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PubMed2002

Hodgkin and Reed-Sternberg cells harbor alterations in the major tumor suppressor pathways and cell-cycle checkpoints: analyses using tissue microarrays.

Tumoral cells in Hodgkin lymphoma (HL) display an increased growth fraction and diminished apoptosis, implying a profound disturbance of the cell cycle and apoptosis regulation. However, limitations of molecular techniques have prevented the analysis of the tumor suppressor pathways and cell-cycle checkpoints. Tissue microarray (TMA) is a powerful tool for analyzing a large number of molecular variables in a large series of tumors, although the feasibility of this technique has not yet been demonstrated in heterogeneous tumors. The expression of 29 genes regulating the cell cycle and apoptosis were analyzed by immunohistochemistry and in situ hybridization in 288 HL biopsies using TMA. The sensitivity of the technique was validated by comparing the results with those obtained in standard tissue sections. The results revealed multiple alterations in different pathways and checkpoints, including G1/S and G2/M transition and apoptosis. Striking findings were the overexpression of cyclin E, CDK2, CDK6, STAT3, Hdm2, Bcl2, Bcl-X(L), survivin, and NF-kappaB proteins. A multiparametric analysis identified proteins associated with increased growth fraction (Hdm2, p53, p21, Rb, cyclins A, B1, D3, and E, CDK2, CDK6, SKP2, Bcl-X(L), survivin, STAT1, and STAT3), and proteins associated with apoptosis (NF-kappaB, STAT1, and RB). The analysis also demonstrated that Epstein-Barr virus (EBV)-positive cases displayed a characteristic profile, confirming the pathogenic role of EBV in HL. Survival probability depends on multiple biologic factors, including overexpression of Bcl2, p53, Bax, Bcl-X(L), MIB1, and apoptotic index. In conclusion, Hodgkin and Reed-Sternberg cells harbor concurrent and overlapping alterations in the major tumor suppressor pathways and cell-cycle checkpoints. This appears to determine the viability of the tumoral cells and the clinical outcome.

Apoptosis↗
PubMed2003

Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients.

MOTIVATIONS AND RESULTS: For classifying gene expression profiles or other types of medical data, simple rules are preferable to non-linear distance or kernel functions. This is because rules may help us understand more about the application in addition to performing an accurate classification. In this paper, we discover novel rules that describe the gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. We also introduce a new classifier, named PCL, to make effective use of the rules. PCL is accurate and can handle multiple parallel classifications. We evaluate this method by classifying 327 heterogeneous ALL samples. Our test error rate is competitive to that of support vector machines, and it is 71% better than C4.5, 50% better than Naive Bayes, and 43% better than k-nearest neighbour. Experimental results on another independent data sets are also presented to show the strength of our method. AVAILABILITY: Under http://sdmc.lit.org.sg/GEDatasets/, click on Supplementary Information.

Algorithms↗
PubMed2005

Predictors of cardiovascular death in ESRD.

End stage renal disease (ESRD) is a situation with a cardiovascular risk profile of almost unique severity. While traditional risk factors dominate the scene in the general population, non traditional risk factors like inflammation (high C Reactive Protein, CRP), high brain natriuretic peptide, as an expression of left ventricular hypertrophy and left ventricular dysfunction, and accumulation of the endogenous inhibitor of the NO synthase, asymmetric dimethyl arginine are all markers of high CV risk of ESRD patients. To obtain a quantitative insight on the predictive power of traditional and emerging risk factors in ESRD, we performed a detailed multivariate survival analysis in the cardiovascular risk extended evaluation (CREED) cohort database. As expected, traditional risk factors (ie, age, sex, smoking, diabetes, and risk factors peculiar to the uremic state such as low serum albumin level) and treatment modality contributed to explain the all-cause mortality (37%) and cardiovascular variation mortality (24%) variation as well. When cardiovascular comorbidities were considered in this analysis, the explained variation in mortality increased to 45.4% and 36.4%, respectively. Furthermore, a combined score based on 2 biomarkers (brain natriuretic peptide and C-reactive protein levels) increased the explanatory power of these models by about 10%. In conclusion, traditional risk factors explain about half of all-cause and cardiovascular mortality variation in the ESRD population. The combined use of 2 biomarkers reflecting inflammation and left ventricular mass and function increases by about one fifth the explained mortality variation in this population. Biomarkers give information beyond that provided by traditional risk factors and therefore represent an useful adjunct for the definition of the risk profile of ESRD patients.

Biomarkers↗
PubMed2006

Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules.

MOTIVATION: Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis. RESULTS: By analyzing five datasets, we demonstrate that among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods [e.g. replacing MVs by zero, K nearest-neighbor (KNN) imputation algorithm, local least square imputation and Bayesian principal component analysis], while the classification and regression tree classifiers are sensitive in terms of classification accuracy. The KNNclassifiers built on differentially expressed genes (DEGs) are robust to the varied MV treatments, but the performances of the KNN classifiers based on all measured genes can be significantly deteriorated when imputing MVs for genes with larger missing rate (MR) (e.g. MR > 5%). Generally, while replacing MVs by zero performs relatively poor, the other imputation algorithms have little difference in affecting classification performances of the SVM or KNN classifiers. We further demonstrate the power and feasibility of our recently proposed functional expression profile (FEP) approach as means to handle microarray data with MVs. The FEPs, which are derived from the functional modules that are enriched with sets of DEGs and thus can be consistently identified under varied MV treatments, achieve precise disease classification with better biological interpretation. We conclude that the choice of MV treatments should be determined in context of the later approaches used for disease classification. The suggested exclusion criterion of ignoring the genes with larger MR (e.g. >5%), while justifiable for some classifiers such as KNN classifiers, might not be considered as a general rule for all classifiers.

