SEARCH · Search PubMed

Results for “Risk stratification”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

838 recordsLinked to original sources

PubMed2026

Thrombus Metabolism-Based Molecular Subtyping for Prognostic Risk Stratification in Acute Ischemic Stroke: A Preliminary Study.

AIMS: To preliminarily characterize metabolic molecular subtypes of cerebral thromboemboli and evaluate their clinical significance in anterior circulation acute ischemic stroke due to large vessel occlusion (AIS-LVO). METHODS: Untargeted metabolomics was performed on thromboemboli retrieved from 36 patients with anterior circulation AIS-LVO using ultra-performance coupled liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Unsupervised hierarchical clustering was employed to identify distinct metabolic molecular subtypes, and their associations with stroke etiology, radiographic severity, and functional outcomes were analyzed. RESULTS: Two distinct thrombus metabolic molecular subtypes (C1 and C2) were identified based on 12 metabolites significantly associated with both short-term (7-day ∆NIHSS) and long-term (90-day mRS) functional outcomes. The C1 subtype, predominantly cardioembolic, exhibited enhanced lipid metabolism, whereas the C2 subtype, primarily atherothrombotic, demonstrated increased folate metabolism. Patients with C1 thromboemboli presented more severe admission ischemic lesions (as indicated by ASPECTS) and experienced poorer short-term and long-term outcomes. A six-metabolite signature derived from LASSO regression was identified for exploratory discrimination of thrombus metabolic subtypes, etiological subtypes, and 90-day outcomes. CONCLUSION: This preliminary exploratory study identifies two metabolically distinct thrombus molecular subtypes with clinical implications in anterior circulation AIS-LVO, providing a novel basis for risk stratification and personalized secondary prevention and warrants further investigation.

Humans↗

PubMed2026

Adjuvant CDK4/6 inhibitors in early-stage breast cancer: Clinical evidence and considerations for risk stratification and treatment selection.

Hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer is the most common biologic subtype and carries a persistent risk of recurrence, particularly in patients with high-risk, early-stage disease. Cyclin-dependent kinase 4 and 6 inhibitors, initially established as a standard component of first-line therapy in the metastatic setting based on improvements in progression-free and overall survival, have since been evaluated in the adjuvant setting. While adjuvant palbociclib did not improve invasive disease-free survival, the monarchE and NATALEE trials demonstrated that abemaciclib and ribociclib, respectively, reduce recurrence risk in patients with high-risk, early-stage disease, with emerging overall survival data further supporting their use. However, the absolute magnitude of benefit varies substantially with baseline risk, and treatment-related toxicity and adherence challenges must be considered, as approximately 20% to 25% of patients discontinue therapy before completion. The integration of these agents into clinical practice also intersects with ongoing efforts to deescalate axillary surgery, as treatment eligibility has been largely defined by anatomic staging, particularly nodal status. Available data suggest that the incremental impact of axillary surgery on identifying candidates for cyclin-dependent kinase 4 and 6 inhibition is modest, especially among the favorable-risk populations now eligible for surgical deescalation. As the field evolves, advances in molecular risk stratification, genomic profiling, and dynamic biomarkers are poised to shift treatment selection from anatomic staging toward biologically driven approaches. Multidisciplinary decision-making that integrates tumor biology, anticipated absolute benefit, toxicity, patient preferences, and surgical considerations will be essential to ensure individualized care.

Humans↗

PubMed2026

Harnessing Polygenic Risk Scores to Refine Venous Thromboembolism Risk Stratification.

