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Results for “Biomarker stratification”

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PubMed2006

Combining serum biomarkers: the association of C-reactive protein, insulin sensitivity, and homocysteine with cardiovascular disease history in the general US population.

BACKGROUND: Elevated levels of serum biomarkers such as C-reactive protein (CRP) and homocysteine have been independently associated with cardiovascular risk. However, the prevalence of concurrent elevations of these biomarkers in the general population is unknown, as is their association with cardiovascular disease (CVD). METHODS: Data from adults (n = 4900) in the National Health and Nutrition Examination Survey were used to investigate the relationship between combinations of serum biomarkers of inflammation (CRP), atherosclerosis (homocysteine), and insulin sensitivity [homeostatic model assessment (HOMA) fasting insulin] and CVD. Using SUDAAN, logistic regression models were constructed to examine the relationships between elevated serum biomarkers (CRP, homocysteine, HOMA, or insulin), singly or in combination, and having a history of heart failure, myocardial infarction (MI), stroke, or any CVD, while controlling for age, race, sex, obesity, smoking, cholesterol level, diabetes history, hypertension history, exercise level, and dietary fiber intake. RESULTS: After adjustment for covariates, there was a significant relationship between concomitant elevations of CRP plus homocysteine and a history of MI [odds ratio (OR) 2.21], heart failure (OR 2.14), and any CVD (OR 1.87) that was stronger than the relationship between individual biomarkers alone and a history of CVD. In addition, combinations of elevated CRP plus HOMA and CRP plus insulin, remained significantly related to having a history of any CVD. CONCLUSION: Recent scientific evidence and the present findings demonstrate the possibility for improving cardiovascular risk stratification through the concurrent evaluation of multiple biomarkers. In particular, these findings demonstrate the need to evaluate the combination of CRP and homocysteine prospectively as predictors of CVD.

Adolescent↗

PubMed2005

Prognostic value of combined use of biomarkers of inflammation, endothelial dysfunction, and myocardiopathy in patients with ESRD.

BACKGROUND: Cardiovascular risk stratification is important in the clinical management of patients with end-stage renal diseases (ESRD) and biomarkers are increasingly used in these patients. METHODS: In a cohort of 246 dialysis patients without heart failure at baseline we tested the combined prognostic power of three well-established biomarkers: brain natriuretic peptide (BNP), C-reactive protein (CRP), and asymmetric dimethyl arginine (ADMA). The independent prognostic value of individual and combined biomarkers was estimated in separate Cox models, including standard risk factors in dialysis patients and comorbidities. RESULTS: When the prediction power of the three biomarkers was evaluated individually, BNP, ADMA, and CRP added significant predictive value (P< or = 0.01) to all-cause and cardiovascular mortality models and the explanatory gain attributable to these biomarkers were of similar degree (ranging from 3.3% to 5.7%). When the biomarkers were evaluated jointly, a score based on the BNP-CRP combination, increased by 9.9% (all-cause) and by 10.5% (cardiovascular) the explained mortality variance of standard Cox models and such gain in power was similar to that achieved by the CRP-ADMA combination (all-cause death 9.0% and cardiovascular death 8.4%). Of note, the explanatory gain derived by the simultaneous use of the three biomarkers was very similar (all-cause death 11.6% and cardiovascular death 10.5%) to that achieved by the use of two biomarkers. CONCLUSION: These findings indicate a potential role for CRP, BNP, and ADMA to be incorporated into diagnostic and therapeutic strategies aimed at detection and treatment of atherosclerotic complications and at preventing heart failure in the dialysis population.

Adult↗

PubMed2006

Sentinel node biopsy before neoadjuvant chemotherapy for determining axillary status and treatment prognosis in locally advanced breast cancer.

