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PubMed2026

Transcriptomic profiling across stages of non-muscle-invasive bladder cancer identifies fibroblast activation protein-alpha as a stromal biomarker associated with progression.

BACKGROUND: T1 non-muscle-invasive bladder cancer (NMIBC) represents a biologically aggressive subgroup with substantial heterogeneity in recurrence and progression risk. Current clinicopathological risk stratification tools lack sufficient precision to identify patients at the highest risk of progression to muscle-invasive bladder cancer (MIBC). OBJECTIVE: To characterize transcriptomic differences between T1 and&#x2009;<&#x2009;T1 (Ta/Tis) NMIBC and to explore the association of fibroblast activation protein-&#x3b1; (FAP) gene expression with disease progression. METHODS: Transcriptomic profiling was performed on formalin-fixed paraffin-embedded (FFPE) tumor tissue from 66 patients with primary, treatment-na&#xef;ve NMIBC and 5 patients with T2 disease (included for exploratory comparisons). Analyses included differential gene expression, gene set enrichment analysis (GSEA), molecular subtyping, immune cell deconvolution, and evaluation of FAP expression in relation to recurrence and progression. External validation of FAP was conducted in three independent NMIBC cohorts. RESULTS: T1 tumors demonstrated a distinct transcriptomic profile compared with&#x2009;<&#x2009;T1 tumors, characterized by enrichment of cell cycle-related and metabolic pathways and a higher prevalence of aggressive molecular subtypes. Despite these molecular differences, no statistically significant differences in recurrence-free, progression-free, cancer-specific, and overall survival were observed, likely reflecting limited event numbers. Among recurrent tumors, early recurrences (&#x2264;&#x2009;24&#xa0;months) were associated with epithelial-mesenchymal transition signatures. FAP expression increased with tumor stage (p&#x2009;=&#x2009;0.0005) and was associated with progression (p&#x2009;=&#x2009;0.002) and mortality (p&#x2009;=&#x2009;0.01). Patients with tumors in the highest quartile of FAP expression had worse progression-free survival. This association was consistently observed in three external NMIBC cohorts. CONCLUSIONS: T1 NMIBC exhibits distinct transcriptomic features suggestive of increased biological aggressiveness. Elevated FAP expression is reproducibly associated with progression risk across multiple cohorts, supporting its potential role as a biomarker of aggressive disease. Given the limited number of progression events, these findings should be considered hypothesis-generating and warrant prospective validation before clinical implementation.

Humans↗
PubMed2006

The molecular diagnosis of hepatitis B virus-associated hepatocellular carcinoma.

Hepatitis B virus (HBV) infection is the major cause of hepatocellular carcinoma (HCC) worldwide. The pathogenesis of HBV-associated HCC has been studied extensively, and molecular changes during malignant transformation have been identified. It has been proposed that the insertion of HBV DNA into the human genome results in chromosomal instability and inactivation of tumor suppressor genes. Transactivation of oncogenes, inactivation of tumor suppressor genes, and alteration of the cell cycle by HBV proteins are also involved in the progression of hepatocellular carcinogenesis. Traditional clinical examinations of HCC, such as biopsy, computer tomography, ultrasonic imaging, and detection of such biomarkers as a-fetoprotein, are currently the "gold standard" in diagnosis. These tests diagnose HCC only in the late stages of disease. This limitation has greatly reduced the chance of survival of HCC patients. To resolve this problem, new biomarkers that can diagnose HCC in earlier stages are necessary. Based on recent molecular studies of the effects of HBV on cellular transformation, differentially expressed biomarkers of HBV infection have been elucidated. With the analyses of the HBV replication profile, the viral load (HBV DNA levels) of patients, and the viral protein expression, the severity of hepatitis in the preneoplastic stages can be assessed. In the future, with the molecular profiles identified by genomic and proteomic approaches, stage-specific biomarkers should be identified to monitor the progression and prognosis of HCC.

Animals↗
PubMed2006

Peptide profiling of cerebrospinal fluid by mass spectrometry.