BACKGROUND: Venous thromboembolism (VTE) is a major cause of morbidity in patients of all ages. Despite growing interest in polygenic risk scores (PRS) for VTE, their utility remains understudied. Our objective was to evaluate the independent impact of a PRS on VTE susceptibility in adults and children. METHODS: We completed a retrospective, case-control study of two separate cohorts with evaluation of three VTE PRS models, with the primary analysis focused on a 293 single nucleotide polymorphism (SNP) PRS. The adult cohort included 597 VTE cases and 31 998 controls, and the pediatric cohort included 109 cases and 448 controls, both obtained from a de-identified databank with linked genetic data. Separate adult and pediatric multivariable logistic regressions were performed to measure the association of risk factors with VTE. RESULTS: Higher PRS in adults was significantly associated with increased odds of VTE, with each 1-standard deviation increase in PRS conferring an adjusted odds ratio of 1.25 (OR = 1.25, 95% CI 1.15-1.36, p < 0.001). Leading risk factors for adults were cancer (OR = 2.43, 95% CI: 2.04-2.89, p < 0.001) and recent surgery (OR = 2.16, 95% CI: 1.83-2.54, p < 0.001). The standardized PRS also exhibited increased risk for VTE in children (OR = 1.38, 95% CI 1.10-1.74, p = 0.003). Central venous catheterization (OR = 5.65, 95% CI 3.40-9.50, p < 0.001) was the foremost risk factor for pediatric VTE. CONCLUSION: VTE in adults and children is multifactorial, with clinical and genome-wide risk factors contributing. PRS may serve as a valuable adjunct to clinical risk factors for VTE risk stratification.

Humans↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

Pretreatment EBV-DNA/TLG-Based Risk Stratification Is Associated With Survival Outcomes in Nonmetastatic Nasopharyngeal Carcinoma: An Exploratory Study.

Whether combining pretreatment plasma Epstein-Barr virus DNA (EBV-DNA) with 18F-FDG PET/CT-derived total lesion glycolysis (TLG) improves prognostic stratification in nonmetastatic nasopharyngeal carcinoma (NPC) is unclear, particularly in nonendemic populations. We retrospectively analyzed 86 eligible nonmetastatic NPC patients treated with definitive radiotherapy (2010-2024) at a single nonendemic-region institution. EBV-DNA (prespecified cutoff 3500 copies/mL) and TLG (cutoff 200, ROC-derived within this cohort) were dichotomized. Both were available in 59/86 patients (68.6%), who differed from the rest in nodal and overall stage and in RT technique. Baseline PET/CT was in-house in 57 of 86 patients, and a robustness analysis in that subgroup is reported. Given limited events (13 PFS, 9 OS), Cox analyses are exploratory and were supplemented with penalized regression and bootstrap validation. At a median follow-up of 75.5 months, 5-year PFS and OS for the whole cohort (n = 86) were 81.1% and 85.9%. The EBV-DNAhigh/TLGhigh subgroup remained associated with inferior PFS after adjustment in an exploratory model (adjusted HR = 3.97, 95% CI: 1.32-11.93) and, in a single-variable model, with inferior OS (HR = 4.13, 95% CI: 1.10-15.52). Discrimination was comparable to the individual-biomarker model for PFS and lower for OS. Five-year PFS fell monotonically across the four risk groups in the complete-case cohort (n = 59; 89.7%-58.3%). OS differed across groups (log-rank p = 0.044) but was not strictly monotonic, with wide, overlapping confidence intervals. This two-biomarker model is hypothesis-generating and needs prospective, multicenter validation before any consideration of risk-adapted treatment.

Epstein–Barr virus DNA↗

PubMed2026

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

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

Chaperone-mediated autophagy↗

PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗

PubMed2026

Factors associated with additional intervention requirement following ESWL in pediatric patients with urolithiasis.

OBJECTIVE: To identify predictors of additional intervention following extracorporeal shock wave lithotripsy (ESWL) in pediatric patients and to develop a clinically applicable predictive model. MATERIALS AND METHODS: This retrospective cohort study included 647 pediatric patients who underwent ESWL between 2015 and 2025. Demographic, clinical, and radiological variables were analyzed. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of additional intervention. Model performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Additional intervention was required in 65 patients (10.0%). On multivariable analysis, stone size 10-20 mm (OR: 3.04, p = 0.003), moderate (OR: 2.16, p = 0.049) and severe hydronephrosis (OR: 6.05, p < 0.001), and multiple stones (OR: 3.52, p = 0.030) were identified as independent risk factors. Increasing age (OR: 0.84, p = 0.026), history of urolithiasis (OR: 0.41, p = 0.006), and lower calyx location (OR: 0.14, p = 0.034) were associated with a reduced risk. The model demonstrated good discriminative performance (AUC: 0.794), with a sensitivity of 72% and specificity of 75%. Internal validation using bootstrap resampling demonstrated stable model performance, yielding a corrected AUC of 0.732. CONCLUSION: Stone burden, hydronephrosis severity, and stone multiplicity are key determinants of additional intervention after ESWL in pediatric patients. The proposed model shows good predictive performance and may support individualized risk stratification and clinical decision-making.

