SEARCH · PubMed Health

Results for “Multivariate response regression”

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 19 recordsLinked to original sources

PubMed2025

Simultaneous inference for generalized linear models with unmeasured confounders.

Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. It begins by disentangling marginal and uncorrelated confounding effects to recover the latent coefficients. Subsequently, latent factors and primary effects are jointly estimated through lasso-type optimization. Finally, we incorporate projected and weighted bias-correction steps for hypothesis testing. Theoretically, we establish the identification conditions of various effects and non-asymptotic error bounds. We show effective Type-I error control of asymptotic-tests as sample and response sizes approach infinity. Numerical experiments demonstrate that the proposed method controls the false discovery rate by the Benjamini-Hochberg procedure and is more powerful than alternative methods. By comparing single-cell RNA-seq counts from two groups of samples, we demonstrate the suitability of adjusting confounding effects when significant covariates are absent from the model.

Hidden variables↗

PubMed2026

Coagulation activation is associated with genomic-instability-related features in TP53-mutated AML and MDS: routine laboratory patterns beyond classical disseminated intravascular coagulation.

BACKGROUND: Disseminated intravascular coagulation (DIC) is a serious complication of acute myeloid leukemia (AML) associated with poor prognosis. In TP53-mutated AML and myelodysplastic syndrome (MDS), however, the classical ISTH criteria rarely identify overt DIC, although bleeding and thrombotic complications are well documented in acute leukaemia. We hypothesized that these patients exhibit a lower-grade, subclinical coagulation activation that is associated with the underlying genomic-instability-related features of TP53-mutant disease. METHODS: We retrospectively analyzed 107 consecutive patients with TP53-mutated AML (n = 52) or high-risk MDS (MDS, n = 55), median age 65 years, diagnosed and initially evaluated at our centre between 2018 and 2025. Seven routine coagulation markers and 46 co-mutated genes were evaluated for associations with overall survival (OS) using univariate and multivariable Cox regression, continuous dose-response modeling, and unsupervised k-means clustering. Internal validity was assessed by 1000 bootstrap resamples. RESULTS: Overt DIC according to ISTH criteria was rare (15%). Subclinical activation was common: 50% of patients had a D-dimer ≥1 μg/mL, 41% a fibrinogen ≥4 g/L, and 29% an INR ≥1.2. In univariate analysis, D-dimer, fibrinogen, INR, prothrombin time, and activated partial thromboplastin time were each associated with OS (HR 1.33-1.38 per SD; all p < 0.05). Complex karyotype correlated with higher D-dimer (median 1.39 vs. 0.60 μg/mL, p = 0.022) and fibrinogen (3.91 vs. 2.53 g/L, p = 0.007), while TP53 variant allele frequency (VAF) showed modest positive correlations with D-dimer (ρ = 0.21), INR (ρ = 0.27), and PT (ρ = 0.27; all p < 0.05). Clustering identified three coagulation phenotypes: Silent (51%), Thrombo-inflammatory (31%), and Consumption-like (18%), showing a graded but statistically non-significant gradient in molecular features and a stepwise decline in median OS (14, 10 and 8 months; log-rank p = 0.041). After adjustment for complex karyotype, TP53 VAF, and favorable co-mutation count, the Consumption-like phenotype was associated with a non-significant increased risk (HR 1.83, 95% CI 0.92-3.65, p = 0.084), whereas favorable co-mutation pathways remained independently protective (HR 0.56, 95% CI 0.35-0.90, p = 0.016). CONCLUSION: In TP53-mutated AML/MDS, coagulation activation intensity is associated with the degree of genomic instability. The three phenotypes may add biological resolution beyond classical DIC and cytogenetic risk groups, but represent laboratory patterns rather than validated bleeding or thrombosis prediction tools. However, after accounting for genomic features, phenotypes were not independent predictors of outcome, with complex karyotype, TP53 VAF, and favorable co-mutation count driving prognosis. Because treatment intensity and other clinical confounders were not available, these survival associations are hypothesis-generating. Coagulation profiling remains inexpensive, widely accessible, and offers a practical window into disease biology that warrants prospective validation.

