zero-coefficient features to define the synaptogenesis-associated histomorphologic signature and construct the pathology-derived risk score (PRS). Multi-omic analyses, Human Protein Atlas validation, and single-nucleus RNA-seq were used to investigate the hub gene and its cellular context. PRS robustly stratified survival in both training and validation cohorts and remained an independent prognostic factor after adjustment for age and 2021 WHO CNS grade. High-risk tumors showed increased stromal and immune scores and enrichment of immune, adhesion, and phagosome-related pathways. EFNB2 emerged as the hub gene and was enriched in glioblastoma, and EFNB2-positive malignant cells displayed prominent communication with neurons, including EFNB2-EPHB1 signaling. Exploratory re-analysis of the myeloid compartment further showed that glioblastoma was enriched for suppressive TAM-like states relative to astrocytoma grade 2, supporting a shift toward a more tumor-associated and potentially immunosuppressive microenvironment. Routine H&E histomorphology can capture synaptogenesis-related molecular programs in glioma. The resulting PRS provides clinically relevant prognostic stratification, while EFNB2-positive malignant cells may represent a candidate hub for neuron–tumor communication within a remodeled tumor ecosystem. Full article