Humans↗

PubMed2026

To Treat or Not to Treat: Navigating Early-Stage CLL in the Era of Targeted Therapy.

Chronic lymphocytic leukemia (CLL) is most frequently diagnosed at early, asymptomatic stages (Rai 0/Binet A), in which a watch-and-wait strategy remains the standard of care, based on historical trials demonstrating no overall survival benefit from early treatment. Over the past two decades, however, substantial advances in genomic profiling-including immunoglobulin heavy-chain variable region (IGHV) mutational status, TP53 disruption, recurrent gene mutations, and complex karyotype-have uncovered marked biological heterogeneity among early-stage patients and substantially improved prediction of disease progression. In parallel, targeted therapies such as Bruton tyrosine kinase (BTK) inhibitors and venetoclax-based combinations have transformed the management of symptomatic CLL, raising renewed interest in whether early intervention might favorably alter the natural history of biologically high-risk disease. In this review, we critically examine the evolution of prognostication in early-stage CLL, integrate contemporary molecular and clinical risk models, and summarize evidence from both historical chemotherapy-era studies and modern early-intervention trials. We discuss key unresolved controversies, including reliance on surrogate endpoints, the risks of overtreatment, and the persistent absence of an overall survival benefit across all early-treatment strategies. Finally, we outline future research priorities, including refined genomic stratification, minimal residual disease-driven (MRD)-driven approaches, and combination targeted therapies currently under investigation. Despite renewed interest in preemptive treatment, available evidence supports continued observation for asymptomatic patients outside clinical trials.

Humans↗

PubMed2026

Imaging-based surgical stratification of parasagittal meningiomas involving the superior sagittal sinus: a case analysis of 62 patients.

OBJECTIVE: The objective was to evaluate the Superior Sagittal Sinus Involvement Grading (SSIG) system as an imaging-based surgical stratification framework for parasagittal meningiomas adjacent to the superior sagittal sinus (SSS) and to assess its relationship with established sinus invasion grading, venous sinus patency, and operative strategy. METHODS: In this single-center retrospective cohort study, the authors included 62 consecutive parasagittal meningioma resections performed by a single surgeon. SSIG grade was assigned primarily on contrast-enhanced coronal MRI, with CT/MR venography used when available to evaluate sinus patency and collateral venous drainage. Operative variables, resection strategy, and clinicopathological factors were compared across SSIG and Sindou grades, and postoperative complications were compared between low- and high-involvement SSIG groups. RESULTS: SSIG correlated significantly with Sindou grade (rs = 0.790, τb = 0.702, both p < 0.001), and among patients with available venous imaging, it also correlated with the venous sinus involvement grade (rs = 0.742, τb = 0.665, both p < 0.001). With increasing SSIG grade, operative time, intraoperative blood loss, and intraoperative fluid administration increased (p = 0.012, p = 0.008, and p = 0.007, respectively). Compared with the low-involvement group (SSIG grades 1, 2, and 4a), the high-involvement group (SSIG grades 3, 4b, and 5) was less likely to achieve Simpson grade I resection and more likely to adopt Simpson grades II-III strategies (66.7% vs 13.6%, p < 0.001; OR 12.667). Surgery-related complication rates did not differ significantly between groups. The mean follow-up was 13.3 ± 7.9 months, with no radiographic recurrence or progression at last follow-up. CONCLUSIONS: SSIG characterizes parasagittal meningiomas by integrating sinus invasion, venous patency, falcine extension, and parasagittal convexity involvement on preoperative imaging. This surgically oriented framework may help anticipate operative exposure, sinus handling, and resection strategy. Its predictive value for complications and long-term oncological outcomes requires validation in larger cohorts with longer follow-up.