TP53↗

PubMed2025

Pharmacoproteomics in the development of personalised medicine in Age-related Macular Degeneration (PHARPRO-AMD) study protocol.

INTRODUCTION: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss among people over 55 years of age globally, being neovascular AMD (nAMD) its most aggressive form. Its treatment consists of the use of drugs that block vascular endothelial growth factor (anti-VEGF). Proteomics may allow the identification of differentially expressed proteins between responders and non-responders to each anti-VEGF drug. Thus, the objective of Pharmacoproteomics in the development of personalised medicine in Age-related Macular Degeneration (PHARPRO-AMD) is to find new proteomic biomarkers, predictive of response to antiangiogenic treatment in patients with nAMD. METHODS AND ANALYSIS: PHARPRO-AMD is a nationwide, multicentre, prospective, observational study. Treatment-naïve patients with nAMD starting anti-VEGF therapy will be enrolled and followed up for 2 years. During this period, clinical variables will be gathered to classify treatment response. In addition, blood, tear and vitreous and aqueous humour samples will be collected and will undergo a ZenoSWATH proteomic analysis. Relevant biomarkers identified and response classification will be used to perform a multivariate logistic regression and construct receiver operating characteristic curves. RESULTS: The study is expected to identify a panel of proteomic biomarkers predictive of anti-VEGF treatment response. Integrating data from invasive and non-invasive biological samples may enhance clinical applicability. Once validated, these biomarkers could support the design of future clinical trials on biomarker-guided therapies, helping to optimise treatment regimens and improve visual outcomes. CONCLUSIONS: The PHARPRO-AMD study aims to provide proof-of-concept for biomarker-guided anti-VEGF therapy in nAMD, potentially improving vision outcomes. A notable limitation is the exclusion of patients with visual acuity above 73 Early Treatment of Diabetic Retinopathy Study letters, a criterion chosen to reduce potential ceiling effects and improve response assessment accuracy. ETHICS AND DISSEMINATION: Approved by the Galician Network of Ethics Committees, with nationwide validity. Anonymised data will be deposited in open-access repositories and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: Spanish Clinical Studies Registry (REec) (0033-2024-OBS).

Humans↗

PubMed2026

Urine Proteomics as a Source of Biological Information and Outcome Predictor in Living Kidney Transplantation.

Kidney transplantation (KTx) is the preferred treatment for kidney failure. However, post-transplant management is challenging due to the limited lifespan of transplanted organs. Current methods for monitoring post-transplant complications are invasive and have limitations. Therefore, there is an urgent need for novel non-invasive biomarkers. This study investigates the proteomic composition of urine to understand renal biology during the process of transplantation and to identify potential markers for outcome prediction. Urine samples were collected from donors before transplantation and from recipients 4 weeks and 1 year after transplantation. Proteomic analysis was performed using mass spectrometry and label-free quantification. Statistical analyses included principal component analysis (PCA) and enrichment analysis. The resulting key findings were confirmed in an independent validation cohort. In addition, correlative regression models to evaluate the relationship between protein abundance and clinical outcomes in the further course after transplantation were performed. 106 urine samples in the setting of 70 kidney transplantations were analyzed. PCA revealed distinct clustering of donor and recipient samples, indicating significant proteomic changes after transplantation. Hierarchical clustering and gene ontology analysis identified molecular changes as a response to transplantation and showed an over-representation of relevant pathways related to inflammation, cell immune response and coagulation in both the original and validation cohorts. Multivariate regression analysis, including linear and logistic regression, identified 11 potential protein biomarkers, including ORM2, IL1RAP, APP, and FABP4 as predictors of eGFR 12 months after transplantation and 1 HP as a predictor of infections within the first year after transplantation, respectively. This study underscores the potential of non-invasive urine proteomics for identifying biological processes involved in kidney transplantation and for enhancing post-transplant monitoring and outcome prediction. We identified 12 potential biomarkers with added value to standard clinical parameters linked to transplant outcomes, which will be promising candidates for future outcome monitoring after KTx.

