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Results for “Biomarker stratification”

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PubMed2026

A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited. METHODS: We conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated. RESULTS: Women who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort. CONCLUSION: Early microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.

Humans↗
PubMed2005

Altered expression of TFF-1 and CES-2 in Barrett's Esophagus and associated adenocarcinomas.

Identification of biomarkers to recognize individuals with Barrett's esophagus (BE) predisposed to develop malignancy is currently a pressing issue. We utilized gene expression profiling to compare molecular signatures of normal esophagus and stomach, BE, and adenocarcinoma (AC) to identify such potential biomarkers. Over 22,000 genes were analyzed by oligonucleotide microarrays on 38 unique RNA Unsupervised and supervised clusterings were performed on a subset of 2849 genes that varied most significantly across the specimens. Immunohistochemistry (IHC) for two of the significantly differentially expressed gene products was performed on tissue microarrays. Unsupervised clustering identified two discernable molecular BE profiles, one of which was similar to normal gastric tissue ("BE1"), and another that was shared by several of the AC specimens ("BE2"). The BE1 profile included expression of several genes that have been described as tumor-suppressor genes, most notably trefoil factor 1 (TFF-1). The BE2 profile included expression of genes previously found overexpressed in cancers, such as carboxylesterase-2 (CES-2). IHC demonstrated the loss of TFF-1 late in the progression of BE to AC. It also revealed CES-2 as being upregulated in AC documented to have arisen in the presence of BE. These potential biomarkers, as well as the relative expression of genes from BE1 versus those from BE2, may be validated in the future to aid in risk stratification and guide treatment protocols in patients with BE and associated AC.

Adenocarcinoma↗
PubMed2004

Inflammatory markers and coronary artery disease.

In the past decade, the important role of inflammatory processes in the development and progression of atherosclerosis has been clearly established. Over the past two years, different circulating (inflammatory) biomarkers indicating the instability of atherosclerotic plaques have been identified. These new markers do not only serve as diagnostic tools for the identification of patients with unstable angina or acute myocardial infarction but also help us to identify high-risk patients. As a result, different markers are used clinically for risk stratification in stable coronary artery disease as well as in acute coronary syndromes.

Biomarkers↗
PubMed2003

Distribution and dose response of laboratory markers to alcohol consumption in a general population: results of the study of health in Pomerania (SHIP).

OBJECTIVE: Biomarker distributions must be well known for use as screening tools for hazardous alcohol consumption in general populations. The aim of this study was to investigate characteristics of carbohydrate-deficient transferrin (CDT) and to compare it with gamma-glutamyltransferase (GGT) and erythrocyte mean corpuscular volume (MCV) in regard to distribution in the general population; the dose response relationship between alcohol consumption and biomarkers; and the effect of gender, age, body mass index (BMI) and smoking. METHOD: In a cross-sectional health survey in northeast Germany (1997-2001), a sample of 7,008 men and women aged 20-79 years was drawn, following stratification by age and gender. Of this sample, 4,310 subjects (2,193 women) took part in the study. Alcohol consumption was evaluated by self-reports with a beverage specific quantity-frequency method. RESULTS: Biomarker distributions differ across age and gender. The distribution of drinkers and nondrinking subjects showed considerable overlap. The association of alcohol consumption to laboratory markers is weak in the general population: strongest for GGT, followed by CDT and MCV For CDT and MCV it is weaker in women than in men. We found increasing risk of all three elevated marker values with increasing alcohol consumption and significant effects of age, gender, smoking and body mass index on the alcohol-biomarker dose response curve. CONCLUSIONS: When biomarkers are used for screening, all confounding effects have to be taken into account and adjusted normal ranges must be used. CDT shows no overall advantage over GGT. Low correlation of biomarkers with alcohol consumption, their high variability and widely spread ranges in nondrinking subjects limit the usefulness of these markers in general population settings.

Adult↗
PubMed2000

Twenty years of experience with the steroid receptor external quality assessment program - the paradigm for tumour biomarker EQA studies. On behalf of the EROTC Receptor and Biomarker Study Group.