Humans↗

PubMed2026

Lactylation-related immune-metabolic dysregulation defines prognostic and therapeutic stratification in lung adenocarcinoma.

BACKGROUND: Lactylation links lactate metabolism with inflammatory signaling and immune regulation in tumors. However, its cellular distribution and translational value in lung adenocarcinoma (LUAD) remain unclear. METHODS: Single-cell RNA-sequencing datasets GSE189357 and GSE171145 were integrated to characterize lactylation-related activity, intercellular communication, and malignant epithelial cell states in LUAD. Single-cell-derived lactylation-related differentially expressed genes were mapped to TCGA-LUAD and multiple GEO cohorts. Univariate Cox regression and machine learning algorithms were used to construct a lactylation-related prognostic signature (LRPS). The associations of LRPS with prognosis, immunotherapy response, drug sensitivity, genomic alterations, immune infiltration, and inflammation- and metabolism-related pathways were evaluated. KRT7 was further validated using virtual knockout analysis, spatial transcriptomics, and in vitro and in vivo experiments. RESULTS: lactylation-related transcriptional activity showed heterogeneous distribution across LUAD cell populations and was associated with altered cell-cell communication. In malignant epithelial cells, LRTS-high and LRTS-low states exhibited distinct metabolic, inflammatory, and tumor-related pathway activities. LRPS showed stable prognostic performance in TCGA-LUAD and multiple GEO cohorts and remained an independent prognostic factor. Low LRPS was associated with greater potential benefit from immunotherapy, whereas different LRPS groups displayed distinct drug sensitivity, genomic alteration, and immune microenvironment patterns. KRT7 was highly expressed in LUAD and associated with poor prognosis. KRT7 knockdown suppressed LUAD cell proliferation, migration, invasion, colony formation, and tumor growth in vivo. CONCLUSIONS: This study identifies lactylation-related immune-metabolic dysregulation as a clinically relevant feature of LUAD and develops a single-cell-guided LRPS for prognosis and therapeutic stratification. KRT7 emerged as an LRPS-related functional candidate with experimentally supported roles in malignant LUAD phenotypes.

Immunotherapy↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

A standardised risk-stratified approach to the urological management of children with spina bifida.

BACKGROUND: The establishment of a multidisciplinary spina bifida (SB) clinic in 2006 resulted in a review of the literature and an audit of renal outcomes based on then management practices. The audit showed 17% new onset renal scarring over a mean 5.8-year follow-up period. This prompted the development of a local protocol based on risk stratification combining serial ultrasound and non-invasive bladder function assessments, with invasive urodynamic studies reserved for high-risk patients. OBJECTIVE: This study sought to assess the impact of a risk stratified protocol on renal scarring and continence outcomes in children with SB. METHODS: A single centre, retrospective case review of SB patients treated after the introduction of the protocol was conducted. Electronic medical records were used to access patient demographics, continence status and the results of investigations and adherence to the local management protocol. Management that deviated from the protocol was deemed non-adherence. Renal scarring was determined by the presence of scarring on DMSA renogram. Continence was defined as having no urinary incontinence or no more than a single episode of incontinence in a month in patients above the age of 5. For statistical analysis, descriptive statistics in percentages were used, for comparisons of dichotomous variables the Students t-test was performed and to calculate statistical significance a Fisher exact test was done. RESULTS: 167 SB patients were identified with a mean follow up of 56 months. 141 patients were considered adherent to the protocol, 26 were non-adherent. In the protocol adherent group 5 patients (3.5%) developed renal scarring compared with 6 patients (23%) managed out of protocol (p = 0.002). Overall, 49/108 patients were continent either self-voiding 8/108 (7%), with urethral CIC 19/108 (20%) and 22/108 (45%) of them required bladder augmentation. Urinary continence improved with age with 26% continence at age 10, 64% continence by age 15 and 90% continent above 15 years of age. CONCLUSION: A management approach based on risk stratification resulted in incidence of renal scarring that is better than historical controls and comparable to published outcomes. Social continence was achieved in 90% of SB patients by 15 years of age. Hostile bladder changes can be readily identified using non-invasive assessment methods. An expectant treatment approach based on risk stratification is associated with good long term renal outcome and utilises invasive urodynamic resources for SB patients at high risk of renal injury or to address urinary continence in the older child.

