[Oxidative stress evaluated by oxidative stress profile (OSP)].
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Biomarkers↗
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Single-cell profiling reveals a novel CAF subpopulation linking stromal heterogeneity to immune suppression in breast cancer subtypes.
BACKGROUND: The tumor microenvironment critically influences breast cancer (BC) progression, immune surveillance, and therapeutic response. Cancer-associated fibroblasts (CAFs), a heterogeneous stromal population, are key regulators of these processes, yet their subtype-specific contributions in BC remain insufficiently defined. METHODS: We integrated three single-cell RNA sequencing datasets from 29 BC patients to characterize stromal populations. Bulk RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to assess correlations between CAF subsets and immune infiltration. Gene signatures were derived to identify subtype-specific CAF-immune interactions, prognostic markers, and potential predictors of chemotherapy response. RESULTS: Three conserved stromal populations (iCAFs, myCAFs, and pericytes) were identified, along with a previously unrecognized subset, the cluster 3 (CL3) CAF-like cells, referred as metabolic stressed CAF (msCAF). msCAF cells displayed transcriptional programs associated with antigen presentation, stress response, glycolysis, and extracellular matrix remodeling. Their abundance was inversely correlated with T-cell infiltration and function, in a subtype-specific manner: triple negative breast cancer (TNBC) was enriched for msCAFs in immune-infiltrated but functionally constrained microenvironments, whereas Luminal A tumors exhibited weaker immune infiltration with heterogeneous CAF-immune associations. msCAFs were characterized by a conserved gene signature (HLA-A, HLA-C, IL32, EMP3) and subtype-specific genes related to T-cell exhaustion. Several genes demonstrated prognostic relevance with distinct patterns in Luminal A (IER3, TIMP1, TBX3, SEC61G) and TNBC (ADM, C4orf3, LDHA) tumors, as well as shared biomarkers (FN1, LOXL2, P4HA1). Multiple msCAF genes also predicted chemotherapy response, suggesting utility as treatment stratification biomarkers. CONCLUSION: msCAFs represent a clinically relevant CAF subset that drives immune suppression, impacts subtype-specific prognosis, and influences therapy response in BC. These findings highlight msCAFs as promising targets for enhancing immunotherapy and personalizing treatment strategies.
Humans↗
Risk potentials for humans of original and remediated PAH-contaminated soils: application of biomarkers of effect.
Contaminated soils represent a potential health risk for the human population. Risk assessment for humans requires specific methods, which must reflect the peculiarities of human behaviour, physiology and biochemistry with respect to contaminant uptake and processing. Biomarkers of effect or exposure have become an appropriate tool. Organic pollutants influence the expression profile of cytochromes P450 (CYP), and CYP1A1 has been shown to be a suitable biomarker for polycyclic aromatic hydrocarbons (PAH). The latter are widely distributed in soils and constitute an important soil contamination. Upon intake of PAH-contaminated soils, CYP1A1 is induced in various organs of rats and minipigs. Increased CYP1A1-levels in lung, kidney and spleen, after oral soil intake, indicate that contaminants escape the primary duodenal and hepatic metabolism and reach further organs. Dose-response relationships reveal that induction effects are to be expected in children based on known exposure conditions. Generally, CYP1A1-induction does not correlate with results of toxicity tests with lower organisms, performed with the same soils. The organic carbon content is largely responsible for this discrepancy. It severely affects the toxicity of soil bound PAH for microorganisms, but obviously affects the mobilization efficiency for PAH in the gastro-intestinal tract of mammals to a minor extent. Soil remediation by different methods may result in a significant reduction of the PAH content and of toxicity. Ingestion of remediated soils by rats shows, however, that the induction potential for CYP1A1 is only slightly decreased after remediation. This means that the major inducing components resist biological remediation or soil washing and remain in the soil. Because data obtained with experimental animals form the guiding principle for in vitro tests to be developed, the suitability of the animal model used for extrapolations to humans has to be proven. Upon soil ingestion, minipigs show a tissue-specific response pattern, which substantially differs from that of rats, which are widely used as animal models. It is not known which response pattern resembles that of man. In summary, cytochromes P450, in particular CYP1A1, are suitable biomarkers to assess the bioavailability of soil bound contaminants and their effects on mammalian species. There are, however, a number of questions to be answered in order to develop an in vitro test for human risk assessment. This concerns, for example, the identification of the suitable animal model, the identification of biomarkers for other contaminants and concepts to transpose the in vivo data to in vitro technologies or to mathematical modelling.
Animals↗
Proteome analysis of human monocytic THP-1 cells primed with oxidized low-density lipoproteins.