BACKGROUND: Treatment of locally advanced breast cancer with neoadjuvant chemotherapy assesses an in vivo tumor response while increasing breast conservation. Axillary clearance of nodal disease after treatment defines prognostic stratification. Our study objective was to show that sentinel node staging before treatment can optimize posttreatment prognostic stratification in clinically N0 patients. METHODS: Eighty-nine patients with locally advanced breast cancer were treated with neoadjuvant chemotherapy. Of these, 42 (47%) clinically palpable or image-detected nodes (cN+) were histologically confirmed before treatment (group 1), and 47 (53%) patients without palpable lymph nodes (cN0) had a sentinel lymph node (SLN) biopsy before treatment (group 2). Survival analysis was conducted with the Kaplan-Meier method. RESULTS: In groups 1 and 2, 82 (92%) of 89 patients had node-positive disease before treatment. Seven (8%) of 89 had negative SLNs and no completion axillary lymph node dissection, 24 (27%) patients had a complete pathologic axillary response (pCRAX; 11 [26%] of 42 in group 1 and 13 [33%] of 40 in group 2), and 58 (65%) of 89 had residual disease in the axilla. Breast-conserving therapy was applied to 27 (30%) of 89 patients. The seven SLN-negative patients had no axillary recurrence at 25 months, and pCRAX patients had a significantly higher overall survival than patients with residual disease. CONCLUSIONS: This study validates the prognostic stratification of patients with a complete pathologic axillary response to neoadjuvant chemotherapy. The addition of SLN biopsy to cN0 patients before treatment increased accurate nodal staging by 53%, eliminated completion axillary lymph node dissection in 15%, and demonstrated an improved prognosis in 28% of pCRAX patients. SLN biopsy before treatment provides accurate staging of cN0 patients; allows acquisition of standard treatment markers, prognostic biomarkers, and microarray analysis; and affords prognostic stratification after treatment.

Axilla↗

PubMed2026

Identifying gene expression signatures for risk stratification of postoperative adjuvant chemotherapy in colorectal cancer.

Clinical risk stratification for postoperative recurrence in patients with pathological stage II (pStage II) colorectal cancer (CRC) is essential for guiding the use of postoperative adjuvant chemotherapy (ACT). In this study, we identified novel prognostic gene expression biomarkers in patients with pStage II CRC and developed a new risk stratification framework for ACT decision-making. First, genome-wide biomarker discovery was conducted to identify prognostic gene expression biomarkers associated with recurrence risk in pStage II CRC. This analysis identified 10 differentially expressed genes as potential biomarkers for recurrence. The efficacy of these biomarkers was then tested using 188 clinical surgical specimens obtained from patients with pStage II CRC. A predictive panel was developed using qRT-PCR and used to assess 93 clinical specimens with an area under the curve (AUC) of 0.82, and its performance was further validated in an independent cohort (n = 95). By incorporating key clinicopathological features, a Gene expression-based Prediction of Recurrence in pStage II CRC (GPRSC) signature was developed, which robustly predicted postoperative recurrence (AUC: 0.80). Finally, combining the GPRSC signature, microsatellite instability status, and conventional criteria, we developed a novel risk stratification system for postoperative ACT decision-making in pStage II CRC. Overall, we identified novel gene expression biomarkers and developed a prognostic signature that informs clinical decision-making regarding postoperative ACT in patients with pStage II CRC.

Humans↗

PubMed2001

Identification of chest pain patients appropriate for an emergency department observation unit.

