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Gene expression profiling of human lung tissue from smokers with severe emphysema.

The mechanism by which inhaled smoke causes the anatomic lesions and physiologic impairment of chronic obstructive pulmonary disease remains unknown. We used high-density microarrays to measure gene expression in severely emphysematous lung tissue removed from smokers at lung volume reduction surgery (LVRS) and normal or mildly emphysematous lung tissue from smokers undergoing resection of pulmonary nodules. Class prediction algorithms identified 102 genes that accurately distinguished severe emphysema from non-/mildly emphysematous lung tissue. We also defined a number of genes whose expression levels correlated strongly with lung diffusion capacity for carbon monoxide and/or forced expiratory volume at 1 s. Genes related to oxidative stress, extracellular matrix synthesis, and inflammation were increased in severe emphysema, whereas expression of endothelium-related genes was decreased. To identify candidate genes that might be causally involved in the pathogenesis of emphysema, we linked gene expression profiles to chromosomal regions previously associated with chronic obstructive pulmonary disease in genome-wide linkage analyses. Unsupervised hierarchical clustering of the LVRS samples revealed distinct molecular subclasses of severe emphysema, with body mass index as the only clinical variable that differed between the groups. Class prediction models established a set of genes that predicted functional outcome at 6 mo after LVRS. Our findings suggest that the gene expression profiles from human emphysematous lung tissue may provide insight into pathogenesis, uncover novel molecular subclasses of disease, predict response to LVRS, and identify targets for therapeutic intervention.

Aged↗

Quantitative real-time RT-PCR analysis of eight novel estrogen-regulated genes in breast cancer.

BACKGROUND: Biological markers capable of predicting the risk of recurrence and the response to treatment in breast cancer are eagerly awaited. Estrogen and progesterone receptors (ER, PgR) in tumor cells mark cancers that are more likely to respond to endocrine treatment, but up to 40% of such patients do not respond. Here, the expression of a group of estrogen-regulated genes, previously identified by microarray analysis of in vitro models, was measured in breast tumors and possible associations with other clinicopathological variables were investigated. METHODS: The expression of CD24, CD44, HAT-1, BAK-1, G1P3, TIEG, NRP-1 and RXRalpha was measured by quantitative real-time RT-PCR on RNA from eighteen primary breast tumors. Statistical analyses were used to identify correlations among the eight genes and the available clinicopathological data. RESULTS: Variable expression levels of all the genes were observed in all the samples examined. Significant associations of CD24 with tumor size, CD44 with lymph node invasion, and HAT-1 and BAK-1 with ER positivity were found. The possible combinatorial value of these genes was assessed. Unsupervised hierarchical clustering analysis demonstrated that the expression profile of these genes was able to predict ER status with an acceptable approximation. CONCLUSIONS: Eight novel potential markers for breast cancer have been preliminarily characterized. As expected from in vitro data, their expression is able to discriminate ER- versus ER+ tumors.

Acetyltransferases↗

Expression of ribosomal and translation-associated genes is correlated with a favorable clinical course in chronic lymphocytic leukemia.

B-cell chronic lymphocytic leukemia (B-CLL) is a heterogeneous disease with a highly variable clinical course. Recent studies have shown that CD38 surface expression on the malignant cell clone may serve as a prognostic marker in that CD38(+) patients with B-CLL are characterized by advanced disease stage, lesser responsiveness to chemotherapy, and shorter survival than CD38(-) patients. To further investigate the molecular phenotype of these 2 clinical subgroups, we compared the gene expression profiles of CD38(+) (n = 25) with CD38(-) (n = 45) B-CLL patients using oligonucleotide-based DNA chip microarrays representative of approximately 5600 genes. The results showed that B-CLLs display a common gene expression profile that is largely independent of CD38 expression. Nonetheless, the expression of 14 genes differed significantly between the 2 groups, including genes that are involved in the regulation of cell survival. Furthermore, unsupervised hierarchical cluster analysis of 76 B-CLL samples led to the separation of 2 major subgroups, comprising 20 and 56 patients. Clustering to the smaller group was due in part to the coordinate high expression of a large number of ribosomal and other translation-associated genes, including elongation factors. Importantly, we found that patients with high expression of translation factors were characterized by a more favorable clinical course with significantly longer progression-free survival and reduced chemotherapy requirements than the remaining patients (P <.05). Our data show that gene expression profiling can help identify B-CLL subtypes with different clinical characteristics. Furthermore, our results suggest a role of translation-associated genes in the pathogenesis of B-CLL.