Native low-density lipoprotein (LDL) and oxidized LDL (oxLDL) possess a wide variety of biological properties, and play a central role in atherogenesis. In this study, we used a proteomic analysis of human monocyte THP-1 cells induced with oxLDL or with LDL, to identify proteins potentially involved in atherosclerotic processes. Of the 2500 proteins detected, 93 were differentially expressed as a result of priming with LDL or oxLDL. The proteins were unambiguously identified by comparing the masses of their tryptic peptides with those of all known proteins using MALDI-TOF MS and the NCBI database. The largest differences in expression were observed for vimentin (94-fold increase), meningioma-expressed antigen 6 (48-fold increase), serine/threonine protein phosphatase 2A (40-fold increase), and beta-1,3-galactosyltransferase (15-fold increase). In contrast, the abundance of an unnamed protein product and phosphogluconate dehydrogenase decreased 30-fold and 25-fold, respectively. The expression of some selected proteins was confirmed by Western blot and RT-PCR analyses. The proteins identified in this study are attractive candidates for further biomarker research. This description of the altered protein profiles induced by oxLDL in human monocytes will support functional studies of the macrophage-derived foam cells involved in the pathogenesis of atherosclerosis.
Biomarkers↗
Profiling of rat plasma by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, a novel tool for biomarker discovery in nutrition research.
The recent development of high-throughput proteomic technologies has given us new methods to analyze how an organism responds to changes in its nutritional environment. The analysis of plasma samples by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) was investigated as a novel approach to the identification of new biomarkers of nutrient status. Pre-fractionation of rat plasma by anion-exchange chromatography in 96-well filter plates markedly increased the total number of unique peptides and proteins that could be observed in SELDI-TOF mass spectra. Replicate fractionations generated nearly identical pH fractions, not only in terms of peptide and protein composition but also in respect to the ion signal intensity of replicate SELDI-TOF mass spectra. The feasibility of this approach was tested with samples from retinol-sufficient and retinol-deficient rats. The comparative analysis revealed reduced levels of three proteins with molecular masses between 10,000 and 20,000 in plasma of retinol-deficient rats. These results demonstrate that plasma profiling by anion-exchange fractionation and SELDI-TOF-MS may be a promising surveillance tool to detect changes in nutritional status and whole body physiology.
Animals↗
Toxicogenomics in drug development.
Toxicogenomics represents the merging of toxicology with technologies that have been developed, together with bioinformatics, to identify and quantify global gene expression changes. It represents a new paradigm in drug development and risk assessment, which promises to generate a wealth of information towards an increased understanding of the molecular mechanisms that lead to drug toxicity and efficacy, and of DNA polymorphisms responsible for individual susceptibility to toxicity. Gene expression profiling, through the use of DNA microarray and proteomic technologies will aid in establishing links between expression profiles, mode of action and traditional toxic endpoints. Such patterns of gene expression, or 'molecular fingerprints' could be used as diagnostic or predictive markers of exposure, that is characteristic of a specific mechanism of induction of that toxic or efficacious effect. It is anticipated that toxicogenomics will be increasingly integrated into all phases of the drug development process particularly in mechanistic and predictive toxicology, and biomarker discovery. This review provides an overview of the expression profiling technologies applied in toxicogenomics. and discusses the promises as well as the future challenges of applying this discipline to the drug development process.
Animals↗
Dermatoxicokinetic modeling of p-nitrophenol and its conjugation metabolite in swine following topical and intravenous administration.
The development of a dermatotoxicokinetic (dTK) model for p-nitrophenol (PNP), a common metabolite from a variety of compounds and a biomarker of organophosphate (OP) insecticide exposure, may facilitate the kinetic modeling and risk assessment strategy for its parent compounds. In order to quantify and then clarify in vivo-in vitro correlation of PNP disposition, multicompartment kinetic models were formulated. Female weanling pigs were dosed with [14C]PNP intravenously (150 microg in ethanol, n = 4) or topically onto non-occluded abdominal skin (300 microg/7.5cm2 in ethanol, n = 4). PNP and p-nitrophenyl-beta-D-glucuronide (PNP-G) profiles were determined in plasma and urine in addition to total 14C quantitation in many other samples. Disposition parameters (rate constants, Ftop, T12, T1/2Ka, AUC, Vss, Clp, MAT, and MRT) and the simulated chemical mass-time profiles on the dosed skin surface and in the local, systemic, and excretory compartments were also determined. Total recoveries of 97.17 +/- 4.18% and 99.80 +/- 2.41% were obtained from topical and intravenous experiments, respectively. Ninety-six hours after topical and intravenous application, 70.92 +/- 9.72% and 98.65 +/- 2.43% of the dose were excreted via urine, and 0.55 +/- 0.16% and 0.51 +/- 0.10% via the fecal route, respectively. Peak excretion rate and time were also determined. It was suggested by experimental observation and modeling that urinary 14C excretion correlates with the systemic tissue depletion profile well and may be used as a biomarker of PNP exposure. This study also supports the strategy of using urinary PNP as a biomonitoring tool for OP pesticide exposure, although some precautions have to be taken. The strategy used in this study will be useful in comprehensive dTK modeling in dermal risk assessment and transdermal drug delivery.