The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2-4 microg/l; and CA125: 20-35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.

Biomarkers↗
PubMed2003

Gene expression profiles in human autoimmune disease.

To acquire a functional view of human autoimmunity, we compared differences in gene expression (>4000 genes) in the peripheral blood mononuclear cells of normal individuals following immunization to those in individuals with four different autoimmune diseases (rheumatoid arthritis, systemic lupus erythematosus, insulin dependent diabetes mellitus, and multiple sclerosis). Each individual from all disease groups displayed a similar pattern of gene expression that was highly distinct from the gene expression pattern of the immunized group. These findings indicate that the expression pattern accompanying autoimmunity is not simply a recapitulation of the immune response to non-self. Of note, expression levels of genes that encode key proteins in several distinct apoptosis pathways were markedly reduced in all autoimmune disease groups. Taken together, these data indicate that the pattern of gene expression describes a molecular portrait of autoimmunity that is constant among individuals with autoimmune disease but is independent of the specific autoimmune disease and the clinical parameters associated with any individual autoimmune disease.

Animals↗
PubMed2004

Gene expression profiling identifies platelet-derived growth factor as a diagnostic molecular marker for papillary thyroid carcinoma.

PURPOSE: Cancer diagnostics and therapeutics are often based on clinically relevant markers that are expressed specifically in a malignant tissue at levels higher than in normal tissue. We examined potential markers for papillary thyroid carcinoma (PTC) by monitoring PTC-specific gene expression using cDNA microarray. EXPERIMENTAL DESIGN: Gene expression profiles for PTC tissue, normal thyroid tissue, and healthy peripheral blood cells were compared by use of a human 4000-gene cDNA microarray. Protein expressions of the up-regulated genes in PTC were examined in thyroid tissues by immunohistochemistry. RESULTS: Sixty-four genes were overexpressed in PTC tissue relative to normal thyroid tissue and healthy peripheral blood cells. The genes that were up-regulated in PTC were involved in cell cycle regulation, DNA damage response, angiogenesis, and oncogenesis. Among these genes, basic fibroblast growth factor and platelet-derived growth factor were identified by immunochemical methods as proteins that are specifically expressed at high levels in thyroid neoplasms. Basic fibroblast growth factor, which has been identified as a biomarker for PTC, was overexpressed in 54% of PTC cases, 67% of follicular thyroid carcinomas, and 36% of benign thyroid neoplasms. Platelet-derived growth factor was overexpressed in 81% of PTC cases and 100% of follicular carcinomas, but was immunonegative in normal thyroid tissues and benign thyroid neoplasms. CONCLUSIONS: Platelet-derived growth factor may be a potential biomarker for PTC and follicular carcinoma. Expression profile analysis using a microarray followed by immunohistochemical study can be used to facilitate the development of molecular biomarkers for cancer.

Carcinoma, Papillary↗
PubMed2026

Liquid biopsy for biliary tract cancer: available evidence and future research directions.

INTRODUCTION: Biliary tract cancers (BTCs) are molecularly heterogeneous, and early genomic profiling is becoming increasingly important for treatment decisions. Because tissue sampling is often limited or inadequate, there is a clear need for minimally invasive biomarkers that can support treatment selection and longitudinal disease assessment. AREAS COVERED: This narrative review summarizes current and emerging liquid-biopsy (LB) applications in BTC, with a primary focus on plasma circulating tumor DNA (ctDNA). The evidence base was assembled through targeted searches of PubMed/MEDLINE and Embase up to 1 February 2026, supported by selective ClinicalTrials.gov searches for ongoing biomarker-driven studies. We discuss ctDNA-based molecular profiling for actionable alterations, its prognostic role including minimal residual disease assessment, and its use in serial monitoring of treatment response and acquired resistance. We also consider how LB may support clinical-trial enrichment and biomarker-guided endpoints, and briefly review complementary approaches using bile and other analytes, while highlighting current evidence gaps. EXPERT OPINION: In BTC, ctDNA is best viewed as a complement to tissue-based profiling, especially when tissue is inadequate or when rapid genotyping is needed. Its broader clinical impact will depend on assay standardization, clearer interpretation frameworks for low-shedding disease, and prospective studies showing that ctDNA-guided decisions improve patient outcomes.