Estrogen receptor (ER) assays have clinical relevance in selecting women who would benefit from endocrine intervention. As the degree of benefit from endocrine therapy is directly related to the quantity of receptor present in the tumour, the quality of the steroid receptor assays is important. Moreover, since patients entered in multi-centre trials often include stratification based on the receptor status, receptor assays should be comparable between different institutes. ER- and progesterone receptor (PgR)-assays have been evaluated in quality assessment studies for almost 20 years by the EORTC Receptor and Biomarker Study Group. This study analyses our findings over these years and concludes with a recommended minimum structure on which QA schemes for any biomarker could be based.

Biomarkers, Tumor↗
PubMed2006

Current status of risk stratification methods in acute coronary syndromes.

Distinguishing which patients with chest pain are at high risk versus which are at low risk remains an important clinical problem despite modern risk stratification strategies. Current approaches often over-utilize hospital resources, yet still miss a significant number of true acute coronary syndromes (ACS). This review focuses on important developments in risk stratification in ACS from 2004 through 2005. Risk models have been developed that use readily available patient characteristics, and head to head comparisons of the various models have been performed to guide clinicians in selecting between the different options. The most powerful models now include measurement of renal function, which has emerged as an important marker of risk. In addition to cardiac troponins, B-type natriuretic peptide (BNP) clearly augments risk prediction, and in the past year serial BNP measurement after discharge has shown promise as a simple way to monitor patient risk following ACS. Newer biomarkers are on the horizon but have not yet established their clinical value. Finally, advances in coronary CT angiography and bedside echocardiography offer hope that noninvasive imaging may play a more important role in early risk stratification in the near future.

Albumins↗
PubMed2005

Monitoring of minimal residual disease in acute myeloid leukemia.

Monitoring minimal residual disease (MRD) becomes increasingly important in the risk-adapted management of patients with acute myeloid leukemia (AML). The two most sensitive and quantitative methods for MRD detection are multiparameter flow cytometry (MFC) and real-time polymerase chain reaction (QRT-PCR). Fusion gene-specific PCR in AML is based on the RNA level, and thus in contrast to MFC expression levels rather than cell counts are assessed. For both methods independent prognostic values have been shown. The strong power of MFC has been shown mainly in the assessment of early clearance of the malignant clone. MRD levels in AML with fusion genes have the strongest prognostic power after the end of consolidation therapy. In addition, with QRT-PCR highly predictive initial expression levels can be assessed. With both methods early detection of relapse is possible. So far, validated PCR-based MRD was done with fusion genes that are detectable in only 20-25% of all AML MFC is superior since it is applicable for most AML. However, QRT-PCR is still more sensitive in most cases. Thus, it is desirable to establish new molecular markers for PCR-based studies. Large clinical trials will determine the role and place of immunologic and PCR-based monitoring in the prognostic stratification of patients with AML.

Biomarkers, Tumor↗
PubMed2006

Risk stratification in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) follows an extremely variable clinical course with survival ranging from months to decades. Available treatments can often induce remissions, but eventually all patients relapse. Recently, there has been major progress in the identification of molecular and cellular markers that may predict the tendency for disease progression in patients with CLL. Genomic aberrations, the mutational profile of IgVH genes and its surrogate marker ZAP-70 expression, and serum markers like B2-microglobulin (beta2-MG) and thymidine kinase (TK) provide prognostic information for individual patients independently of clinical disease characteristics. These molecular markers are about to enter the stage of risk stratification for individual patients in clinical trials.

Biomarkers, Tumor↗
PubMed1998

Serum neopterin, beta2-microglobulin, soluble interleukin-2 receptors, and immunoglobulin levels in healthy adolescents.

Serum biomarkers, such as neopterin, beta2-microglobulin (B2M), and soluble interleukin-2 receptors (sIL-2R), are elevated in viral infections, including HIV-1 infection, and in inflammatory conditions, autoimmune disease, and malignancies. For many of these conditions, serum levels correlate with disease activity. Application of these biomarkers in adolescents is limited by a lack of information on the range and determinants of variability (age, sex, race) for serum levels of these important molecules in this age group. To address this question, we analyzed serum samples from a well-characterized heterogeneous population of 111 healthy adolescents. White children had significantly higher serum levels of sIL-2R and IgM and lower levels of IgG (P </= 0.001) than black children. Boys had higher sIL-2R and B2M levels (P < 0.005) and lower IgM levels (P < 0.05) than girls. No significant age effect on B2M or neopterin level was observed over the age range of 12-19 years included in this analysis. However, stratification by race showed that serum sIL-2R level was significantly associated with age among whites, but not among blacks. Values of these biomarkers in this population are compared with age-stratified values in the previously analyzed 20- to 69-year-old population from whose households the adolescent subjects were recruited.