Humans↗

PubMed2025

Risk Factors for Breakthrough Acute SARS-CoV-2 Infections in Fully Vaccinated Individuals: A Case-Control Study Nested in a Prospective Cohort in Medelln, Colombia.

To effectively curb coronavirus disease 2019 (COVID-19), it is essential to understand the risk factors for breakthrough acute infections of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) despite vaccination. We conducted a case-control study to assess risk factors for acute SARS-CoV-2 infections requiring hospitalization in vaccinated individuals. The study included 50 vaccinated patients who experienced breakthrough infections requiring hospitalization (inpatient cohort) and 250 control participants from the outpatient cohort of the "Genomic Surveillance and Immune Response Monitoring for COVID-19 in the Metropolitan Area of the Aburrá Valley, Medellín-Colombia". Demographic characteristics, vaccination status, and immune responses were compared between cases and controls using multivariate logistic regression. Advanced age (≥ 65 years), male sex, high-risk comorbidities, and immunosuppression were associated with an increased risk of breakthrough SARS-CoV-2 infections despite prior vaccination. In contrast, receipt of a booster dose and the presence of neutralizing antibodies were linked to a reduced risk of such infections. This study identifies key risk and protective factors associated with breakthrough SARS-CoV-2 infections. Derived from a high-middle-income setting, these real-world findings provide valuable insights to guide targeted vaccination strategies for vulnerable populations.

Humans↗

PubMed1978

[Predictive studies in psychopharmacology (author's transl)].

Once the efficacy of a drug has been proven (by controlled trials), it may be interesting to define "responder" and "non-responder" profiles. This can be done with a non-comparative prediction trial in which initial (pre-treatment) characteristics of subjects with good or poor result are compared, or with a randomized trial in which one attempts to define initial features of favorable or unfavorable cases, which could lead later to a better choice for a future given patient between these therapies (and only these ones) given in similar conditions. Predictor variables can be: demographic, physiologic, nosologic, symptomatic. Statistical methods can be: simple comparison of responses between sub-classes according to one initial feature, or multivariate techniques particularly discriminant analysis (when results are expressed in a dichotomic way), multiple regression (when responses are graded, as with an improvement score). Validation of results of such trials can be done by checking their predictive value on subjects independant of those from which prediction algorithms have been set up.

Adult↗

PubMed2026

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

BACKGROUND: Head and neck squamous cell carcinomas (HNSCCs) cause considerable morbidity and mortality. Multimodal treatment strategies can cause significant toxicity, and therapy options are limited for recurrent disease. Immunotherapy has emerged as a promising approach. However, patient response variability underscores the need for better predictive markers. OBJECTIVE: This study aims to use artificial intelligence to develop two predictive models in patients with HNSCC to assess (1) progression or recurrence following primary curative treatment and (2) long-term survival after immunotherapy schemes in recurrent and metastatic disease. This study will also describe the characteristics of patients with early, locally advanced, and recurrent or metastatic cancers. METHODS: This is a retrospective, observational study of data captured in electronic health records (EHRs) from participating hospitals between January 1, 2014, and December 31, 2021. This study's population comprises adults diagnosed with HNSCC at any stage. Study variables, including demographics, comorbidities, clinical variables, treatments, and outcomes, will be extracted using EHRead, a technology that applies natural language processing and machine learning to extract and analyze structured and unstructured clinical information in deidentified EHRs. Predictive models based on dynamic risk stratification for treatment response and progression or recurrence will be developed using multivariable logistic regressions, decision tree classifiers, and random forest approaches. Descriptive and outcome analyses will be shown for different anatomic subsites and stratified by stage and treatment. RESULTS: This study began enrolling sites in July 2021 and is currently ongoing. By December 2025, data from 10 centers has been collected, comprising a total of 151,934,990 EHRs from 2,159,719 patients. CONCLUSIONS: Development of predictive models using artificial intelligence will advance clinical understanding of HNSCC to improve patient outcomes.