Humans↗
PubMed2004

Short-term effects of high soy supplementation on sex hormones, bone markers, and lipid parameters in young female adults.

BACKGROUND: High intake of soy products has been suggested to prevent breast cancer, osteoporosis, and cardiovascular diseases. AIM OF THE STUDY: To investigate the effects of isoflavone-containing soy on circulating sex hormones, biomarkers of bone turnover, and lipoprotein profiles. METHODS: Fourteen young women received in a randomized crossover design 5 soy cookies (52 mg isoflavones) or 5 soy-free cookies (no isoflavones) per day for one menstrual cycle starting one week before menstruation. Serum and urine analyses were performed on day 3 after onset of menstruation (t(1)), 3 days before ovulation (t(2)), 3 days after ovulation (t(3)), during the midluteal phase (t(4)), and again 3 days after onset of the next menstruation (t(5)). RESULTS: With the exception of higher progesterone levels at t(2), soy supplementation did not affect the physiologic fluctuations in circulating sex hormones. The ratio of C-telopeptide (a bone resorption marker) to osteocalcin (a bone formation marker) was slightly higher at t(4) during the soy period compared to t(4) during the control period (P < 0.05), indicating an uncoupling of bone resorption and formation processes. Serum levels of total cholesterol, LDL cholesterol, and HDL cholesterol were not influenced by soy intake. CONCLUSIONS: High short-term isoflavone-containing soy intake slightly affects physiologic fluctuations in bone turnover, but has no significant effects on most circulating sex hormones and on lipoprotein parameters in young healthy women.

Adult↗
PubMed2004

Gene expression profiling predicts clinical outcome of prostate cancer.

One of the major problems in management of prostate cancer is the lack of reliable genetic markers predicting the clinical course of the disease. We analyzed expression profiles of 12,625 transcripts in prostate tumors from patients with distinct clinical outcomes after therapy as well as metastatic human prostate cancer xenografts in nude mice. We identified small clusters of genes discriminating recurrent versus nonrecurrent disease with 90% and 75% accuracy in two independent cohorts of patients. We examined one group of samples (21 tumors) to discover the recurrence predictor genes and then validated the predictive power of these genes in a different set (79 tumors). Kaplan-Meier analysis demonstrated that recurrence predictor signatures are highly informative (P < 0.0001) in stratification of patients into subgroups with distinct relapse-free survival after therapy. A gene expression-based recurrence predictor algorithm was informative in predicting the outcome in patients with early-stage disease, with either high or low preoperative prostate-specific antigen levels and provided additional value to the outcome prediction based on Gleason sum or multiparameter nomogram. Overall, 88% of patients with recurrence of prostate cancer within 1 year after therapy were correctly classified into the poor-prognosis group. The identified algorithm provides additional predictive value over conventional markers of outcome and appears suitable for stratification of prostate cancer patients at the time of diagnosis into subgroups with distinct survival probability after therapy.

Algorithms↗
PubMed2006

Sepsis plasma protein profiling with immunodepletion, three-dimensional liquid chromatography tandem mass spectrometry, and spectrum counting.

Sepsis is a systemic, often fatal inflammatory response whose biochemical pathways are not fully understood and with no single biomarker capable of its reliable prediction. Increased interest in protein profiling to reveal fundamental biochemical events as well as disease diagnosis has grown considerably, largely due to advances in mass spectrometry and related front-end technologies. In this study, patients with sepsis and systemic inflammatory response syndrome (SIRS) were examined using plasma protein profiling following immunodepletion treatment to remove the most abundant proteins, serum albumin, transferrin, haptoglobin, anti-trypsin, IgG, and IgA. These proteins cause significant signal suppression, and their removal allows for lower abundance proteins to be examined through improved ion signal. Analyses after immunodepletion were performed using 3-dimensional reverse phase/strong cation exchange/reverse phase liquid chromatography with electrospray ion trap mass spectrometry (3D LC-MS/MS) and spectrum counting for comparative quantitation. The results revealed a major theme in immune system activity, including activation of the complement and coagulation pathways. Additionally, lipid transport may prove to be important in distinguishing sepsis from SIRS. Specifically, significant multi-fold changes were observed in 10 proteins and are now being investigated for the early diagnosis of sepsis.

