SEARCH · PubMed Health

Results for “External validation”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 91 records · Page 5Linked to original sources

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed2025

Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

BACKGROUND: Lung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. METHODS: PRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. RESULTS: The best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC=0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI:0.629,0.653) and odds ratio of 1.71 (95% CI:1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. CONCLUSIONS: Multi-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

East Asian never smokers↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.

INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.

Humans↗

PubMed1985

Attrition in prevention research.

Selective attrition can detract from the internal and external validity of longitudinal research. Four tests of selective attrition applicable to longitudinal prevention research were conducted on data bases from two recent studies. These tests assessed (1) differences between dropouts and stayers in terms of pretest indices of primary outcome variables (substance use), (2) differences in change scores for dropouts and stayers, (3) differences in rates of attrition among experimental conditions, and (4) differences in pretest indices for dropouts among conditions. Results of these analyses indicate that cigarette smokers, alcohol drinkers, and marijuana users are more likely to drop out than nonusers, limiting the external validity of both studies. For one project, differential rates of attrition among conditions suggested a possible attrition artifact which will interfere with interpretation of outcome results, possibly masking true program effectiveness. Recommendations for standardizing reports of attrition and for avoiding attrition through second efforts are made.

Adolescent↗

PubMed2026

ceRNA network of lncRNAs and mRNAs in OSF-to-OSCC progression: Diagnostic biomarkers and functional pathways.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic potentially malignant disorder that can progress to oral squamous cell carcinoma (OSCC). Although dysregulated non-coding RNAs have been implicated in oral carcinogenesis, the competing endogenous RNA (ceRNA)-mediated regulatory mechanisms underlying OSF-to-OSCC progression remain poorly understood. This study aimed to identify candidate regulatory molecules and construct a putative lncRNA-miRNA-mRNA network associated with malignant transformation. METHODS: Publicly available microarray datasets (GSE117973 and GSE125866) were analyzed to identify differentially expressed genes between OSF and OSCC. Differentially expressed transcripts were classified into mRNAs and lncRNAs based on public transcript annotations. Highly correlated lncRNA-mRNA pairs were identified using Pearson correlation analysis and integrated with multiMiR-supported miRNA-mRNA interactions obtained from public databases to construct a putative ceRNA regulatory network. Functional characterization focused on apoptosis, epithelial-mesenchymal transition (EMT), and immune checkpoint-related pathways. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance, and selected biomarkers were externally validated using The Cancer Genome Atlas (TCGA) OSCC cohort. RESULTS: Integrated transcriptomic analysis identified several dysregulated mRNAs and lncRNAs associated with OSF-to-OSCC progression. Network analysis highlighted TBC1D3B, RREB1, TEAD3, SREBF1, TMEM41B, FOXK2, and KIAA1958 as prominent hub genes within the putative regulatory network. Functional analyses demonstrated significant associations with apoptosis-, EMT-, and immune checkpoint-related genes, suggesting potential involvement in multiple biological processes contributing to malignant transformation. Several hub genes exhibited strong diagnostic performance, with ROC analysis yielding AUC values ranging from 0.891 to 1.000, indicating excellent discrimination between OSF and OSCC samples. External validation using TCGA further supported the relevance of the identified biomarkers in OSCC. CONCLUSIONS: This study provides a comprehensive transcriptomic framework describing putative lncRNA-miRNA-mRNA regulatory interactions associated with OSF progression to OSCC. The identified hub genes and regulatory networks represent candidate biomarkers for early detection and provide a foundation for future mechanistic and experimental validation. As the proposed ceRNA interactions are computationally inferred, further biological validation is required before clinical application.

RNA, Long Noncoding↗

PubMed1991

An empirical subgrouping of Finnish learning-disabled children.

The internal and external validity of a subgrouping of 82 Finnish children with relatively mild learning disabilities and 84 Controls was explored. The sample was selected from a total population of 1607 second grade pupils. Eight neuropsychological measures from four function areas were selected as classification criteria in cluster analysis. Six consistent and clinically meaningful subgroups were derived. These subgroups were designated as follows: (1) Normal, (2) General Language, (3) Visuo-Motor, (4) General Deficiency, (5) Naming, and (6) Mixed. Most of the LDs were clustered in subgroups (2) through (6); and most of the Controls, in subgroup (1). Several internal and external validation procedures indicated at least moderate validity in the subgroups, with the exception of the Mixed subgroup. The five valid subgroups encompassed 82% of the LDs and 90% of the Controls, and moreover, resembled subgroups which previously have been found among English-speaking children. This suggests that language differences exert no significant effect on the types of the emerging subgroups.