Humans↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

A Multi-omics Regulated Cell Death Framework Defines Immune Phenotypes and Guides Precision Therapy in Colorectal Cancer.

Colorectal cancer (CRC) is molecularly and immunologically heterogeneous, contributing to variable treatment response. Because regulated cell death (RCD) intersects with tumor metabolism, immune regulation, and therapeutic susceptibility, we built an RCD-centered framework for CRC stratification. Multi-cohort transcriptomic data were used to infer RCD subtypes with non-negative matrix factorization (NMF) and non-negative least squares (NNLS). Genomic, bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomic datasets were integrated to characterize subtype-associated biology. Machine-learning models were developed for immunotherapy response and survival-risk estimation. Candidate compounds were screened by GDSC2-based drug-sensitivity modeling and molecular docking, and FSTL3 was functionally assessed in vitro. The framework separated CRC samples into two RCD-related phenotypes resembling immune-hot and immune-cold states. RCD1 showed immune activation and higher mutational burden, whereas RCD2 showed immune-suppressed features, intratumoral heterogeneity, and aggressive biology. RCD-associated signatures showed potential for predicting immunotherapy response and survival risk. Dasatinib was prioritized for immune-cold, high-risk tumors, with preliminary evidence supporting its activity in CRC cells, while functional assays suggested a role for FSTL3 in growth, invasion, epithelial-mesenchymal transition, and apoptosis regulation. These findings suggest that RCD-based multi-omics analysis may refine CRC stratification and help generate therapeutic hypotheses.

Colorectal cancer↗

PubMed2026

Prognostic Value of Frailty in Aortic Surgery: A Systematic Review and Meta-Analysis Comparing Frailty Assessment Tools.

BACKGROUND: Frailty is increasingly recognized as an important determinant of outcomes after aortic vascular surgery, but assessment methods vary substantially and the optimal tool for risk stratification remains uncertain. This systematic review and meta-analysis evaluated the prognostic value of preoperative frailty and compared the predictive performance of different frailty instruments in aortic surgery. METHODS: PubMed, Embase, and Cochrane Library were searched from inception to April 27, 2026. Eligible studies included patients undergoing open, endovascular, or hybrid aortic procedures involving abdominal, thoracic, thoracoabdominal, arch, and proximal aortic diseases, including aneurysms and dissections, assessed frailty preoperatively, and reported postoperative outcomes. RESULTS: Thirty studies comprising 419,459 patients were included. Frailty was associated with higher early mortality (odds ratio [OR] 2.20; 95% confidence interval [CI] 1.54-3.14) and late mortality (hazard ratio 2.18; 95% CI 1.64-2.90). Frail patients also had increased risks of major complications (OR 2.52; 95% CI 1.22-5.19), acute kidney injury (OR 1.64; 95% CI 1.34-2.02), and nonhome discharge (OR 5.50; 95% CI 3.05-9.92). Associations were consistent across surgical approaches and aortic segments. Judgment-based or phenotype-like tools yielded higher effect estimates than deficit-accumulation indices, although differences were not statistically significant; among index-based tools, Modified Frailty Index (mFI)-11 outperformed mFI-5. CONCLUSION: Preoperative frailty strongly predicts mortality, morbidity, and loss of functional independence after open, endovascular, and hybrid aortic surgery across different aortic segments and pathologies, including aneurysmal and dissecting aortic disease. Routine frailty assessment may improve risk stratification and perioperative decision-making.

Humans↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

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↗