Adolescent↗
PubMed2026

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗
PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗
PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗
PubMed2026

Spindle Assembly Checkpoint Competency Determines Sensitivity to KIF18A Inhibition in Small-Cell Lung Cancer.

BACKGROUND: Small-cell lung cancer (SCLC) is characterized by pervasive chromosomal instability (CIN) and remains largely refractory to targeted therapies. KIF18A, a motor protein that regulates chromosome alignment during mitosis, has emerged as a selective dependency in CIN-high tumors. Whether this dependency extends to SCLC, a prototypical CIN-high cancer, has not been established, and biomarkers predicting response to KIF18A inhibition, currently in clinical trials, are lacking. METHODS: We integrated analyses of patient tumor datasets, neuroendocrine (NE) and non- NE SCLC cell lines, and functional perturbation models to define the determinants of response to KIF18A inhibition. Chromosomal instability metrics, transcriptional programs, mitotic dynamics, and spindle assembly checkpoint (SAC) function were assessed using genomic profiling, live-cell imaging, genetic perturbation, and pharmacologic inhibition. RESULTS: KIF18A expression was elevated in SCLC tumors and correlated with CIN-associated transcriptional programs, proliferative markers, and NE status; however, these features did not predict sensitivity to KIF18A inhibition. Instead, response was determined by the functional integrity of the SAC. SAC-proficient SCLC cells underwent sustained mitotic arrest followed by apoptotic cell death upon KIF18A inhibition, whereas SAC-defective cells failed to maintain checkpoint activation and survived. Mechanistically, resistant cells exhibited impaired kinetochore recruitment of core SAC components, including MAD1 and BUBR1. Importantly, transient induction of acute CIN through MPS1 inhibition partially restored sensitivity to KIF18A inhibition in resistant models. CONCLUSIONS: This study provides the first mechanistic characterization of KIF18A dependency in SCLC, identifying SAC competency as the primary determinant of response. These findings establish a biologically informed framework for patient stratification and rational combination strategies. TRANSLATIONAL RELEVANCE: Small-cell lung cancer (SCLC) is an aggressive malignancy with few effective targeted therapies and marked chromosomal instability. KIF18A has emerged as a potential therapeutic target in genomically unstable cancers, but biomarkers predicting response to KIF18A inhibition are lacking. We demonstrate that sensitivity to KIF18A inhibition in SCLC is determined not by KIF18A expression, neuroendocrine subtype, or baseline chromosomal instability, but by the functional integrity of the spindle assembly checkpoint (SAC). SCLC cells with intact SAC signaling undergo sustained mitotic arrest and apoptosis upon KIF18A inhibition, whereas SAC-defective cells bypass checkpoint activation and survive aberrant mitosis. Notably, transient induction of acute chromosomal instability through MPS1 inhibition partially restores sensitivity in resistant models. Together, these findings identify mitotic checkpoint competency as a mechanistic determinant and candidate predictive biomarker for KIF18A-targeted therapies, providing a biologically informed framework for patient stratification and rational combination strategies relevant to ongoing KIF18A inhibitor clinical trials.

Journal Article↗
PubMed2006

New horizons: NT-proBNP for risk stratification of patients with shock in the intensive care unit.

B-type natriuretic peptide (BNP) and amino-terminal pro-BNP (NT-proBNP) are promising cardiac biomarkers that have recently been shown to be of diagnostic value in decompensated heart failure, acute coronary syndromes and other conditions resulting in myocardial stretch and volume overload. In view of the high prevalence of cardiac disorders in the intensive care unit, the experience of elevated natriuretic peptide levels in the critically ill might be of enormous diagnostic and therapeutic value. BNP and NT-proBNP levels rise to different degrees in critical illness and may also serve as markers of severity and prognosis in diseases beyond acute or chronic heart failure. The diagnostic and prognostic use of natriuretic peptides in the intensive care setting for patients with various forms of shock could be an attractive alternative as noninvasive markers of cardiac dysfunction that could obviate the need for pulmonary artery catheterization in some patients.

Biomarkers↗