Biomarkers↗
PubMed2005

Global profiling of Streptococcus pneumoniae gene expression at different growth temperatures.

Streptococcus pneumoniae is a common commensal of the upper respiratory tract of healthy humans and is an important pathogen in young children, immunocompromised adults, and the elderly. To better understand the strategies employed by this bacterial species in adapting to conditions present at different infection sites in the host, global transcription profiling was used to study gene expression at different growth temperatures: 21, 29, 33, 37, and 40 degrees C. Here, we found that 658 genes (29%) out of 1717 genes were differently expressed (>or=1.5-fold change) in at least one growth temperature relative to 37 degrees C. The percentages of genes whose expression was altered in each growth temperature, respectively, were: 21 degrees C: 53% upward arrow, 47% downward arrow; 29 degrees C: 44% upward arrow, 56% downward arrow; 33 degrees C: 27% upward arrow, 73% downward arrow and 40 degrees C: 44% upward arrow, 56% downward arrow. Hierarchical clustering (HC) of the temperature regulated genes resulted in four clusters, namely A-D of differently expressed genes grouped by bacterial growth temperature. Cluster A represented 81 genes reflecting enhanced expression at 33 degrees C. Cluster B included 260 genes whose expression increased with growth temperature. Cluster C had 28 genes with 68% showing enhanced expression at 29 degrees C while cluster D had 289 genes with 74% genes showing enhanced expression at 21 degrees C relative to 37 degrees C. Principal component (PC) analysis also divided differentially expressed genes into four groups and was highly correlated with HC, suggesting that temperature regulated expression is not random but coordinated. Overall, these results indicated substantial reprogramming of transcription in response to growth temperature. Functional characterization of differential gene expression at different temperatures provides further information on the molecular mechanism(s) that allows S. pneumoniae to adapt to various host environments.

Bacterial Proteins↗
PubMed2006

Protein expression profiling of postmortem brain in schizophrenia.

Surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) enables the sensitive, high-throughput protein profiling of complex biological mixtures. In combination with bioinformatics, this technology has the potential to identify combinations of spectral peaks that can differentiate individuals with a particular disease from normal controls. SELDI-TOF-MS was used to screen postmortem tissue derived from the dorsolateral prefrontal cortex of individuals with schizophrenia (n = 34) and matched controls (n = 35), obtained from the Stanley Foundation Neuropathology Consortium. Tissue samples were homogenized in urea buffer, applied to four different chip arrays which possess different chromatographic surfaces, and analyzed using the Ciphergen ProteinChip Biomarkers System (Model PBS II). Protein expression profiles of the schizophrenia and control groups were compared and analyzed using the Ciphergen Express (CE) and Biomarker Patterns Software (BPS) package. We detected several protein peaks whose intensities differed between the schizophrenia and control groups to a highly significant degree. A combination of these peaks was capable of distinguishing between schizophrenia and controls with a sensitivity and specificity of about 70%. The classification model that distinguished schizophrenia from controls was complex, suggesting that the biochemical abnormalities underlying schizophrenia are heterogeneous. Our results suggest that SELDI-TOF-MS has the potential for distinguishing individuals with schizophrenia from normal controls and may eventually lead to a better understanding of the classification, diagnosis and pathogenesis of this disorder.

Adult↗
PubMed2003

Television viewing and low participation in vigorous recreation are independently associated with obesity and markers of cardiovascular disease risk: EPIC-Norfolk population-based study.

