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PubMed2025

DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.

Leukemia↗
PubMed2026

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the data set is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently used as pseudolabels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methods, such as hierarchical clustering and density-based spatial clustering of applications with noise (DBSCAN), are used, with results visualized through principal component analysis-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underlines the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable method for analyzing complex genomic data sets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.

Humans↗
PubMed2026

Molecular Profiling Across 80,000 Patients With Lung Cancer.

INTRODUCTION: Biomarker testing is an essential component of optimal therapeutic management in NSCLC, enabling the use of both Food and Drug Administration-approved and emerging targeted therapies. Despite well-established biomarker testing guidelines and the availability of many approved targeted therapies, a substantial proportion of patients with advanced NSCLC are not benefiting from precision oncology. In this study, we analyze the distribution of actionable genomic alterations across histologic subtypes and clinicodemographic subgroups of NSCLC using data in 82,328 samples profiled with a single comprehensive genomic profiling assay, aiming to support universal molecular testing across all NSCLC subtypes to ensure equitable access to available therapeutics. METHODS: This is an observational retrospective analysis on histologically confirmed NSCLC cases tested with comprehensive genomic profiling by next-generation sequencing between 2014 and 2022 using Foundation One/Foundation CDx. All cases were centrally reviewed by board-certified anatomic pathologist to determine histologic type and subtype. RESULTS: A total of 82,328 patients with NSCLC were included. An actionable genomic alteration (GA) was found in 35.1% of the cases. Lung adenocarcinoma (LUAD) and adenosquamous carcinoma were more frequently associated with actionable GA (45.8% and 40.9%, respectively) as compared with sarcomatoid (29.1%), not otherwise specified (27.6%), large cell (21.1%), and squamous cell (6.5%) histologies. Sarcomatoid histology had the highest METex14 skipping mutation (mut) frequency (9.95% versus 2.43% in LUAD). Tumor mutation burden more than or equal to 10 mut/Mb was associated with histology (50.91% in large cell, 40.79% in not otherwise specified, 39.08% in squamous cell, and 36.30% in sarcomatoid versus 31.22% in LUAD and 29.22% in adenosquamous carcinoma). Patients with actionable GA had usually a low tumor mutation burden (80.88%). A significant correlation (p < 0.005) between age and actionable GA was reported for BRAF/ERBB2 muts, ALK/RET/ROS1 rearrangements, and MET amplification. EGFR actionable muts and KRAS G12C were more frequently observed in females, whereas no significant correlation between sex and other GA was observed. Finally, genetic ancestry analyses revealed a strong correlation for EGFR actionable muts and South/East Asia and America, but not for other GA. CONCLUSIONS: This is the largest NSCLC data set analyzed for biomarker distribution across histologies, age, sex, and genetic ancestry. This data set confirms sufficient enough biomarker prevalence across many histologic subtypes of NSCLC, providing reassurance that all NSCLC cases should be considered for biomarker workup.

Humans↗
PubMed2026

Cytokines and Inflammatory Gene Polymorphisms Associated With Nosocomial Pulmonary Infection After Spontaneous Intracerebral Hemorrhage.

Nosocomial pulmonary infection is a frequent complication after spontaneous intracerebral hemorrhage and may worsen neurological recovery, prolong hospitalization, and increase clinical burden. This retrospective clinical-laboratory study presents a reproducible workflow for evaluating inflammatory biomarker and host immune-genetic profiles associated with nosocomial pulmonary infection after primary spontaneous intracerebral hemorrhage. Patients are classified according to whether nosocomial pulmonary infection occurs after admission. Peripheral venous blood is collected in the early post-admission period under standardized pre-analytical conditions. Serum is separated, aliquoted, and stored for enzyme-linked immunosorbent assay measurement of IL-1&#x3b2;, IL-6, IL-10, IL-17, IFN-&#x3b3;, TNF-&#x3b1;, TLR2, TLR4, and TLR9. In parallel, genomic DNA is extracted from anticoagulated whole blood and used for polymerase chain reaction-restriction fragment length polymorphism genotyping of selected cytokine- and Toll-like receptor-related loci. The workflow also includes quality-control procedures for sample handling, duplicate ELISA measurements, DNA purity assessment, genotype calling, and repeat genotyping. Statistical analysis includes between-group comparison of clinical characteristics and biomarker levels, Hardy-Weinberg equilibrium testing, logistic regression analysis for genotype and allele associations, adjustment for relevant clinical covariates, and false-discovery-rate correction for multiple genetic comparisons. This combined clinical, inflammatory, and immune-genetic workflow may help characterize infection-risk profiles after spontaneous intracerebral hemorrhage, although prospective multicenter validation is still required before routine clinical application.