Administration, Topical↗
Quantitative PET imaging of tumor integrin alphavbeta3 expression with 18F-FRGD2.
UNLABELLED: The development of noninvasive methods to visualize and quantify integrin alpha(v)beta(3) expression in vivo appears to be crucial for the success of antiangiogenic therapy based on integrin antagonism. Precise documentation of integrin receptor levels will allow appropriate selection of patients who will most likely benefit from an antiintegrin treatment regimen. Imaging can also be used to provide an optimal dosage and time course for treatment based on receptor occupancy studies. In addition, imaging integrin expression will be important to evaluate antiintegrin treatment efficacy and to develop new therapeutic drugs with favorable tumor targeting and in vivo kinetics. We labeled the dimeric RGD peptide E[c(RGDyK)](2) with (18)F and evaluated its tumor-targeting efficacy and pharmacokinetics of (18)F-FB-E[c(RGDyK)](2) ((18)F-FRGD2). METHODS: E[c(RGDyK)](2) was labeled with (18)F by conjugation coupling with N-succinimidyl-4-(18)F-fluorobenzoate ((18)F-SFB) under a slightly basic condition. The in vivo metabolic stability of (18)F-FRGD2 was determined. The diagnostic value after injection of (18)F-FRGD2 was evaluated in various xenograft models by dynamic microPET followed by ex vivo quantification of tumor integrin level. RESULTS: Starting with (18)F(-) Kryptofix 2.2.2./K(2)CO(3) solution, the total reaction time for (18)F-FRGD2, including final high-performance liquid chromatography purification, is about 200 +/- 20 min. Typical decay-corrected radiochemical yield is 23% +/- 2% (n = 20). (18)F-FRGD2 is metabolically stable. The binding potential extrapolated from graphical analysis of PET data and Logan plot correlates well with the receptor density measured by sodium dodecyl sulfate polyacrylamide electrophoresis and autoradiography in various xenograft models. The tumor-to-background ratio at 1 h after injection of (18)F-FRGD2 also gives a good linear relationship with the tumor tissue integrin level. CONCLUSION: The dimeric RGD peptide tracer (18)F-FRGD2, with high integrin specificity and favorable excretion profile, may be translated into the clinic for imaging integrin alpha(v)beta(3) expression. The binding potential calculated from simplified tracer kinetic modeling such as the Logan plot appears to be an excellent indicator of tumor integrin density.
Animals↗
Histology-Based Virtual RNA Inference Identifies Pathways Associated With Metastasis Risk in Colorectal Cancer.
Colorectal cancer (CRC) remains a major health concern, with >150,000 new diagnoses and >50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (eg, Visium and Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented virtual RNA inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST data set to date, comprising 45 patients and >300,000 Visium spots from primary tumors. Using state-of-the-art deep learning models (UNI, ResNet-50, Vision Transformer, and Vision Mamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, and inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.
Humans↗
Identification of protein biomarkers in Dupuytren's contracture using surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).
BACKGROUND: To study the protein expression profiles associated with Dupuytren's contracture (DC) to identify potential disease protein biomarkers (PBM) using a proteomic technology--Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). METHODS: Normal and disease palmar fascia from DC patients were analyzed using Ciphergen's SELDI-TOF-MS Protein Biological System II (PBSII) ProteinChip reader. Analysis of the resulting SELDI-TOF spectra was carried out using the peak cluster analysis program (BioMarker Wizard, Ciphergen). Common peak clusters were then filtered using a bootstrap algorithm called SAM (Significant Analysis of Microarrays) for increased fidelity in our analysis. RESULTS: Several differentially expressed low molecular weight (<20 kDa) tissue proteins were identified. Spectra generated using both ZipTipC18 aided Au array and WCX2 array based SELDI-TOF-MS were reproducible, with an average peak cluster mass standard deviation for both methods of <1.74 x10(-4). Initial peak cluster analysis of the SELDI spectra identified both up-(14) and down-(3)regulated proteins associated with DC. Further analysis of the peak cluster data using the bootstrap algorithm SAM identified three disease-associated protein features (4600.8 Da, 10254.5 Da, and 11405.1 Da) that were elevated (5.45, 11.7, and 4.28 fold, respectively, with a 0% median false discovery rate). CONCLUSION: SELDI-TOF-MS identified three potential low molecular weight tissue protein markers (p4.6DC, p1ODC, p11.7DC) for DC. The ability of SELDI-TOF-MS to resolve low molecular weight proteins suggests that the method may provide a means of deciphering the biomarker-rich low molecular weight region of the human proteome. Application of such novel technology may help clinicians to focus on specific molecular abnormalities in diseases with no known molecular pathogenesis, and uncover therapeutic and/or diagnostic targets.