There are no perfect tests or algorithms to exclude ACI. Because acute coronary occlusion often occurs in patients with low-grade coronary stenosis, the diagnostic goal of a chest pain diagnostic protocol is not to identify patients with CAD, but rather to identify patients who may be safely discharged home without the development of complications such as MI, unstable angina, death, shock, or CHF over the next 1 to 6 months. There is an advantage to evaluating patients at the time of their symptoms. Patients who have a small plaque that is ruptured, leading to intracoronary thrombosis and ischemia, will manifest ischemia on diagnostic testing that could missed in routine outpatient testing when their plaque were stable. The diagnosis and risk stratification of acute coronary ischemia in the ED depends on a careful history and interpretation of the ECG. Multiple regression models using readily available data (e.g., history, physical examination, and ECG) provide the best tools for risk stratification. If one is deciding how to select patients for an EDOU chest pain evaluation, diagnostic tools that have previously been tested and validated in this setting are preferable. These include the Multicenter Chest Pain Study derived tools (i.e., Goldman, Lee), the ACI and ACI-TIPI tools, and sestamibi risk stratification tools. This is not to say that other tools may not play a role at individual institutions. It is probably better to select a consistent approach and evaluate its performance, rather than to allow random variation to dictate practice. The future direction probably will involve standardization of the ED chest pain population. This allows outcome and cost-effectiveness comparative research of various strategies for patients with normal or nondiagnostic ECGs and normal biomarkers. Although this approach allows more precise stratification, the risk will never be zero, meaning that there will never be a substitute for good clinical judgment and close follow-up care.

Chest Pain↗

PubMedDate not supplied

Clinical proteomics: from biomarker discovery and cell signaling profiles to individualized personal therapy.

The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized "designer" therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.

Biomarkers↗

PubMed2001

Magnetic resonance imaging and spectroscopic imaging: Improved patient selection and potential for metabolic intermediate endpoints in prostate cancer chemoprevention trials.

In the design of prostate cancer chemoprevention trials there is a clear need for improved patient selection and risk stratification, as well as the use of biomarkers that could provide earlier assessment of therapeutic efficacy. Studies in preprostatectomy patients have indicated that the metabolic information provided by 3-dimensional magnetic resonance spectroscopic imaging (3D-MRSI) combined with the morphologic information provided by magnetic resonance imaging (MRI) can improve the assessment of cancer location and extent within the prostate, extracapsular spread, and cancer aggressiveness. Additionally, pre- and posttherapy studies have demonstrated the potential of MRI/3D-MRSI to provide a direct measure of the presence and spatial extent of prostate cancer after therapy, a measure of the time course of response, and information concerning the mechanism of therapeutic response. These studies suggest that the addition of MRI/3D-MRSI data to prostate-specific antigen and biopsy data may improve patient selection and risk stratification for chemoprevention trials, improve tissue sampling for ex vivo molecular marker analysis, and provide shorter-term endpoints in chemoprevention trials. However, future studies are necessary to establish the ability of MRI/3D-MRSI to accurately assess patients with premalignant or very early malignant changes, to validate metabolic markers as intermediate endpoints in chemoprevention trials, and to correlate metabolic endpoints with other promising intermediate biomarkers.

Citric Acid↗

PubMed2004

Heart disease, clinical proteomics and mass spectrometry.

Heart disease is the leading cause of mortality and morbidity in the world. As such, biomarkers are needed for the diagnosis, prognosis, therapeutic monitoring and risk stratification of acute injury (acute myocardial infarction (AMI)) and chronic disease (heart failure). The procedure for biomarker development involves the discovery, validation, and translation into clinical practice of a panel of candidate proteins to monitor risk of heart disease. Two types of biomarkers are possible; heart-specific and cardiovascular pulmonary system monitoring markers. Here we review the use of MS in the process of cardiac biomarker discovery and validation by proteomic analysis of cardiac myocytes/tissue or serum/plasma. An example of the use of MS in biomarker discovery is given in which the albumin binding protein sub-proteome was examined using MALDI-TOF MS/MS. Additionally, an example of MS in protein validation is given using affinity surface enhanced laser desorption ionization (SELDI) to monitor the disease-induced post-translational modification and the ternary status of myocyte-originating protein, cardiac troponin I in serum.

Albumins↗

PubMed2026

CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.

BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.

CCNA2↗

PubMed2026

Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.