Humans↗

PubMed2026

Clinical and molecular prognostic factors in newly diagnosed pediatric T-cell lymphoblastic lymphoma: a prospective, multicenter, single-arm phase 2 clinical trial.

BACKGROUND: Poor early treatment response in T-cell lymphoblastic lymphoma (T-LBL) is associated with an unfavorable prognosis. This multicenter prospective study evaluated the efficacy of the response-adjusted Chinese Children's Cancer Group (CCCG-LBL-2016) protocol for pediatric T-LBL and examined clinical and molecular prognostic factors. METHODS: Clinical and laboratory data from seven pediatric oncology centers were analyzed. A sub-cohort of 23 patients underwent exploratory integrated genomic analysis, including targeted next-generation sequencing, RNA sequencing, and copy-number array analysis. Survival was evaluated using the Kaplan-Meier method, and prognostic factors were analyzed using multivariable Cox proportional hazards regression. RESULTS: A total of 163 patients (median age: 108 months; 116 males, 47 females) were enrolled, most with advanced disease (stage III: 81.0%; stage IV: 17.8%). Patients were stratified into the low-risk (R1, n = 2) and intermediate-risk groups (R2, n = 161); thirty one patients in the R2 group were escalated to the high-risk intensified regimen (R3) due to poor early response. The 3-year overall survival (OS) was 78.6% ± 3.3% and event-free survival (EFS) was 73.9% ± 3.5%. Outcomes differed by risk group (P < 0.05), with 3-year OS and EFS of 100% and 100% in R1, 82.6% ± 3.4% and 79.5% ± 3.4% in R2, and 58.6% ± 9.1% and 48.3% ± 9.1% in R3. Progression or recurrence occurred in 42 patients (median: 7 months; 3-year OS: 17.1% ± 6.3%). Clinical risk factors included R3 assignment and elevated lactate dehydrogenase. In the exploratory molecular sub-cohort, recurrent alterations included CDKN2A (39.1%), NOTCH1 (26.1%), FBXW7 (21.7%), and MTAP/PIK3R1/NRAS (13.0%). Exploratory multivariable Cox regression analysis identified that CDKN2A alteration was associated with an increased risk of progression or recurrence (hazard ratio = 35.89, 95% confidence interval: 3.07-419, P = 0.004). CONCLUSIONS: Adjusting the risk stratification based on treatment response significantly improved the overall prognosis of T-LBL. However, survival rates remain very low among patients who experience disease progression or recurrence. The preliminarily explored molecular genetic risk factors might contribute to further risk stratification and provide potential therapeutic targets.

Humans↗

PubMed2024

The association between neighbourhood marginalization and SARS-CoV-2 outcomes in patients presenting to emergency departments.

OBJECTIVE: Social and economic marginalizations have been associated with inferior health outcomes in Canada. Our objective was to describe the relationship between neighbourhood marginalization and COVID-19 outcomes among patients presenting to Canadian emergency departments (ED). METHODS: We conducted an observational study among consecutive COVID-19 patients recruited from 47 hospitals participating in the Canadian COVID-19 ED Rapid Response Network (CCEDRRN) between March 3, 2020, and July 24, 2022. We linked data with the Canadian Marginalization Index (CAN-Marg). We used multivariable, multi-level logistic regression models to understand the association between dimensions of neighbourhood marginalization, and severe COVID-19 and in-hospital mortality. RESULTS: There were 55,588 eligible patients. Those from neighbourhoods with a higher proportion of recent immigrants (OR = 0.86 per unit increase [0.81, 0.92]), lower workforce participation (OR = 0.84 per unit increase [0.75, 0.94]), and more housing insecurity (OR = 0.81 per unit increase [0.77, 0.86]) were less likely to present to EDs with severe COVID-19. However, patients from materially marginalized neighbourhoods had increased odds of dying in hospital (OR = 1.19 per unit increase [95% CI 1.09, 1.30]) compared to patients from less materially marginalized neighbourhoods. Patients living in neighbourhoods with a higher proportion of recent immigrants (OR = 0.83 per unit increase [0.78, 0.91]) and lower participation in the workforce (OR = 0.77 per unit increase [0.66, 0.87]) experienced lower odds of dying. CONCLUSION: Despite no association with severe COVID-19 at ED presentation, the only marginalization domain associated with in-hospital mortality was material deprivation. Our findings present insights on ED-seeking behaviour, hospital access, and care that population studies could not.