Humans↗
PubMed2001

Individual osteoblasts in the developing calvaria express different gene repertoires.

Several studies in vitro and a few in vivo have suggested that mature osteoblasts heterogeneously express osteoblast markers. In one recent study of the osteoblasts associated with bone nodules formed in vitro in rat calvaria cell populations, extensive diversity was documented in the overall gene repertoires expressed. To address whether comparable heterogeneity is evident in vivo, we investigated the expression of nine osteoblast lineage markers by both in situ hybridization and immunohistochemistry. At 21 days of fetal rat development, the calvaria is a rapidly growing bone with distinct maturational zones that are readily observed in coronal sections; that is, an osteogenic front emerging at sagittal and coronal sutures is adjacent to areas of growing trabeculae of bone, followed by more mature areas of remodeling bone. Based on expression patterns, markers can be divided into two categories. One category comprises markers that are globally expressed by all osteoblasts irrespective of their position in the calvaria. Of those tested, only two, alkaline phosphatase and the pth/pthrp receptor, fit into this category. All other markers analyzed, including transcription factors (c-fos and msx-2), matrix molecules (bone sialoprotein, osteopontin, and osteocalcin), and a hormone (pthrp), were differentially expressed only in subpopulations of osteoblasts, based on cell maturational status, environment (ectocranial vs. endocranial surfaces), and microenvironment (adjacent osteoblasts). Preosteoblasts and osteocytes in different regions of the calvaria also expressed different subsets of the lineage markers. Mechanisms responsible for generating differential gene expression profiles appear to be both transcriptional and posttranscriptional. These results indicate that postproliferative, morphologically indistinguishable osteoblasts are not a homogeneous class of cells, but instead are molecularly diverse. The present results also raise the possibility that lineage progression and/or maintenance of the differentiated state may be adaptable in the calvaria.

Animals↗
PubMed2004

Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations.

Serum proteomic profiling, by using surfaced-enhanced laser desorption/ionization-time-of-flight mass spectrometry, is one of the most promising new approaches for cancer diagnostics. Exceptional sensitivities and specificities have been reported for some cancer types such as prostate, ovarian, breast, and bladder cancers. These sensitivities/specificities are far superior to those obtained by using classical cancer biomarkers. In this review, I concentrate more on questions that cast doubt on the results reported and propose experiments to investigate these questions in detail, before the technique is used at the clinic. It is clear that the method needs to be externally and thoroughly validated before clinical implementation is warranted.

Biomarkers, Tumor↗
PubMed2006

Expression biomarkers for clinical efficacy and outcome prediction in cancer.

Progress in cancer treatment has been slow, and the outlook for curing cancer is only marginally different from the situation a decade ago. Paradoxically, although the pharmaceutical industry has stepped up costly discovery research and drug development, approvals are on the decline and pipelines are dwindling. In an effort to reduce the number of drug failures and curtail burgeoning R&D costs, drug companies are exploring the use of biomarkers to evaluate toxicity and efficacy earlier in the development process. Biomarkers hold promise for optimization in dosing, adverse event prediction, efficacy evaluation, lead prioritization, and mechanism-of-action profiling of drug candidates. Furthermore, clinicians can use biomarkers to monitor patient response in clinical trials. In this perspective article, the authors explore the applications of cancer-related expression biomarkers in drug discovery and discuss how this will impact the industry and benefit the patient.