ADP-ribosyl Cyclase↗

Mycosis fungoides shows concurrent deregulation of multiple genes involved in the TNF signaling pathway: an expression profile study.

Mycosis fungoides (MF) is the most frequent type of cutaneous T-cell lymphoma, whose diagnosis and study is hampered by its morphologic similarity to inflammatory dermatoses (ID) and the low proportion of tumoral cells, which often account for only 5% to 10% of the total tissue cells. cDNA microarray studies using the CNIO OncoChip of 29 MF and 11 ID cases revealed a signature of 27 genes implicated in the tumorigenesis of MF, including tumor necrosis factor receptor (TNFR)-dependent apoptosis regulators, STAT4, CD40L, and other oncogenes and apoptosis inhibitors. Subsequently a 6-gene prediction model was constructed that is capable of distinguishing MF and ID cases with unprecedented accuracy. This model correctly predicted the class of 97% of cases in a blind test validation using 24 MF patients with low clinical stages. Unsupervised hierarchic clustering has revealed 2 major subclasses of MF, one of which tends to include more aggressive-type MF cases including tumoral MF forms. Furthermore, signatures associated with abnormal immunophenotype (11 genes) and tumor stage disease (5 genes) were identified.

Cluster Analysis↗

Insights into the multistep transformation of MGUS to myeloma using microarray expression analysis.

To define specific pathways important in the multistep transformation process of normal plasma cells (PCs) to monoclonal gammopathy of uncertain significance (MGUS) and multiple myeloma (MM), we have applied microarray analysis to PCs from 5 healthy donors (N), 7 patients with MGUS, and 24 patients with newly diagnosed MM. Unsupervised hierarchical clustering using 125 genes with a large variation across all samples defined 2 groups: N and MGUS/MM. Supervised analysis identified 263 genes differentially expressed between N and MGUS and 380 genes differentially expressed between N and MM, 197 of which were also differentially regulated between N and MGUS. Only 74 genes were differentially expressed between MGUS and MM samples, indicating that the differences between MGUS and MM are smaller than those between N and MM or N and MGUS. Differentially expressed genes included oncogenes/tumor-suppressor genes (LAF4, RB1, and disabled homolog 2), cell-signaling genes (RAS family members, B-cell signaling and NF-kappaB genes), DNA-binding and transcription-factor genes (XBP1, zinc finger proteins, forkhead box, and ring finger proteins), and developmental genes (WNT and SHH pathways). Understanding the molecular pathogenesis of MM by gene expression profiling has demonstrated sequential genetic changes from N to malignant PCs and highlighted important pathways involved in the transformation of MGUS to MM.

Cell Transformation, Neoplastic↗

The molecular classification of multiple myeloma.

To better define the molecular basis of multiple myeloma (MM), we performed unsupervised hierarchic clustering of mRNA expression profiles in CD138-enriched plasma cells from 414 newly diagnosed patients who went on to receive high-dose therapy and tandem stem cell transplants. Seven disease subtypes were validated that were strongly influenced by known genetic lesions, such as c-MAF- and MAFB-, CCND1- and CCND3-, and MMSET-activating translocations and hyperdiploidy. Indicative of the deregulation of common pathways by gene orthologs, common gene signatures were observed in cases with c-MAF and MAFB activation and CCND1 and CCND3 activation, the latter consisting of 2 subgroups, one characterized by expression of the early B-cell markers CD20 and PAX5. A low incidence of focal bone disease distinguished one and increased expression of proliferation-associated genes of another novel subgroup. Comprising varying fractions of each of the other 6 subgroups, the proliferation subgroup dominated at relapse, suggesting that this signature is linked to disease progression. Proliferation and MMSET-spike groups were characterized by significant overexpression of genes mapping to chromosome 1q, and both exhibited a poor prognosis relative to the other groups. A subset of cases with a predominating myeloid gene expression signature, excluded from the profiling analyses, had more favorable baseline characteristics and superior prognosis to those lacking this signature.

Chromosome Mapping↗

Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis.