Humans↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

A per- and polyfluoroalkyl substances-based gene signature links prognosis to immune landscapes in thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy with a rising global incidence and significant heterogeneity. Although per- and polyfluoroalkyl substances (PFAS) exposure is linked to thyroid dysfunction, the prognostic value of per- and polyfluoroalkyl substances-related genes (PFASRGs) and their role in the tumor immune microenvironment (TME) remain poorly understood. This study aims to systematically screen key PFASRGs and evaluate their prognostic value as biomarkers for THCA. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-THCA transcriptomic data and PFASRGs, we constructed a prognostic model through differential expression analysis, univariate and multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO). The model's robustness was validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and clinical nomograms. Furthermore, the TME, immunotherapy response, and drug sensitivities were systematically evaluated. Distinct molecular landscapes were characterized by stratifying the cohort via unsupervised consensus clustering analysis. RESULTS: The eight-gene prognostic model demonstrated robust performance, with area under the curve (AUC) values exceeding 0.85 across all validation cohorts. High-risk patients exhibited significantly shorter overall survival and an "inflamed" TME characterized by high immune scores and checkpoint expression. In contrast, the therapeutic efficacy of anti-programmed death-ligand 1 (PD-L1) agents was more pronounced in the low-risk category, as evidenced by a superior objective response. Furthermore, distinct molecular subtypes and risk-specific sensitivities to targeted agents, such as sorafenib and sunitinib, were identified, highlighting the model's clinical utility for personalized treatment. CONCLUSIONS: We established a novel THCA prognostic framework based on eight PFASRGs. This model exhibits superior performance in risk stratification, effectively distinguishing cohorts with divergent clinical trajectories, unique immune microenvironment features, and varied therapeutic responses. Our findings provide a powerful predictive tool for refining prognostic evaluation and facilitating the implementation of personalized management strategies for THCA patients.

Per- and polyfluoroalkyl substances-related genes ↗

PubMed2026

Exploring precision risk in pediatric vesicoureteral reflux: Innate immune gene variations and reflux outcomes in the RIVUR cohort.

INTRODUCTION: Children with vesicoureteral reflux (VUR) are at increased risk for morbidity from recurrent urinary tract infections (UTIs), yet the factors influencing spontaneous VUR resolution remain poorly defined. This study evaluates whether genetic variations in key urinary innate immune effectors (DEFA1A3, DMBT1, and RNASE7) influences VUR resolution and interacts with prophylaxis to alter clinical response. METHODS: We conducted a secondary analysis of 303 RIVUR participants with available DEFA1A3 and DMBT1 copy number variation (CNV) data and RNASE7 rs1263872 genotype. Primary outcomes were (1) VUR improvement (decrease in grade) and (2) VUR resolution at study exit. Multivariable logistic regression models included genotype, treatment, and their interactions, adjusting for age, sex, baseline grade (high vs low), laterality, bowel/bladder dysfunction, and any UTI. Internal validation used 2000-sample bootstrap with bias-corrected and accelerated confidence intervals and influence diagnostics. RESULTS: Clinical covariates did not significantly predict VUR improvement. Children with DEFA1A3 CNV >5 had higher odds of improvement (OR 2.36, 95% CI 1.12-4.96, p = 0.023), an effect that remained significant in bootstrap analyses. High-grade VUR was associated with lower odds of resolution (OR 0.34, 95% CI 0.12-0.94, p = 0.038). A significant interaction was observed between prophylaxis and high DMBT1 copy number for VUR resolution (interaction OR 2.99, 95% CI 1.11-8.04, p = 0.031); no interaction was seen for improvement. RNASE7 rs1263872 was not associated with either outcome. CONCLUSION: Innate immune gene variation may contribute to heterogeneity in VUR outcomes. High DEFA1A3 copy number was associated with reflux improvement and a DMBT1-prophylaxis interaction was associated with reflux resolution. The results of this study is hypothesis-generating and prompt further evaluation to assess whether a subset of children may experience structural benefit from prophylaxis or have a more favorable natural history based on their innate immune genotype.