Cardiovascular-kidney-metabolic (CKM) syndrome is an emerging clinical entity that highlights the complex, bidirectional interplay among cardiovascular disease, chronic kidney disease, and metabolic disorders, representing a substantial and growing global health burden. This conceptualization marks a paradigm shift from viewing these conditions in isolation to understanding them as an interconnected disease continuum. Traditional biomarkers face significant limitations in the early detection, risk stratification, and precise management of CKM, necessitating a transition towards an integrated framework that captures its multisystem nature. This review systematically outlines an emerging multidimensional biomarker system encompassing key pathological axes such as metabolism, immuno-inflammation, oxidative stress, and biological aging, offering refined risk assessment beyond conventional metrics. The development of this system is propelled by revolutionary platforms, including accessible sampling techniques (e.g., dried blood spots), advanced in vitro models (e.g., multi-organ-on-a-chip), and multi-omics technologies. These platforms not only facilitate a deeper dissection of the heterogeneous origins and inter-organ crosstalk in CKM but also accelerate the discovery and validation of novel biomarkers. Concurrently, artificial intelligence serves as a pivotal tool for clinical translation, effectively integrating high-dimensional data to transform complex molecular profiles into actionable clinical insights. By enabling the construction of dynamic risk prediction and decision-support systems, this review charts a pathway toward proactive, individualized, and precise prevention and management of CKM syndrome.

Humans↗

PubMed2006

Increased ventricular ectopic activity in relation to C-reactive protein, and NT-pro-brain natriuretic peptide in subjects with no apparent heart disease.

BACKGROUND: Subjects with frequent ventricular premature complexes (VPC) and no apparent heart disease make a heterogenic group with regard to prognosis. Some biomarkers have recently proved useful in risk stratification in different heart diseases. We examined prognostic impact of NT-Pro-brain natriuretic peptide (NT-Pro BNP), and C-reactive protein (CRP) in relation to frequent VPC in subjects with no apparent heart disease. METHODS: Six hundred seventy-eight healthy subjects between 55 and 75 years of age with no history of cardiovascular disease were included in the study. All were tested with fasting laboratory testing and 48-hour ambulatory ECG monitoring. Frequent VPC was defined as VPC > or =30/hour. RESULTS: In 56 subjects (8%) with frequent VPC the prognosis was much poorer compared to those without frequent VPC (Hazard ratio and 95% CI: 2.3;1.2-4.4, P = 0.01), after adjustment for conventional risk factors. In subjects with frequent VPC increased levels of CRP (above 2.5 microg/mL) was the only factor among the tested biomarkers, which was associated with a poor prognosis. Taking subjects without frequent VPC as reference, the hazard ratio and 95% CI for subjects with frequent VPC and increased CRP was 3.6;1.8-7.1, P = 0.0004, and for those with frequent VPC and normal CRP 0.8;0.2-3.5, P = 0.83, after correction for conventional risk factors. CONCLUSIONS: Among middle-aged and elderly subjects with no apparent heart disease and frequent VPCs, a CRP value > or =2.5 microg/mL is associated with a significantly higher risk of death and acute myocardial infarction. These subjects deserve primary prevention measures and further work up for structural heart disease.

Aged↗

PubMed2004

In vitro chemoresistance and biomarker profiles are unique for histologic subtypes of epithelial ovarian cancer.