Monoclonal gammopathy of undetermined significance (MGUS) can progress to multiple myeloma (MM). Although these diseases share many of the same genetic features, it is still unclear whether global gene-expression profiling might identify prior genomic signatures that distinguish them. Through significance analysis of microarrays, 52 genes involved in important pathways related to cancer were differentially expressed in the plasma cells of healthy subjects (normal plasma-cell [NPC]; n=22) and patients with stringently defined MGUS/smoldering MM (n=24) and symptomatic MM (n=351) (P<.001). Unsupervised hierarchical clustering of 351 patients with MM, 44 with MGUS (24+20), and 16 with MM from MGUS created 2 major cluster branches, one containing 82% of the MGUS patients and the other containing 28% of the MM patients, termed MGUS-like MM (MGUS-L MM). Using the same clustering approach on an independent cohort of 214 patients with MM, 27% were found to be MGUS-L. This molecular signature, despite its association with a lower incidence of complete remission (P=.006), was associated with low-risk clinical and molecular features and superior survival (P<.01). The MGUS-L signature was also seen in plasma cells from 15 of 20 patients surviving more than 10 years after autotransplantation. These data provide insight into the molecular mechanisms of plasma-cell dyscrasias.

Aged↗

Redefinition of Affymetrix probe sets by sequence overlap with cDNA microarray probes reduces cross-platform inconsistencies in cancer-associated gene expression measurements.

BACKGROUND: Comparison of data produced on different microarray platforms often shows surprising discordance. It is not clear whether this discrepancy is caused by noisy data or by improper probe matching between platforms. We investigated whether the significant level of inconsistency between results produced by alternative gene expression microarray platforms could be reduced by stringent sequence matching of microarray probes. We mapped the short oligo probes of the Affymetrix platform onto cDNA clones of the Stanford microarray platform. Affymetrix probes were reassigned to redefined probe sets if they mapped to the same cDNA clone sequence, regardless of the original manufacturer-defined grouping. The NCI-60 gene expression profiles produced by Affymetrix HuFL platform were recalculated using these redefined probe sets and compared to previously published cDNA measurements of the same panel of RNA samples. RESULTS: The redefined probe sets displayed a substantially higher level of cross-platform consistency at the level of gene correlation, cell line correlation and unsupervised hierarchical clustering. The same strategy allowed an almost complete correspondence of breast cancer subtype classification between Affymetrix gene chip and cDNA microarray derived gene expression data, and gave an increased level of similarity between normal lung derived gene expression profiles using the two technologies. In total, two Affymetrix gene-chip platforms were remapped to three cDNA platforms in the various cross-platform analyses, resulting in improved concordance in each case. CONCLUSION: We have shown that probes which target overlapping transcript sequence regions on cDNA microarrays and Affymetrix gene-chips exhibit a greater level of concordance than the corresponding Unigene or sequence matched features. This method will be useful for the integrated analysis of gene expression data generated by multiple disparate measurement platforms.

Breast Neoplasms↗

A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections.

BACKGROUND: Three-dimensional (3D) multivariate Fourier Transform Infrared (FTIR) image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique. METHODS: Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB routine and placed in a single data matrix for multivariate analysis using Cytospec. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software. RESULTS: The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components. CONCLUSION: 3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

Journal Article↗

Inference of ancestry: constructing hierarchical reference populations and assigning unknown individuals.

The ability to infer personal genetic ancestry is being increasingly utilised in certain medical and forensic situations. Herein, the unsupervised Bayesian clustering algorithms structure, is employed to analyse 377 autosomal short tandem repeats typed on 1,056 individuals from the Centre d'Etude du Polymorphisme Humain Human Diversity Panel. Individuals of known geographical origin were hierarchically classified into a framework of increasingly homogeneous clusters to serve as reference populations into which individuals of unknown ancestry can be assigned. The groupings were characterised by the geographical affinities of cluster members and the accuracy of these procedures was verified using several genetic indices. Fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored in this dataset. Metrics indicated that within certain lines, the strongest structuring signals were detected at the leaves of the hierarchy where lineage-specific groupings were identified. The accuracy of unknown assignment was assessed at each level of the hierarchy using a 'leave one out' strategy in which each individual was stripped of cluster membership and then re-assigned using the supervised Bayesian clustering algorithm implemented in GeneClass2. Although most clusters at all levels of resolution experienced highly accurate assignment, a decline was observed in the finer levels due to the mixed membership characteristics of some individuals. The parameters defined by this study allowed for assignment of unknown individuals to genetically defined clusters with measured likelihood. Shared ancestry data can then be inferred for the unknown individual.

Algorithms↗

Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritis.