Animals↗
PubMed2004

Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine.

Genome-wide monitoring of gene expression profiles using DNA microarrays provides a unique approach to exploring the biological processes underlying oral diseases and disorders by providing a comprehensive survey of a cell's or tissue's transcriptional mapping. This revolutionary technology allows for the simultaneous assessment of the transcription levels of tens of thousands of genes, and of their relative expression between normal and diseased cells. As microarray data analysis evolves, there is a widespread hope that microarrays will significantly impact our ability to explore the genetic changes associated with disease etiology and development, ultimately leading to the discovery of new biomarkers for disease diagnosis and prognosis prediction as well as new therapeutic tools. The goal of this manuscript is to review 2 of the most commonly used microarray technologies, provide an overview of data analyses involved in a typical microarray experiment, and comment upon the application of microarrays to oral medicine.

Biomarkers↗
PubMed2000

Idiosyncratic reactions: new methods of identifying high-risk patients.

This article describes the mechanisms of idiosyncratic drug reactions (IDRs) and provides an analysis of potential methods for identifying patients at high risk for antiepileptic idiosyncratic drug reactions. IDRs may be caused by toxic metabolites, either directly or indirectly (by way of an immunologic response or a free radical-mediated process). Four methods to potentially identify patients at high risk for AED IDRs are discussed: development of an "at-risk" clinical profile for a particular AED: identification of biomarkers that measure the formation of a toxic metabolite by a previously unrecognized bioactivation pathway for a particular AED; identification of biomarkers indicating deficient detoxification abilities [e.g., deficient free radical scavenging enzyme activities or low calculated oxidative protection (COP) ratios 1 and 2]; and identification of at-risk genetic markers. Clinical profiles for patients receiving valproic acid (VPA), felbamate (FBM), and lamotrigine (LTG) and who are at risk for development of AED IDRs are presented. Patients with VPA IDRs have deficient erythrocyte glutathione peroxidase activity, low plasma selenium concentrations, low COP1 ratios, and low COP2 ratios compared with age-matched controls. Patients with FBM-associated aplastic anemia have deficient erythrocyte glutathione peroxidase, superoxide dismutase (SOD), and glutathione reductase activities compared with age-matched controls. Use of at-risk clinical profiles (for VPA, FBM, and LTG) and measurement of erythrocyte glutathione peroxidase activity, erythrocyte SOD activity, and calculation of COP1 and COP2 ratios (for VPA and FBM) are inexpensive, simple methods of identifying high-risk patients for IDRs. Research is needed to further characterize the mechanism of IDRs, to investigate the clinical utility of free radical-scavenging enzyme activity measurement and calculation of COP ratios for other AED IDRs, and to develop additional methods of identifying patients at high risk for AED IDRs.

Anticonvulsants↗
PubMed2005

The xylem and phloem transcriptomes from secondary tissues of the Arabidopsis root-hypocotyl.

The growth of secondary xylem and phloem depends on the division of cells in the vascular cambium and results in an increase in the diameter of the root and stem. Very little is known about the genetic mechanisms that control cambial activity and the differentiation of secondary xylem and phloem cell types. To begin to identify new genes required for vascular cell differentiation and function, we performed genome-wide expression profiling of xylem and phloem-cambium isolated from the root-hypocotyl of Arabidopsis (Arabidopsis thaliana). Gene expression in the remaining nonvascular tissue was also profiled. From these transcript profiles, we assembled three sets of genes with expression significantly biased toward xylem, phloem-cambium, or nonvascular tissue. We also assembled three two-tissue sets of genes with expression significantly biased toward xylem/phloem-cambium, xylem/nonvascular, or phloem-cambium/nonvascular tissues. Localizations predicted by transcript profiles were supported by results from promoter-reporter and reverse transcription-polymerase chain reaction experiments with nine xylem- or phloem-cambium-biased genes. An analysis of the members of the phloem-cambium gene set suggested that some genes involved in regulating primary meristems are also regulators of the cambium. Secondary phloem was implicated in the synthesis of auxin, glucosinolates, cytokinin, and gibberellic acid. Transcript profiles also supported the importance of class III HD ZIP and KANADI transcription factors as regulators of radial patterning during secondary growth, and identified several members of the G2-like, NAC, AP2, MADS, and MYB transcription factor families that may play roles as regulators of xylem or phloem cell differentiation and activity.