Aging↗

PubMed2026

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool. METHODS: Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female). RESULTS: DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency. CONCLUSIONS: DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.

Humans↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2026

Resolution-dependent self-supervised transfer in chest radiograph classification.

BACKGROUND: Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anchored self-distillation and explicit high-resolution adaptation. Whether these changes improve transfer learning for chest radiograph classification has not been established. METHODS: We benchmarked DINOv3 against DINOv2 and supervised ImageNet initialization across seven chest radiograph datasets comprising 816,183 radiographs from pediatric and adult cohorts. ViT-B/16 and ConvNeXt-B were evaluated under full fine-tuning at 224 × 224 and 512 × 512 pixels, with targeted 1024 × 1024 experiments on three cohorts. Additional analyses examined parameter-efficient adaptation, synthetic label corruption, external validation, frozen 7B features, and computational efficiency. The primary outcome was the mean area under the receiver operating characteristic curve across labels. RESULTS: In adult cohorts, DINOv3 did not consistently outperform DINOv2 at 224 × 224 pixels, but became the strongest initialization at 512 × 512 pixels, especially with ConvNeXt-B. Gains were greatest for small focal and boundary-dependent abnormalities, whereas large-structure findings changed little. The pediatric cohort showed no significant benefit from DINOv3, higher resolution, or backbone choice. Scaling to 1024 × 1024 rarely improved performance and markedly increased computational cost. ConvNeXt-B remained superior to ViT-B/16 under both full and parameter-efficient adaptation. External validation preserved the 512 × 512 DINOv3 advantage, whereas synthetic label corruption showed that this benefit should not be interpreted simply as superior noise robustness. Frozen DINOv3-7B features underperformed relative to fully adapted 86 to 89M-parameter backbones. CONCLUSIONS: For adult chest radiograph classification, DINOv3 provides its most reliable benefit at 512 × 512 pixels, particularly with ConvNeXt-B. Fully adapted mid-sized models at 512 × 512 pixels provided the best performance-cost trade-off in our benchmark.

Journal Article↗

PubMed2026

Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.

BACKGROUND: Glioblastoma (GBM) exhibits profound molecular and spatial heterogeneity, complicating prognostic evaluations. While multiparametric MRI provides crucial multidimensional biological information, conventional end-to-end deep learning integration strategies, such as early or late fusion, often fail to capture complex nonlinear cross-modal interactions. We aimed to systematically evaluate a cross-attention fusion (CAF) architecture for GBM survival prediction and quantify its incremental prognostic value relative to existing clinical tools. METHODS: In this multicenter retrospective study, 386 adults with IDH-wildtype, WHO grade 4 GBM were assembled from an institutional cohort (n = 226), the Chinese Glioma Genome Atlas (CGGA, n = 62), and The Cancer Genome Atlas (TCGA, n = 98). Using a unified 3D ResNet-18 backbone, we compared single-modality models, early fusion, late fusion, and CAF on preoperative T1-weighted, contrast-enhanced T1-weighted (T1CE), and T2-weighted MRI, and integrated the resulting deep learning risk score with routine clinical variables through multivariable Cox regression. Performance was assessed using Harrell's C-index, time-dependent AUC, and decision curve analysis. RESULTS: CAF showed numerically higher, more consistent C-index trends than early fusion, late fusion, and single-modality models (pooled C-index 0.629, 95% CI 0.594-0.664), although pairwise differences in time-dependent AUC were not statistically significant. Integrating clinical variables raised the pooled C-index to 0.691 (95% CI 0.660-0.721) in the treatment-era model, with comparable performance across the three cohorts (Local 0.688; CGGA 0.716; TCGA 0.689); a pre-treatment configuration excluding adjuvant therapy yielded a pooled C-index of 0.642. Under leave-one-cohort-out external validation, the combined model retained significant risk stratification in all held-out cohorts (C-index 0.63-0.71; all log-rank P < 0.01), albeit with attenuated discrimination. The deep learning risk score remained independent after multivariable adjustment (HR 1.41 per SD, 95% CI 1.26-1.57; P < 0.001). Kaplan-Meier analysis confirmed significant high- versus low-risk separation in all cohorts, and decision curve analysis showed greater net benefit than clinical-only and deep-learning-only models. CONCLUSION: The CAF-derived risk score offers prognostic information complementary to routine clinical variables, representing a promising noninvasive tool for individualized risk stratification when molecular profiling is incomplete or unavailable; these findings warrant prospective external validation before clinical use.