Humans↗

PubMed2026

Integrative analysis identifies a glycosylation-related lncRNA signature associated with prognosis in kidney renal clear cell carcinoma.

BACKGROUND: Glycosylation and long non-coding RNAs (lncRNAs) play critical roles in tumor progression. However, the prognostic significance of glycosylation-related lncRNAs (GRLncs) in kidney renal clear cell carcinoma (KIRC) remains largely unclear. This study aimed to identify prognostic GRLncs and construct a predictive model for KIRC prognosis. METHODS: Transcriptomic and clinical data of KIRC patients were analyzed to identify GRLncs associated with overall survival (OS). A prognostic model was constructed based on selected GRLncs, and its predictive performance was evaluated using Kaplan-Meier (KM) survival analysis, receiver operating characteristic (ROC) curves, and univariate and multivariate Cox regression analyses. Patients were stratified into high- and low-risk groups according to the median risk score, and internal validation was performed using training and testing cohorts to assess the stability of the model. Tumor microenvironment characteristics, immune checkpoint expression, immunotherapy response, and drug sensitivity were further analyzed. In addition, the expression of three signature lncRNAs was validated by real-time quantitative polymerase chain reaction (RT-qPCR) in 10 paired KIRC tumor and adjacent normal tissues. Functional roles of selected lncRNAs were investigated using antisense oligonucleotides (ASOs)-mediated knockdown in KIRC cell lines, followed by Cell Counting Kit 8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU) incorporation, colony formation, and migration assays. RESULTS: Five GRLncs (AC093278.2, EPB41L4A-DT, DLGAP1-AS2, AC084876.1, and AC005261.3) were identified and used to construct a prognostic model. AC093278.2 and EPB41L4A-DT were protective factors, whereas DLGAP1-AS2, AC084876.1, and AC005261.3 were risk factors. KM analysis on GRLncs-based risk score stratification revealed patients in the high-risk group had significantly poorer OS than those in the low-risk group. ROC analysis and Cox regression demonstrated that the GRLnc-based risk score served as an independent predictor of KIRC prognosis and exhibited favorable predictive performance compared with conventional clinical variables. High- and low-risk groups also exhibited distinct immune microenvironment characteristics, immune checkpoint expression patterns, and predicted drug sensitivities. RT-qPCR detected significant downregulation of protective factor-EPB41L4A-DT in KIRC tissues, while risk factors-DLGAP1-AS2 and AC084876.1 showed expression trends consistent with their predicted risk attributes. Functional experiments further revealed that knockdown of DLGAP1-AS2 and AC084876.1 suppressed proliferation and migration of KIRC cells, whereas knockdown of EPB41L4A-DT promoted these processes, supporting the biological relevance of these three signature lncRNAs. CONCLUSIONS: This study establishes a novel prognostic model based on five GRLncs that showed promising performance in The Cancer Genome Atlas (TCGA)-based analyses of KIRC. The combined clinical expression analysis and functional validation of three constituent GRLncs (DLGAP1-AS2, EPB41L4A-DT, and AC084876.1) supports the biological plausibility of the model and suggest that GRLncs may serve as potential prognostic biomarkers and therapeutic targets for KIRC.

Kidney renal clear cell carcinoma (KIRC)↗

PubMed2025

Clinical characteristic of isolated thrombocytopenia in patients with bone marrow failure-related germline variants: a retrospective study from a single centre.