Algorithms↗
Accurate molecular classification of human cancers based on gene expression using a simple classifier with a pathological tree-based framework.
Recent studies suggest accurate prediction of tissue of origin for human cancers can be achieved by applying sophisticated statistical learning procedures to gene expression data obtained from DNA microarrays. We have pursued the hypothesis that a more straightforward and equally accurate strategy for classifying human tumors is to use a simple algorithm that considers gene expression levels within a tree-based framework that encodes limited information about pathology and tissue ontogeny. By considering gene expression data within this framework, we found only a small number of genes were required to achieve a relatively high accuracy level in tumor classification. Using as few as 45 genes we were able to classify 157 of 190 human malignant tumors correctly, which is comparable to previous results obtained with sophisticated classifiers using thousands of genes. Our simple classifier accurately predicted the origin of metastatic tumors even when the classifier was trained using only primary tumors, and the classifier produced accurate predictions when trained and tested on expression data from different labs, and from different microarray platforms. Our findings suggest that accurate and robust cancer diagnosis from gene expression profiles can be achieved by mimicking the classification strategies routinely used by surgical pathologists.
Algorithms↗
Gene expression profiles in peripheral lymphocytes by arsenic exposure and skin lesion status in a Bangladeshi population.
Millions of individuals worldwide are chronically exposed to arsenic through their drinking water. In this study, the effect of arsenic exposure and arsenical skin lesion status on genome-wide gene expression patterns was evaluated using RNA from peripheral blood lymphocytes of individuals selected from the Health Effects of Arsenic Longitudinal Study. Affymetrix HG-U133A GeneChip (Affymetrix, Santa Clara, CA) arrays were used to measure the expression of approximately 22,000 transcripts. Our primary statistical analysis involved identifying differentially expressed genes between participants with and without arsenical skin lesions based on the significance analysis of microarrays statistic with an a priori defined 1% false discovery rate to minimize false positives. To better characterize differential expression, we also conducted Gene Ontology and pathway comparisons in addition to the gene-specific analyses. Four-hundred sixty-eight genes were differentially expressed between these two groups, from which 312 differentially expressed genes were identified by restricting the analysis to female never-smokers. We also explored possible differential gene expression by arsenic exposure levels among individuals without manifest arsenical skin lesions; however, no differentially expressed genes could be identified from this comparison. Our findings show that microarray-based gene expression analysis is a powerful method to characterize the molecular profile of arsenic exposure and arsenic-induced diseases. Genes identified from this analysis may provide insights into the underlying processes of arsenic-induced disease and represent potential targets for chemoprevention studies to reduce arsenic-induced skin cancer in this population.
Adult↗
Gene expression profiling in single cells within tissue.
We developed a robust multiplex fluorescent in situ hybridization (FISH) technique in archival formalin-fixed, paraffin-embedded (FFPE) human tissue sections while preserving the microanatomical context. This identifies single-cell gene expression patterns by probing multiple, unique nascent RNA transcripts and yields predictive quantitative gene expression signatures.
Biomarkers, Tumor↗
Toward early oral cancer detection using gene expression profiling of saliva: a thoroughfare or dead end?
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Biomarkers, Tumor↗
Differences in gene expression between B-cell chronic lymphocytic leukemia and normal B cells: a meta-analysis of three microarray studies.
MOTIVATION: A major focus of current cancer research is to identify genes that can be used as markers for prognosis and diagnosis, and as targets for therapy. Microarray technology has been applied extensively for this purpose, even though it has been reported that the agreement between microarray platforms is poor. A critical question is: how can we best combine the measurements of matched genes across microarray platforms to develop diagnostic and prognostic tools related to the underlying biology? RESULTS: We introduce a statistical approach within a Bayesian framework to combine the microarray data on matched genes from three investigations of gene expression profiling of B-cell chronic lymphocytic leukemia (CLL) and normal B cells (NBC) using three different microarray platforms, oligonucleotide arrays, cDNA arrays printed on glass slides and cDNA arrays printed on nylon membranes. Using this approach, we identified a number of genes that were consistently differentially expressed between CLL and NBC samples.