Arabidopsis↗
PubMed2005

Assessment of soil remediation workers' exposure to polycyclic aromatic hydrocarbons (PAH): biomonitoring of naphthols, phenanthrols, and 1-hydroxypyrene in urine.

Urinalysis of multiple polycyclic aromatic hydrocarbons (PAH) biomarkers has been applied to assess the exposure of soil remediation workers on a former creosote wood impregnation site polluted with creosote oil. The uptake of PAHs was measured in preshift, end-of-shift, evening, and next preshift specimens (n=33) of nine volunteers with diverse tasks, using sensitive HPLC-FD methods. The ranges of biomarker concentrations in urine (nmol/l) were: 1-naphthol (14-159), 2-naphthol (9-166), 1- plus 2-naphthol (35-269), 1-hydroxyphenanthrene (OHPhe) (6-56), 2- plus 3-OHPhe (6-70), 4-OHPhe (1-6), 9-OHPhe (1-7), the sum of phenanthrols (15-135), and 1-hydroxypyrene, OHP (2.2-67). Eight of nine workers had OHP levels higher than the Finnish biological limit value for non-occupationally exposed persons (3nmol/l). A linear correlation was observed between 1- and 2-naphthol (r=0.90). The biomarker OHP correlated well in urine both with the major (1-OHPhe, r=0.96; 2- plus 3-OHPhe, r=0.84) and the minor phenanthrene metabolites (4-OHPhe, r=0.77; 9-OHPhe, r=0.68), and with the sum of all phenanthrols (r=0.94), but not so well with the sum of naphthols (r=0.66, p<0.001). The smokers had 2.9-, 2.2-, and 4.8-fold higher average concentrations of naphthols, phenanthrols, and OHP, respectively, than the non-smokers. The PAH biomarker data (concentrations and diurnal excretion profiles) showed significant work-related exposure in both non-smoking and smoking subjects. The average exposure levels were clearly higher than those we have measured for instance in asphalt paving workers. The workers' exposure should be assessed by biological monitoring, because at this type of outdoor work the dermal and pulmonary uptake of PAHs are both likely. Adequate measures for preventing, particularly, dermal absorption are of crucial importance for reducing the workers' risk of exposure to carcinogens on soil remediation sites.

Biomarkers↗
PubMed2002

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

Cluster Analysis↗
PubMed2000

Human gastric cancer kinase profile and prognostic significance of MKK4 kinase.

Alterations of protein tyrosine kinase are often associated with uncontrolled cell growth and tumor progression. Knowledge of the overall expression pattern of tyrosine kinases should prove beneficial in understanding the signaling pathways involved in gastric cancer oncogenesis and in providing possible biomarkers for gastric cancer progression. To establish a general tyrosine-kinase expression profile, degenerated polymerase chain reaction primers designed from the consensus catalytic kinase motifs were used to amplify protein tyrosine kinase molecules from gastric cancer tissues. We observed more than 50 tyrosine and serine/threonine kinases from matching pairs of gastric cancer tissue and normal mucosa. Based on this new kinase profile information, we selected the MKK4 gene for further immunohistochemical studies. Statistical analysis of MKK4 protein expression and clinicopathological features indicated that MKK4 kinase expression could serve as a significant prognostic factor for relapse-free survival and for overall survival. We demonstrated a simple and sensitive method for establishing protein tyrosine-kinase expression profiles of human gastric cancer tissues as well as for discovering novel and useful clinical biomarkers from such kinase expression profiles.