As indicators of responsiveness to a tumour necrosis factor (TNF)alpha blocking agent (infliximab) are lacking in rheumatoid arthritis, we have used gene profiling in peripheral blood mononuclear cells to predict a good versus poor response to infliximab. Thirty three patients with very active disease (Disease Activity Score 28 >5.1) that resisted weekly methotrexate therapy were given infliximab at baseline, weeks 2 and 6, and every 8th week thereafter. The patients were categorized as responders if a change of Disease Activity Score 28 = 1.2 was obtained at 3 months. Mononuclear cell RNAs were collected at baseline and at three months from responders and non-responders. The baseline RNAs were hybridised to a microarray of 10,000 non-redundant human cDNAs. In 6 responders and 7 non-responders, 41 mRNAs identified by microarray analysis were expressed as a function of the response to treatment and an unsupervised hierarchical clustering perfectly separated these responders from non-responders. The informativeness of 20 of these 41 transcripts, as measured by qRT-PCR, was re-assessed in 20 other patients. The combined levels of these 20 transcripts properly classified 16 out of 20 patients in a leave-one-out procedure, with a sensitivity of 90% and a specificity of 70%, whereas a set of only 8 transcripts properly classified 18/20 patients. Trends for changes in various transcript levels at three months tightly correlated with treatment responsiveness and a down-regulation of specific transcript levels was observed in non-responders only. Our gene profiling obtained by a non-invasive procedure should now be used to predict the likely responders to an infliximab/methotrexate combination.

Adult↗

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n&#x2009;=&#x2009;39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n&#x2009;=&#x2009;30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC&#x2009;>&#x2009;0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p&#x2009;<&#x2009;0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗

Gene expression profiling of localized esophageal carcinomas: association with pathologic response to preoperative chemoradiation.

PURPOSE: Patients with localized esophageal carcinoma have a 5-year survival rate of less than 20%. Patients are often treated similarly (ie, with preoperative chemoradiotherapy) but the outcomes vary greatly. Chemoradiotherapy and surgery can result in significant undesirable consequences. Currently, however, there are no tools to help select optimum therapy. We hypothesized that gene expression profiling could provide clues and biomarkers for selection of therapy. METHODS: Pretreatment endoscopic cancer biopsies from 19 patients (16 with adenocarcinoma, two with squamous cell carcinoma, and one with adenosquamous carcinoma) enrolled onto a preoperative chemoradiotherapy protocol were profiled using oligonucleotide microarrays. Surgical specimens following therapy were assessed for the degree of pathologic response. On the basis of array data, selected genes were analyzed by polymerase chain reaction. RESULTS: Unsupervised hierarchical cluster analysis segregated the cancers into two molecular subtypes, each consisting 10 and nine specimens, respectively. Most cancers (five of six) that had pathologic complete response (pathCR) clustered in molecular subtype I. Subtype II, with one exception, consisted cancers that had less than pathCR (< pathCR). Using a combination marker approach, levels of PERP, S100A2, and SPRR3 allowed discrimination of pathCR from < pathCR with high sensitivity and specificity (85%). Pathway analysis identified apoptotic pathway as one of the key functions downregulated in molecular type II in comparison with type I. CONCLUSION: These encouraging, albeit preliminary, data suggest that expression profiling may distinguish cancers with different pathologic outcome. This is the first report to show subtypes of esophageal cancers with distinct molecular signatures. The potential of PERP, S100A2, and SPRR3 as biomarkers of pathCR warrants further validation.

Adenocarcinoma↗

Phenotypic anchoring of gene expression changes during estrogen-induced uterine growth.

A major challenge in the emerging field of toxicogenomics is to define the relationships between chemically induced changes in gene expression and alterations in conventional toxicologic parameters such as clinical chemistry and histopathology. We have explored these relationships in detail using the rodent uterotrophic assay as a model system. Gene expression levels, uterine weights, and histologic parameters were analyzed 1, 2, 4, 8, 24, 48, and 72 hr after exposure to the reference physiologic estrogen 17 beta-estradiol (E2). A multistep analysis method, involving unsupervised hierarchical clustering followed by supervised gene ontology-driven clustering, was used to define the transcriptional program associated with E2-induced uterine growth and to identify groups of genes that may drive specific histologic changes in the uterus. This revealed that uterine growth and maturation are preceded and accompanied by a complex, multistage molecular program. The program begins with the induction of genes involved in transcriptional regulation and signal transduction and is followed, sequentially, by the regulation of genes involved in protein biosynthesis, cell proliferation, and epithelial cell differentiation. Furthermore, we have identified genes with common molecular functions that may drive fluid uptake, coordinated cell division, and remodeling of luminal epithelial cells. These data define the mechanism by which an estrogen induces organ growth and tissue maturation, and demonstrate that comparison of temporal changes in gene expression and conventional toxicology end points can facilitate the phenotypic anchoring of toxicogenomic data.