OBJECTIVE: This study describes the associations between sedentary behaviour (television viewing) and participation in vigorous recreational activity with obesity and with biomarkers of cardiovascular disease (CVD) risk profile. DESIGN: Cross-sectional analysis of the EPIC-Norfolk cohort study. SETTING: The study is a population-based study of participants living in Norfolk, UK. SUBJECTS: A total of 15 515 men and women aged between 45 and 74 y, recruited through General Practice lists, who completed the detailed physical activity questionnaire. RESULTS: Following exclusion of those with self-reported myocardial infarction, stroke and diabetes, 14 189 participants remained for the analysis. Self-reported television viewing was positively and participation in vigorous activity negatively associated with markers of obesity, blood pressure and plasma lipids. In multiple regression analysis, adjusting for age, alcohol, smoking, treatment for hypertension, vigorous and total physical activity, these associations remained significant. For women who participated in more than 1 h/week of vigorous activity and who watched fewer than 2 h of television each day, the adjusted mean body mass index was 1.92 kg/m(2) less than for women who reported participating in no vigorous activity and who watched more than 4 h of television each day (P<0.001). The equivalent figure for men was 1.44 kg/m(2) (P<0.001). In a similar analysis, with blood pressure as the outcome, mean diastolic blood pressure difference between the extreme groups of vigorous activity and television viewing was 3.6 mmHg in men (P<0.001) and 2.7 mmHg (P=0.001) in women. CONCLUSIONS: These data suggest that time spent participating in vigorous recreational physical activity and television viewing, an indicator of a sedentary lifestyle, are associated with obesity and markers of CVD disease risk independent of total reported physical activity. Whether these observations represent the true underlying aetiological relations or are a manifestation of the different precision with which the subdimensions of activity are measured remains uncertain.

Aged↗
PubMed2004

New directions of miniaturization within the biomarker research area.

An overview of the current trends within protein expression profiling is given where multidimensional separation of both gel and liquid phase techniques linked to mass spectrometry is viewed as a major route in the global proteome mapping. A clear trend in these biochemical developments is the effort to sequence and identify low-abundant protein expressions where assay miniaturization and integrated sample processing play a central role. Two areas of miniaturization within the proteomics field are addressed: (i) sample cleanup and enrichment, and (ii) silicon microstructure developments for protein chip microarrays.

Animals↗
PubMed2004

Artificial neural network technologies to identify biomarkers for therapeutic intervention.

High-throughput technologies such as DNA/RNA microarrays, mass spectrometry and protein chips are creating unprecedented opportunities to accelerate towards the understanding of living systems and the identification of target genes and pathways for drug development and therapeutic intervention. However, the increasing volumes of data generated by molecular profiling experiments pose formidable challenges to investigate an overwhelming mass of information and turn it into predictive, deployable markers. Advanced biostatistics and machine learning methods from computer science have been applied to analyze and correlate numerical values of profiling intensities to physiological states. This article reviews the application of artificial neural networks, an information-processing tool, to the identification of sets of diagnostic/prognostic biomarkers from high-throughput profiling data.

Animals↗
PubMed2005

The impact of blood contamination on the proteome of cerebrospinal fluid.

Human cerebrospinal fluid (CSF) is in direct contact with the brain extracellular space. Beside the secretion of CSF by the choroid plexus the fluid also derives directly from the brain by the ependymal lining of the ventricular system and the glial membrane and from blood vessels in the arachnoid. Therefore, biochemical change in the brain may be reflected in the CSF. CSF is a potential source of protein molecular indices of central nervous system function and pathology. However, various amounts of blood contamination in CSF may arise during sample acquisition. The concentration of protein in the CSF is only 0.2 to 0.5% that of blood. Minor contamination of CSF with blood during collection of the fluid may dramatically alter the protein profile confounding the identification of potential biomarkers. We have analyzed CSF and CSF spiked with increasing amounts of whole blood using proteomic techniques. We detected at least four blood specific highly abundant proteins: hemoglobin, catalase, peroxiredoxin and carbonic anhydrase I. These proteins can be used as blood contamination markers for proteomic analysis of CSF. Proteins in blood contaminated CSF samples were less stable compared to neat CSF at 37 degrees C suggesting that blood borne protease may induce protein degradation in CSF during sample acquisition. This analysis was aimed at identification of proteins found primarily in CSF, those found primarily in blood and assessment of the impact of blood contamination on those proteins found in both fluids.