OBJECTIVES: To determine whether there is a relationship between histologic subtype of epithelial ovarian cancer and chemoresistance, we evaluated ovarian carcinomas of six histologic subtypes and correlated histology with in vitro drug response. Biomarker profiles (p53, Her-2 neu, and EGFR) were also evaluated to determine if their expression patterns were associated with histology. METHODS: In vitro drug response profiles for different histologic subsets of epithelial ovarian carcinomas exposed to standard relevant chemotherapy agents were determined in the Extreme Drug Resistance assay (EDR). Immunohistochemistry techniques were employed to determine biomarker expression. RESULTS: Of 5195 referred serial cases of epithelial ovarian cancer, there were 2660 papillary serous, 303 endometrioid, 142 mucinous, 102 clear cell, 952 undifferentiated carcinomas, and 42 tumors of low malignant potential. For the samples as a whole, the incidences of extreme drug resistance to the tested chemotherapeutic agents were cisplatin 10%, carboplatin 16%, cyclophosphamide 16%, doxorubicin 40%, gemcitabine 21%, paclitaxel 22%, and topotecan 13%. When compared to papillary serous tumors, mucinous tumors were more frequently resistant to cisplatin (10% vs. 18%) but less frequently resistant to topotecan (13% vs. 5%) and doxorubicin (42% vs. 16%). Endometrioid tumors were less resistant to cisplatin (10% vs. 6%) and doxorubicin (42% vs. 20%). Clear cell and undifferentiated tumors had the lowest rates of EDR to paclitaxel (13% and 18%) and cyclophosphamide (7% and 11%), while borderline tumors showed high rates of EDR to these agents (52% and 63%, respectively). With respect to biomarker profiles, mP53 was detected in 46%, Her-2 neu in 16%, and EGFR in 30% of the cases evaluated. As compared to all other subtypes, clear cell carcinomas had significantly higher Her-2 neu expression (19%). Relative to papillary serous carcinomas, borderline tumors exhibited significantly lower rates of mP53 expression (60% vs.17%). CONCLUSIONS: We found significant differences in the frequencies of extreme drug resistance to chemotherapeutic agents and biomarker expression among histologic subtypes of epithelial ovarian cancer. The data collected in this investigation may provide a guide for stratification of patients entering clinical trials based on histology and biomarker expression.

Biomarkers, Tumor↗

PubMed2026

Synaptic vesicle glycoprotein 2A PET imaging in parkinsonian α-synucleinopathies: a systematic review.

Synaptic dysfunction is increasingly recognized as an early and biologically relevant component of α-synucleinopathies. However, conventional imaging biomarkers mainly assess dopaminergic dysfunction, glucose metabolism, or structural damage rather than presynaptic density itself. Synaptic vesicle glycoprotein 2A (SV2A) PET enables in vivo assessment of presynaptic terminal integrity and may provide complementary information in Parkinson's disease (PD), Parkinson's disease dementia/dementia with Lewy bodies (PDD/DLB), and multiple system atrophy (MSA). This systematic review synthesized the available evidence on SV2A-targeted PET in parkinsonian α-synucleinopathies, focusing on regional imaging patterns, clinical associations, longitudinal findings, and methodological determinants of interpretation. Seventeen reports were included. In PD, the most recurrent finding was reduced SV2A binding in the substantia nigra, although additional involvement of brainstem, caudate, striatal, thalamic, raphe, or cortical regions was reported in selected cohorts. In PDD/DLB, abnormalities appeared broader and more cortical, with evidence of association between cortical SV2A binding and cognitive performance. In MSA, one study suggested a distinct infratentorial and cerebellar pattern with potential relevance for phenotypic stratification. SV2A PET is a promising research biomarker for biological characterization of synucleinopathies. However, the field remains limited by small cohorts, methodological heterogeneity, variable quantification strategies, limited longitudinal evidence, and potential cohort overlap. Multicentre validation and harmonized protocols are required before clinical translation.

Humans↗

PubMed2007

The applications of biomarkers in early clinical drug development to improve decision-making processes.