cross-attention fusion↗

PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗

PubMed2026

Integrating genetic predictors into subsequent breast cancer risk prediction in survivors of childhood cancer.

PURPOSE: Female survivors of childhood cancer are at high risk for developing breast cancer. The contributions of most general population primary breast cancer genetic predictors to this risk have not been explored. METHODS: Analyses included females who survived ≥5 years after their childhood cancer diagnosis with available array (N = 2096, subsequent breast cancer [SBC]=218) or whole-genome sequencing (WGS; N = 3292, SBC=101) data from the Childhood Cancer Survivor Study and St. Jude Lifetime Cohort. We computed 99 externally-validated primary breast cancer polygenic risk scores (PRS). Using deep-coverage WGS, ClinVar-annotated pathogenic/likely pathogenic (P/LP) variants in breast cancer susceptibility genes were identified. Cox proportional hazards models assessed associations with SBC risk, adjusting for treatments and genetic ancestry. RESULTS: Among 5388 female survivors (genetic ancestry, European: N = 4,752; African: N = 444; East Asian: N = 192), 319 developed SBC. Most (90.9%) PRSs were nominally associated with SBC risk (P < 0.05), but effect sizes varied substantially. PRSs with superior discriminatory ability had greater genome-wide coverage (e.g., 6.4 million-variant PRS, HR per SD = 1.71, 95% CI = 1.43 to 2.05; P = 4.2x10-9) and 7.7-fold higher odds (P = 7.0x10-4) of including variants in multiple DNA damage repair pathways compared with PRSs with weaker risk associations. Among survivors with WGS, 1.6% carried P/LP variants in clinical testing panel genes, which was associated with a 7.4-fold greater risk (95% CI = 3.16 to 17.19). Including genetic factors improved SBC risk prediction by age 40 (P < 0.001) compared to treatment exposures alone. CONCLUSIONS: Externally-validated primary breast cancer genetic susceptibility predictors are relevant for SBC risk prediction and should be prioritized for risk stratification in survivors.

Journal Article↗

PubMedDate not supplied

Recruitment issues, health habits, and the decision to participate in a health promotion program.

To understand the external validity of experimental studies, it is important to estimate the extent to which the participants are representative of the general population. This paper describes recruitment methods and considers the representativeness of participants in the San Diego Family Health Project. The study was designed to experimentally evaluate the effectiveness of a family-based behavior change intervention in Anglo and Mexican-American families. Initial contact with the families was made through a household health survey that was sent home with all fifth- and sixth-grade children in 12 participating elementary schools. The survey asked about a variety of demographic characteristics, dietary habits, and physical activity habits. Parents were also asked if they were interested in participating in the project. Respondents were classified by level of participation into one of three groups: not interested, expressed initial interest but did not attend the recruitment meeting, and volunteered to participate. Level of participation was the independent variable in the analyses. In separate analyses for Anglo and Mexican-American responders, our data suggested many similarities and a few differences among participant groups. The differences that were observed suggest that participants may already have healthier diets than nonparticipants, although only one of four dietary variables differed by participation status in each ethnic group. The external validity of these data and general recruitment issues are discussed.

Adolescent↗

PubMed1990

An external construct validity study of Rorschach personality variables.