BACKGROUND: Immune thrombocytopenia (ITP) comprises the majority of thrombocytopenia. Some patients respond poorly to first-line ITP therapy or develop pancytopenia years later. Recent studies link heterozygous germline variants in acquired aplastic anemia (AA), yet their role in isolated thrombocytopenia carrying bone marrow failure-related germline variants (ITGV-BMFs) remains unclear. While whole-exome sequencing (WES) detects these variants, its cost limits routine use. This study compares prognosis and clinical features of isolated thrombocytopenia in patients with ITGV-BMFsand those with classic ITP. METHODS: The clinical data of patients diagnosed with ITGV-BMFs were retrospectively analyzed and compared with those of patients with classic ITP from August 2018 to February 2024. The baseline characteristics, genomic systematically background, previous treatment response as well as their follow-up outcomes were compared. RESULTS: Patients with ITGV-BMFs demonstrated earlier onset age (p < 0.001), lower bleeding scores and CD34%, along with elevated mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and reticulocyte (RET) counts (p < 0.001). Multivariate logistic regression analysis showed that the patients with ITGV-BMFs may possess distinct characteristics, including an earlier age at onset (p = 0.014) and lower bleeding score (p = 0.048). Notably, MCV and RET showed promising performance in receiver operating characteristic (ROC) curve analysis. During the follow-up period, 57.69% (15/26) ITGV-BMFs patients were further confirmed as aplastic anemia (AA, n = 13) or myelodysplastic syndrome (MDS, n = 2), with a median progression-free survival (PFS) of 7.25 years (p < 0.0001). CONCLUSION: ITGV-BMFs may be diagnosed early using elevated MCV and reticulocyte counts, a diagnostic approach that may lead to earlier intervention and improved prognosis.

Humans↗

PubMed2026

Integrating clinical and genomic features to predict response to neoadjuvant therapy in microsatellite-stable rectal cancer.

BACKGROUND: Neoadjuvant therapy (NAT) has shifted rectal cancer management toward organ preservation. However, achieving a complete response (CR) for "watch-and-wait" strategies is hindered by high response heterogeneity. Although immunotherapy-combined NAT has expanded the candidate pools, the predictive significance of molecular alterations remains unclear. OBJECTIVES: This study aimed to evaluate clinical and genomic profiles of rectal cancer patients undergoing NAT to identify response predictors and to develop a nomogram for estimating CR probability. DESIGN: Retrospective, single-center cohort study. METHODS: This study included 437 patients with rectal adenocarcinoma at Fudan University Shanghai Cancer Center between December 2019 and March 2023. Patients underwent paired tumor and germline genomic sequencing (887-gene panel) before NAT. Logistic and Cox regression analyses were performed to identify clinical and genetic risk factors associated with tumor response and long-term survival. RESULTS: Of the 437 patients, 96.6% had microsatellite-stable (MSS) tumors. In the MSS locally advanced rectal cancer cohort (N = 307), the CR rate was 35.5%. Multivariate analysis identified immunotherapy-combined NAT (iTNT) (OR 4.41, 95% CI: 2.42-8.27), SYNE1 mutation (OR 2.12, 95% CI: 1.06-4.26), negative mesorectal fascia (MRF) status (OR 0.34, 95% CI: 0.17-0.66), and lower tumor location (OR 0.48, 95% CI: 0.27-0.84) as independent predictors of CR. KRAS mutation was the sole independent predictor of reduced disease-free survival (DFS; HR 1.93, 95% CI: (1.11-3.36), p = 0.020). KRAS G12D subtype was associated with the worst 2-year distant metastasis-free survival (71.3%) and exhibited a distinct predilection for lung metastasis. The clinical-genomic nomogram yielded strong discrimination (AUC = 0.705) and calibration, with favorable DCA net benefit. CONCLUSION: Clinical and genomic features jointly determine outcomes in MSS rectal cancer. SYNE1 mutation serves as a novel biomarker for CR, while KRAS mutations, especially the G12D subtype, identify patients at high risk for systemic relapse. The clinical-genomic nomogram facilitates individualized selection for organ-preservation strategies.

biomarker↗

PubMed2026

Development and internal validation of a six-gene prognostic model based on galactose metabolism for overall survival in lung adenocarcinoma.