Selecting and evaluating biomarkers in drug discovery and early drug development can substantially shorten clinical development time or the time to reach a critical decision point in exploratory drug development. Critical decisions such as candidate selection, early proof of concept/principle, dose ranging, development risks, and patient stratification are based on the appropriate measurements of biomarkers that are biologically and/or clinically validated. The use of biomarkers helps to streamline clinical development by determining whether the drug is reaching and affecting the molecular target in humans, delivering findings that are comparable to preclinical data, and by providing a measurable endpoint that predicts desired or undesired clinical effects and will increase the success rate in the confirmatory stage of clinical development. Appropriateness of biomarkers depends on the stage of development, development strategy, and the nature of the medical indication. Even if a biomarker fails in the validation process there may be still a benefit of having used it. More knowledge about pathophysiology of the disease and the drug has been obtained. Different levels of validation exist at different development phases. Biomarkers are perhaps most useful in the early phase of clinical development when measurement of clinical endpoints may be too time-consuming or cumbersome to provide timely proof of concept or dose-ranging information. Examples of biomarkers are illustrated for the development of new drugs in variant cardiovascular, pulmonary, and CNS diseases.

Animals↗

PubMed2026

Protein Profiling Identifies Biomarkers for Predicting Disease Severity in Anti-NMDAR Encephalitis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune neurological disorder characterized by pathogenic antibodies against the NMDAR. A systematic protein profiling approach is warranted to identify biomarkers capable of predicting disease status. An Olink proximity extension assay (PEA) profiled 91 inflammation-related proteins from anti-NMDAR encephalitis patients. Disease severity or prognosis were assessed by CASE score or mRS score at 6-month follow-up. Patients were stratified into distinct molecular clusters using unsupervised clustering. Logistic regression models incorporating selected biomarkers were developed to predict disease severity and prognosis, followed by absolute quantification using ELISA. Patients were classified into four consensus clusters. Clusters 1 and 2 corresponded to the mild group, while Cluster 3 represented the severe group, consistent with CASE score above 6. Cluster 4 showed heterogeneous clinical features. Elevated serum levels of IL-10, IL-6, and SIRT2, as well as increased CSF levels of CXCL10, CXCL11, and MMP10, were positively associated with severe disease. Conversely, several proteins including LTA and CCL11, CCL8, TGFB1, CXCL6 were associated with severe disease or unfavorable 6-month outcomes. A logistic regression model combining serum CXCL6 and CCL11 with CSF MMP10 achieved an area under the curve (AUC) of 0.95 for predicting disease severity. Serum CCL11 alone showed predictive value for 6-month prognosis, with an AUC of 0.79. These findings delineate distinct protein signatures associated with clinical heterogeneity of anti-NMDAR encephalitis. Prediction models incorporating multiple biomarkers may provide an approach for disease severity stratification and prognosis forecast.

Humans↗

PubMed2026

Unveiling tumor heterogeneity by single cell RNA-sequencing: From basic considerations to clinical applications.

Tumor heterogeneity-encompassing diverse cellular phenotypes, genomic alterations, and microenvironmental contexts-is a principal barrier to effective cancer therapy. Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve this complexity by capturing transcriptomes at single-cell resolution. Here, we review the technical foundations required for high-quality scRNA-seq studies. We then trace the evolution of scRNA-seq platforms from manual micromanipulation to high-throughput systems, and describe the computational pipelines that enable reliable data interpretation. The application of scRNA-seq is exemplarily shown in the context of lung cancer, where single-cell profiling has revealed (i) the clonal and sub-clonal architecture of tumors, (ii) extensive remodeling of the immune microenvironment, iii) key mechanisms underlying resistance to targeted agents and immune-checkpoint blockade, and (iv) the dynamics of neo-antigen-specific T-cell responses. Integrating machine-learning techniques-such as deep-learning classifiers and graph-based models-with single-cell transcriptomic data has markedly sped up biomarker discovery, produced more accurate risk-stratification scores, and enabled the generation of patient-specific therapeutic predictions. We surveyed the major trial registry ClinicalTrials.gov and identified ∼380 ongoing or completed studies that explicitly incorporate scRNA-seq as a correlative or pharmacodynamic endpoint. Overall, the analysis shows that scRNA-seq becomes an increasingly important component of modern trials, providing high-resolution cellular and molecular readouts that complement conventional imaging and bulk-omics endpoints. While key challenges remain, ranging from costs, scalability and need for rigorous validation before routine clinical deployment, ongoing technological advances continue to expand the potential of scRNA-seq as a cornerstone of precision medicine.