This study examined (a) hypothesized relationships between Rorschach variables and self-report test measures relating to nominally similar aspects of personality functioning and (b) interrelationships among Rorschach variables. Sixty-two undergraduates were administered the Rorschach, Barron Ego Strength Scale, Kaplan Self-Derogation Scale, Eagly Self-Esteem Scale, Multiple Affective Adjective Checklist (MAACL), Marlowe-Crowne Social Desirability Scale, and the Rotter Locus of Control Scale. Only a few of the predictions received confirmation: inanimate movement (m) correlated, as expected, with MAACL anxiety and hostility, the egocentricity index (3r + 2)/R (R = total responses) correlated significantly with self-esteem, and human movement with minus form level (M-) correlated (inversely) with ego strength. Among the unpredicted findings were some that appear inconsistent with standard Rorschach interpretation. Rorschach variables human movement (M), and experience actual (EA), generally interpreted as reflecting coping resources, related significantly with self-report measures of poor coping and of dysphoric affect. In general, the Rorschach appears better at identifying weaknesses in the ego rather than strengths.

Adolescent↗

PubMed2026

Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.

BACKGROUND: Neoadjuvant therapy (NAT) is recommended for locally advanced gastric cancer (LAGC), but some patients respond poorly. We aimed to construct a multimodal model integrating CT images, transcriptomic sequencing, and clinicopathological data to assess prognosis in LAGC patients receiving NAT. MATERIALS AND METHODS: This multicenter study included 505 LAGC patients who underwent NAT. Radiomic features were extracted from preoperative CT images of 505 patients. RNA-seq was performed on 277 post-NAT specimens, with additional data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (n = 804). Patients were divided into training (168 cases), internal validation (72 cases), and external validation cohorts. Machine learning algorithms identified key radiomic, molecular, and clinical features associated with NAT response, which were then integrated into a multimodal model to predict overall survival (OS) and disease-free survival (DFS). RESULTS: Six radiomic and three molecular features significantly associated with NAT response were selected. Radiomic risk (hazard ratio [HR]: 4.0, P < 0.001) and molecular risk (HR: 7.1, P < 0.001) were independent prognostic factors. By integrating radiomic risk, molecular risk, and clinical characteristics, a multimodal model (MuMo) was constructed.The C-index results (OS, C-index = 0.855; DFS, C-index = 0.786) demonstrated that MuMo outperformed the single-modality models and ypTNM staging.Mechanistic analysis suggested that the efficacy of neoadjuvant therapy was significantly enriched in immune-inflammatory pathways. CONCLUSIONS: MuMo can effectively predict postoperative survival risk in LAGC patients receiving NAT, serving as a powerful tool for optimizing prognostic assessment.

Humans↗

PubMed2025

Noninvasive detection and differentiation of gastric malignancy using cell-free DNA biomarkers.

INTRODUCTION: Gastric cancer remains a major global health burden, with high mortality driven by late-stage diagnoses that limit treatment options and reduce survival. Current diagnostic methods such as endoscopy and biopsy are invasive, resource-intensive, and impractical for large-scale early detection. OBJECTIVES: This study aimed to develop and validate an ensemble machine learning model integrating four cell-free DNA (cfDNA) fragmentomic feature classes derived from 5 × whole genome sequencing (WGS) data to non-invasively differentiate malignant gastric cancer from benign gastric lesions in high-risk or symptomatic patients. METHODS: A total of 681 plasma samples were prospectively collected, comprising 329 from patients with gastric cancer or high-grade intraepithelial neoplasia (HGIN) and 352 from individuals with benign gastric conditions. The dataset was divided into a training cohort (n = 333) and a temporally independent validation cohort (n = 348). An external validation cohort of 305 participants was also included. RESULTS: The ensemble model achieved an AUROC of 0.920 in cross-validation testing on the training cohort, 0.912 in the independent validation cohort, and 0.896 (95% CI 0.860-0.932) in the external cohort. At a pre-specified prediction threshold of 0.402, the model demonstrated 93.3% sensitivity and 71.9% specificity in the validation cohort, yielding a PPV of 71.3% and an NPV of 93.5%. In the external cohort, sensitivity and specificity were 91.7% and 69.1%, respectively (PPV 75.7%, NPV 88.8%). Model scores correlated with clinical stage, tumor grade, and histopathological subtype. Approximately 71% of non-cancer patients could have been spared unnecessary endoscopy. CONCLUSIONS: The cfDNA fragmentomics-based ensemble model enables accurate, non-invasive differentiation between gastric cancer and benign gastric lesions in high-risk or symptomatic patients. This approach demonstrates strong potential as a pre-endoscopy triage tool, supporting earlier detection and more efficient use of diagnostic resources.

Humans↗