BACKGROUND: Lung cancer remains a leading cause of cancer incidence and mortality globally. Metabolic reprogramming promotes tumor progression and shapes an immunosuppressive tumor microenvironment. Galactose metabolism is involved in multiple malignancies, but its prognostic value in lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop and internally validate a galactose metabolism-related multigene prognostic model for LUAD. METHODS: A retrospective prognostic model development and internal validation study was performed using RNA sequencing (RNA-seq) and clinical data from 585 LUAD patients in The Cancer Genome Atlas (TCGA). Differential expression, functional enrichment, univariate and multivariate Cox regression were applied to construct a prognostic gene signature. Internal validation was performed using bootstrap resampling. Model performance was evaluated by time-dependent receiver operating characteristic (ROC), C-index, calibration, and Kaplan-Meier analysis. Associations between the model and immune infiltration, immunotherapy responsiveness, and tumor stemness were also analyzed. RESULTS: A six-gene prognostic model (GALT, GANC, PGM1, GALM, B4GALT1, PGM2) was developed. The model showed good discrimination with 1-, 3-, and 5-year area under the curve (AUC) values of 0.719, 0.693, and 0.684, respectively. The low-risk group exhibited significantly longer survival, increased antitumor immune infiltration (CD8+ T cells, M1 macrophages, activated CD4+ memory T cells), higher expression of T cell proliferation-related genes, lower immune checkpoint expression, better predicted immunotherapy response, and lower tumor stemness compared with the high-risk group. CONCLUSIONS: We developed and internally validated a six-gene prognostic model for LUAD based on galactose metabolism. The model shows moderate prognostic performance and is associated with antitumor immunity and tumor stemness. It may be used for prognostic risk stratification and to guide personalized immunotherapy in LUAD.

Galactose metabolism↗

PubMed2026

Identification of a novel human gut microbes and microbial metabolites related genes signature for prognostic implication in head and neck squamous carcinomas.

BACKGROUND: The gut microbiota acts as a critical driver influencing the pathogenesis, therapeutic response, and clinical outcomes across various cancer types. This study aimed to investigate the prognostic value of human gut microbes and microbial metabolites related genes (HGMMMRGs) in head and neck squamous cell carcinoma (HNSCC). METHODS: We constructed a prognostic risk model comprising 19 core HGMMMRGs using LASSO penalized regression and a multivariate Cox proportional hazards model. The predictive performance of the model was evaluated through Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and concordance index. In addition, functional enrichment analysis was performed on the differentially expressed risk genes. Furthermore, the relationship between the immune microenvironment of HNSCC and the risk diagnostic model was examined. Western blot analysis was used to assess the expression levels of IL10 in both HNSCC tissues and adjacent normal tissues. Finally, the correlation between IL10 and the gut microbiota was explored. RESULTS: This study developed a risk score model integrating 19 HGMMMRG genes, which can serve as a tool to guide prognosis and immune microenvironment assessment in HNSCC patients. Survival analysis showed that patients in the high-risk group had significantly worse outcomes (P < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment of differentially expressed genes (DRLs) and immune-related pathways. Western blot analysis further confirmed that IL10 was highly expressed in HNSCC, and the abundance of Faecalibacterium prausnitzii and Enterococcus durans colonies was correlated with IL10 expression. CONCLUSION: We developed a prognostic model for HGMMMRGs that can be effectively used to predict OS in patients with HNSCC. Second, Faecalibacterium prausnitzii and Enterococcus durans can influence the prognosis of patients with HNSCC by mediating the expression IL10 and thereby affecting the prognosis of HNSCC patients. Thus, human gut microbes and microbial metabolite-related genes may be another promising strategy for the treatment of patients with HNSCC.

HNSCC↗