Humans↗

PubMed2026

Integrating machine learning and GWAS for variant prioritization in the INCIPE cohort highlights ABC transporter genes in chronic kidney disease.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health challenge, affecting approximately 674 million people worldwide and representing one of the fastest-growing causes of mortality. Since CKD is frequently asymptomatic in its early stages, the identification of novel genetic biomarkers may improve early detection and risk stratification. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with CKD and related traits; however, their performance is often limited in small and imbalanced cohorts, where reduced statistical power increases both false-positive and false-negative findings. Machine learning (ML) approaches can complement conventional GWAS by prioritizing biologically relevant genetic signals from high-dimensional genomic data. METHODS: In this study, we implemented a nested ensemble (NCBC) model composed of an undersampler and a CatBoostClassifier (CBC) to prioritize candidate genetic variants associated with CKD in the INCIPE cohort. Prioritized variants were functionally annotated and evaluated through enrichment analyses, GTEx gene expression profiling, and protein-protein interaction network analyses. Genes identified by the CKDGen Consortium were analysed as an external reference set and used to validate the biological relevance of the prioritized results. RESULTS: The NCBC model outperformed conventional ML classifiers, achieving a ROC AUC score of 87.77%, compared to 50%-53% for the other evaluated models. Among the prioritized genes, 56.25% showed protein-protein interactions with genes previously reported by the CKDGen Consortium, whereas only 1.9% of randomly generated gene sets showed interactions. DISCUSSION: Our study demonstrates that the NCBC model improves the prioritization of biologically plausible candidate variants in a small and imbalanced CKD cohort. Functional analyses suggested ABC transporter-related genes, including ABCA13, ABCA4, and ABCC4 genes, as promising candidate for future validation, with ABCA4 showing substantial expression in kidney tissues. Overall, these findings support the integration of ML with GWAS to prioritize candidate genes and investigate the genetic architecture of complex diseases.

SNP prioritization↗

PubMed2006

Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397].

INTRODUCTION: Pro-adrenomedullin (proADM) is helpful for individual risk assessment and outcome prediction in sepsis. A major cause of sepsis is community-acquired pneumonia (CAP). The aim of this study was to investigate the value of proADM levels for severity assessment and outcome prediction in CAP. METHODS: Data from 302 patients admitted to the emergency department with CAP were included in a prospective observational study. Procalcitonin, C-reactive protein levels, leukocyte count, clinical variables and the pneumonia severity index (PSI) were measured. ProADM levels were measured with a new sandwich immunoassay for mid regional ProADM (MR-proADM, Brahms AG, Hennigsdorf/Berlin, Germany). RESULTS: ProADM levels, in contrast to C-reactive protein and leukocyte count, increased with increasing severity of CAP, classified according to the PSI score (ANOVA, p < 0.001). In patients who died during follow-up, proADM levels on admission were significantly higher compared to levels in survivors (2.1 (1.5 to 3.0) versus 1.0 (0.6 to 1.6) nmol/l, p < 0.001). In a receiver operating characteristic (ROC) analysis for survival, the area under the ROC curve (AUC) for proADM was 0.76 (95% confidence interval (CI) 0.71-0.81), which was significantly higher compared to procalcitonin (p = 0.004), C-reactive protein (p < 0.001) and total leukocyte count (p = 0.001) and similar to the AUC of the PSI (0.73, p = 0.54). A clinical model including the PSI and proADM increased the prognostic accuracy to predict failure compared to a model relying on the PSI alone (AUC, 0.77 (0.70 to 0.84), p = 0.03). CONCLUSION: ProADM, as a novel biomarker, is a useful tool for the risk stratification of patients with CAP.

Adrenomedullin↗