Amino Acid Sequence↗
PubMed2026

Metabolic convergence of diabetes and prostate cancer: from dysglycemia to tumor microenvironment reprogramming.

The relationship between diabetes mellitus and prostate cancer (PC) represents one of the most intriguing paradoxes in cancer epidemiology, with diabetic individuals exhibiting a reduced incidence of PC yet poorer prognosis following diagnosis. This apparent contradiction underscores the need for an integrated understanding of how systemic metabolic dysfunction influences prostate carcinogenesis and disease progression. The present review critically synthesizes contemporary epidemiological, mechanistic, and translational evidence to establish metabolic convergence as a unifying framework linking diabetes-associated metabolic abnormalities with PC biology. Current evidence indicates that chronic dysglycemia, hyperinsulinemia, insulin resistance, and endocrine perturbations orchestrate interconnected intracellular signaling networks involving PI3K-AKT-mTOR, AMPK, AGE-RAGE signaling, oxidative stress, mitochondrial dysfunction, and epigenetic reprogramming, collectively driving metabolic adaptation and tumor evolution. Beyond tumor-intrinsic mechanisms, diabetes profoundly remodels the prostate tumor microenvironment through alterations in stromal metabolism, cancer-associated fibroblast activation, adipocyte-tumor crosstalk, extracellular matrix (ECM) remodeling, hypoxic adaptation, and vascular dysfunction, while simultaneously promoting immunometabolic reprogramming characterized by macrophage polarization, T-cell dysfunction, immune checkpoint activation, and immune evasion. The review further examines the bidirectional interactions between antidiabetic therapies and PC treatment, critically evaluating the translational potential of metformin and emerging glucose-lowering agents within the context of precision metabolic therapeutics. Finally, future directions encompassing biomarker-guided patient stratification, longitudinal metabolic profiling, multi-omics integration, artificial intelligence, and clinically relevant mechanistic validation are discussed as essential components of next-generation precision oncology. Collectively, this review reframes diabetes as an active metabolic determinant of PC rather than a coincidental comorbidity and highlights metabolism-centered precision strategies as promising avenues for improving risk stratification, therapeutic decision-making, and clinical outcomes in diabetes-associated PC.

Humans↗
PubMed2005

Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments.

Progress in mapping the genome and developments in array technologies have provided large amounts of information for delineating the roles of genes involved in complex diseases and quantitative traits. Since complex phenotypes are determined by a network of interrelated biological traits typically involving multiple inter-correlated genetic and environmental factors that interact in a hierarchical fashion, microarrays hold tremendous latent information. The analysis of microarray data is, however, still a bottleneck. In this paper, we review the recent advances in statistical analyses for associating phenotypes with molecular events underpinning microarray experiments. Classical statistical procedures to analyze phenotypes in genetics are reviewed first, followed by descriptions of the statistical procedures for linking molecular events to measured gene expression phenotypes (microarray-based gene expression) and observed phenotypes such as diseases status. These statistical procedures include (1) prior analysis, such as data quality controls, and normalization analyses for minimizing the effects of experimental artifacts and random noise; (2) gene selections and differentiation procedures based on inferential statistics for the class comparisons; (3) dynamic temporal patterns analysis through exploratory statistics such as unsupervised clustering and supervised classification and predictions; (4) assessing the reliability of microarray studies using real-time PCR and the reproducibility issues from many studies and multiple platforms. In addition, the post analysis to associate the discovered patterns of gene expression to pathway and functional analysis for selected genes are also considered in order to increase our understanding of interconnected gene processes.

Algorithms↗