Aged↗
PubMed2005

Highly consistent patterns for inherited human diseases at the molecular level.

Over 1600 mammalian genes are known to cause an inherited disorder, when subjected to one or more mutations. These disease genes represent a unique resource for the identification and quantification of relationships between phenotypic attributes of a disease and the molecular features of the associated disease genes, including their ascribed annotated functional classes and expression patterns. Such analyses can provide a more global perspective and a deeper understanding of the probable causes underlying human hereditary diseases. In this perspective and critical view of disease genomics, we present a comparative analysis of genes reported to cause inherited diseases in humans in terms of their causative effects on physiology, their genetics and inheritance modes, the functional processes they are involved in and their expression profiles across a wide spectrum of tissues. Our analysis reveals that there are more extensive correlations between these attributes of genetic disease genes than previously appreciated. For instance, the functional pattern of genes causing dominant and recessive diseases is markedly different. Also, the function of the genes and their expression correlate with the type of disease they cause when mutated. The results further indicate that a comparative genomics approach for the analysis of genes linked to human genetic diseases will facilitate the elucidation of the underlying molecular and cellular mechanisms.

Biomarkers↗
PubMed2006

Molecular pathogenesis of primary central nervous system lymphoma.

Primary central nervous system lymphoma (PCNSL) is an aggressive form of non-Hodgkin lymphoma (NHL) typically associated with a worse prognosis than other localized extranodal lymphomas with similar histological characteristics. The defining feature of PCNSL is its confinement to the central nervous system (CNS), with proclivity for growth within the leptomeningeal as well as intraocular compartments. Primary CNS lymphoma rarely disseminates outside the CNS and accounts for less than 5% of all primary brain neoplasms. At least 95% of PCNSLs are of large B-cell histology, the most common subtype of NHL. Consistent with the trend seen in systemic NHLs, the incidence of PCNSL has markedly increased over the past three decades, both in immunocompromised and immunocompetent patients. Because PCNSL is relatively rare, the identification of molecular prognostic biomarkers and the definition of a standard therapeutic strategy have been challenging. The authors discuss the current knowledge of the molecular pathogenesis of CNS lymphomas and review the recent advances in gene expression profile analysis and identification of novel prognostic biomarkers.

Biomarkers, Tumor↗
PubMed2003

Protein profiles associated with survival in lung adenocarcinoma.

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survival-associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P = 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer.

Adenocarcinoma↗
PubMed2006

A serum protein signature with high diagnostic value in bacterial endocarditis: results from a study based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

BACKGROUND: Bacterial endocarditis is a serious disease. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) based on serum protein profiling is a powerful approach that can generate biomarkers with diagnostic value. METHODS: To identify a protein signature associated with bacterial endocarditis, we retrospectively performed SELDI-TOF MS profiling of serum samples from 88 patients hospitalized because of clinical suspicion of endocarditis. The diagnosis was confirmed by conventional criteria for 34 patients (endocarditis positive) and was excluded for 54 patients (endocarditis negative). Serum samples were incubated with cation-exchange ProteinChip arrays. The protein profiles generated were subjected to biostatistical processing. RESULTS: Fifty-nine samples (23 endocarditis positive and 36 endocarditis negative) were randomly selected for a learning set, with the 29 remaining samples (11 endocarditis positive and 18 endocarditis negative) serving as an independent testing (validation) set. Sixty-six protein peaks were differentially expressed between the endocarditis-positive and the endocarditis-negative patients. By combining partial least squares and logistic regression methods, we built a serum protein model that perfectly discriminated between endocarditis-positive and endocarditis-negative patients. Importantly, when this model was tested on the independent testing set, a correct prediction rate of nearly 90% was demonstrated. Overall, sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 98%, 96%, and 96%, respectively. CONCLUSIONS: SELDI-TOF MS profiling revealed a serum signature with high diagnostic potential for endocarditis.

Adolescent↗