Algorithms↗
A molecular correlate to the Gleason grading system for prostate adenocarcinoma.
Adenocarcinomas of the prostate can be categorized into tumor grades based on the extent to which the cancers histologically resemble normal prostate glands. Because grades are surrogates of intrinsic tumor behavior, characterizing the molecular phenotype of grade is of potential clinical importance. To identify molecular alterations underlying prostate cancer grades, we used microdissection to obtain specific cohorts of cancer cells corresponding to the most common Gleason patterns (patterns 3, 4, and 5) from 29 radical prostatectomy samples. We paired each cancer sample with matched benign lumenal prostate epithelial cells and profiled transcript abundance levels by microarray analysis. We identified an 86-gene model capable of distinguishing low-grade (pattern 3) from high-grade (patterns 4 and 5) cancers. This model performed with 76% accuracy when applied to an independent set of 30 primary prostate carcinomas. Using tissue microarrays comprising >800 prostate samples, we confirmed a significant association between high levels of monoamine oxidase A expression and poorly differentiated cancers by immunohistochemistry. We also confirmed grade-associated levels of defender against death (DAD1) protein and HSD17 beta4 transcripts by immunohistochemistry and quantitative RT-PCR, respectively. The altered expression of these genes provides functional insights into grade-associated features of therapy resistance and tissue invasion. Furthermore, in identifying a profile of 86 genes that distinguish high- from low-grade carcinomas, we have generated a set of potential targets for modulating the development and progression of the lethal prostate cancer phenotype.
Adenocarcinoma↗
Use of high-throughput DNA microarrays to identify biomarkers for bladder cancer.
BACKGROUND: Numerous markers have been described to correlate to some extent with tumor stage and prognosis of patients with bladder cancer. The power of many of these biomarkers in detecting superficial disease or predicting the clinical outcome of individual tumors is limited, and alternative markers are still in demand. High-throughput microarrays represent novel means for cancer research and tumor marker discovery. APPROACH: The aim of this report was to discuss the application of DNA technologies to provide novel biomarkers for bladder cancer. CONTENT: Specific bladder tumor subtypes have distinct gene expression profiles. The use of high-throughput DNA microarrays allows identification of the most prevalent and relevant alterations within bladder tumors. Clusters of differentially expressed genes will become biomarkers to discriminate subgroups of patients with different histopathology or clinical outcome. Additionally, the identified individual molecular targets might be further validated and developed into novel serum or urinary biomarkers for the diagnosis and/or as prognostic factors to be applied in clinical practice. The diagnosis and prognosis of bladder cancer would be enhanced by the use of such markers, and the marker itself may constitute a therapeutic target when studied in appropriate patients and control groups. SUMMARY: Expression profiling with high-throughput DNA microarrays has the potential of providing critical clues for the management of bladder cancer patients. As the quality, standardization, and ease of use of the technology increase and the costs decrease, DNA microarrays will move from being a technology restricted to research to clinical laboratories in the near future.
Biomarkers, Tumor↗
tie-1 protein tyrosine kinase: a novel independent prognostic marker for gastric cancer.
Protein tyrosine kinases (PTKs) are a major class of proto-oncogenes that are involved in tumor progression. The purpose of this study was to establish a comprehensive PTK expression profile in gastric cancers, with the objective of identifying possible biomarkers for gastric cancer progression. We have designed degenerate primers according to the consensus catalytic motifs to amplify PTK molecules from gastric cancers by reverse transcriptase-PCR methods. The PTK expression profile was established by sequencing analysis of the cloned PCR products. We have identified 17 PTKs from a gastric adenocarcinoma. Two receptor PTKs, tie-1 and axl, were selected for in situ immunohistochemistry studies because of their higher expression level and their described roles in adhesion, invasion, and angiogenesis. Among the 97 gastric adenocarcinoma tissues examined, we observed positive immunohistochemical staining of tie-1 PTK in 69 and positive staining of axl kinase in 71 tissues. Statistical analysis with clinicopathological features indicates that tie-1 kinase expression is inversely correlated with patients' survival, whereas axl fails to show similar clinical significance. Our results illustrate the utility of tyrosine kinase gene family profiling in human gastric cancers and show that tie-1 tyrosine kinase may serve as a novel independent prognostic marker for gastric adenocarcinoma patients.
Adenocarcinoma↗