Animals↗

CLASPP: A unified model for predicting post-translational modifications.

Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular pathways and increasing the functional diversity of the proteome. Accurately predicting the PTM types that are likely to occur at a given site in the primary sequence is a key challenge in functional proteomics. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types, making it difficult to predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-based Stratified PTM Predictor (CLASPP), a unified PTM prediction model. CLASPP addresses imbalance challenges by leveraging unsupervised clustering-based undersampling and a novel contrastive learning framework tailored to PTM data. Additionally, our hierarchical data organization and curation are shown to improve CLASPP's performance by balancing the representation of individual PTM types and provides a standardized dataset to train and validate future model designs. Drawing inspiration from advancements in image and natural language processing, the CLASPP model employs a multi-stage training strategy and a high-quality, curated training dataset to improve PTM prediction performance. To uncover what is learned during the contrastive learning stage, the CLASPP model is shown to distinguish known protein kinase substrate specificity profiles as a form of explainability. Finally, we evaluate the application of CLASPP in predicting PTMs in different model organisms and experimentally validated ubiquitination sites in the understudied DCLK3 kinase. Overall, CLASPP represents a unified model for PTM prediction that addresses key bottlenecks in data imbalance and offers new strategies for biological data curation, thereby improving PTM-type prediction performance across diverse organisms.

Protein Processing, Post-Translational↗

Gene expression profiling predicts survival in conventional renal cell carcinoma.

BACKGROUND: Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival. METHODS AND FINDINGS: Gene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001). CONCLUSIONS: cRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

Adult↗

Distinction in gene expression profiles demonstrated in parathyroid adenomas by high-density oligoarray technology.

OBJECTIVES: Somatic deletion of chromosome 11q13 is the most frequent genetic aberration in parathyroid adenoma. To gain further insight into the genetic etiology of parathyroid tumor development, we examined a comprehensive gene expression profile of parathyroid adenomas and normal parathyroid tissues. The results were then evaluated with respect to differences between adenomas and normal parathyroid tissue, and to the presence of loss of heterozygosity (LOH) in chromosomal region 11q13. DESIGN AND METHODS: Sporadic parathyroid adenomas and normal parathyroids were hybridized against HG-U95Av2 oligonucleotide arrays (Affymetrix) containing a total of 12,625 probe sets. Quantitative real-time PCR (QRT-PCR) was performed in a larger series of parathyroid adenomas, in order to con-firm the microarray results. RESULTS: Cyclin D1 and c-Jun showed increased expression in adenomas vs normal parathyroids by microarray analysis and QRT-PCR, suggesting an oncogenic role of these genes in parathyroid tumor development. At unsupervised hierarchical clustering, the adenomas fell into two groups: Group I adenomas were characterized by 11q13 LOH, while Group II adenomas lacked this abnormality. In addition, a t-test analysis identified largely non-overlapping genes with differential expression in the tumors subgroups; e.g. in Group I tumors the putative oncogene ENC 1 was found highly over-expressed vs Group II adenomas. CONCLUSIONS: The microarray analyses revealed partly distinctive and partly common expression profiles in parathyroid adenomas with and without 11q13 LOH. In addition, approximately half of the under-expressed genes were mapped to chromosome 11, in agreement with a dose effect following loss of this chromosome.

Adenoma↗

CD133-positive hematopoietic stem cell "stemness" genes contain many genes mutated or abnormally expressed in leukemia.

Affymetrix human Hu133A oligonucleotide arrays were used to study the expression profile of CD133+ cord blood (CB) and peripheral blood (PB) using CD133 cell-surface marker. An unsupervised hierarchical clustering of 14,025 valid probe sets showed a clear distinction between the CD133+ cells representing the hematopoietic stem cell (HSC) population and CD133-differentiated cells. Two hundred forty-four genes were found to be upregulated by at least twofold in the CD133-positive cells of both CB and PB compared with the CD133-negative cells. These genes represent the hematopoietic "stemness," whereas the 218 and 304 upregulated genes exclusively in PB and CB, respectively, represent tissue specificity. Some of the stemness genes were also common to HSC genes found to be upregulated in several recently published studies. Among these common stemness genes, we identified several groups of genes that have an important role in hematopoiesis: growth factor receptors, transcription factors, genes that have an important role in development, and genes involved in cell growth. Sixteen selected stemness genes are known to be mutated or abnormally regulated in acute leukemias. It can be suggested that key hematopoietic stemness machinery genes may lead to abnormal proliferation and leukemia upon mutation or change of their expression.

AC133 Antigen↗