Algorithms↗
PubMed2005

Transcriptomic and proteomic responses of human renal HEK293 cells to uranium toxicity.

The industrial use of uranium, in particular depleted uranium, has pin-pointed the need to review its chemical impact on human health. Global methodologies, applied to the field of toxicology, have demonstrated their applicability to investigation of fine molecular mechanisms. This report illustrate the power of toxicogenomics to evaluate the involvement of certain genes or proteins in response to uranium. We particularly show that 25% of modulated genes concern signal transduction and trafficking, that the calcium pathway is heavily disturbed and that nephroblastomas-related genes are involved (WIT-1, STMN1, and STMN2). A set of 18 genes was deregulated whatever the concentration of toxicant, which could constitute a signature of uranium exposure. Moreover, a group of downregulated genes, with corresponding disappearing proteins (HSP90, 14-3-3 protein, HMGB1) in two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), are good candidates for use as biomarkers of uranium effects. These results reveal a cross-checking between transcriptomic and proteomic technologies. Moreover, our temporal gene expression profiles suggest the existence of a concentration threshold between adaptive response and severe cell deregulation. Our results confirm the involvement of genes already described and also provide new highlights on cellular response to uranium.

Apoptosis↗
PubMed2004

Simple diagnostic assay for hairy cell leukaemia by immunocytochemical detection of annexin A1 (ANXA1).

A marker capable of distinguishing with certainty between hairy cell leukaemia and other B-cell malignant disease would be of great diagnostic value. Through gene expression profiling, annexin A1 (ANXA1) has been identified as a gene that is upregulated in hairy cell leukaemia. We did immunostaining of 500 B-cell tumours with a specific anti-ANXA1 monoclonal antibody and showed that ANXA1 protein expression is specific to hairy cell leukaemia. Immunocytochemical detection of ANXA1 represents a simple, inexpensive, highly sensitive and specific (100%) assay for diagnosis of hairy cell leukaemia. This assay will be especially useful in distinguishing hairy cell leukaemia from splenic lymphoma with villous lymphocytes and variant hairy cell leukaemia, both of which usually respond poorly to treatments that are effective in hairy cell leukaemia.

Annexin A1↗
PubMed2005

Identification of potential biomarkers of genotoxicity and carcinogenicity in L5178Y mouse lymphoma cells by cDNA microarray analysis.

In the present study, cDNA microarray analyses were performed with mouse cDNA chips in order to evaluate similarities and differences in the gene expression profiles for compounds differing in their genotoxic and carcinogenic potential. Eight test substances were evaluated, two each from four classes of compounds: genotoxic carcinogens (1,2-dibromoethane and glycidol), genotoxic noncarcinogens (8-hydroxyquinoline and emodin), nongenotoxic carcinogens (methyl carbamate and o-nitrotoluene), and nongenotoxic noncarcinogens (D-mannitol and 1,2-dichlorobenzene). Quadruplicate hybridization experiments were performed in order to identify a set of genes with significant expression changes for these four classes of substances. Twelve genes were consistently altered more than twofold by the genotoxic noncarcinogens while four genes were consistently regulated by the nongenotoxic carcinogens. One gene (Trp63) was identified whose expression was upregulated by all four genotoxic substances regardless of the presence or absence of carcinogenicity; this finding, however, was not confirmed by quantitative real-time RT-PCR. RT-PCR did confirm the change in expression of 9 of 15 genes (60%) identified by microarray analysis. Interestingly, the downregulated genes were least likely to be validated by real-time RT-PCR. Those genes showing more than a twofold change in expression level in response to at least one substance were further analyzed with hierarchical clustering after category assignment of each gene according to its main cellular function. Clustering revealed differences in the gene expression profiles between the genotoxic and nongenotoxic substances for genes involved in cell cycle control, the stress response, and the immune response. However, no clustering specific to all four carcinogenic substances was observed in any of the functional categories. Taken together, these results suggest that gene expression profiling in mouse lymphoma cells can provide valuable information for the evaluation of potential genotoxicity but may have limitations in predicting carcinogenicity.

Animals↗
PubMed2005

Comparative gene and protein expression in primary cultures of epithelial cells from benign prostatic hyperplasia and prostate cancer.

Primary cultures are widely used to investigate the disease-specific biology of prostate cancer and benign prostatic hyperplasia (BPH). To identify genes differentially expressed between epithelial cells cultured from adenocarcinomas versus BPH tissues, we used probe array technology. Gene expression profiles were evaluated on Affymetrix Human Cancer G110 Array Chips containing approximately 1900 cancer-related genes. After defined statistical analysis, genes that were over-expressed in cancer cultures were identified. Protein expression of four of the differentially expressed genes was measured in immunoblots, and the expression of two other genes was measured by real-time reverse transcription-polymerase chain reaction (RT-PCR). While no gene or protein was consistently over-expressed in all cancer versus BPH cell cultures, cytokeratin 16 protein was highly elevated in several of the cancer cultures, suggesting that a hyperproliferative phenotype may be characteristic of prostate cancer cells.

Adenocarcinoma↗
PubMed2003

Discovery of significant rules for classifying cancer diagnosis data.

METHODS AND RESULTS: We introduce a new method to discover many diversified and significant rules from high dimensional profiling data. We also propose to aggregate the discriminating power of these rules for reliable predictions. The discovered rules are found to contain low-ranked features; these features are found to be sometimes necessary for classifiers to achieve perfect accuracy. The use of low-ranked but essential features in our method is in contrast to the prevailing use of an ad-hoc number of only top-ranked features. On a wide range of data sets, our method displayed highly competitive accuracy compared to the best performance of other kinds of classification models. In addition to accuracy, our method also provides comprehensible rules to help elucidate the translation between raw data and useful knowledge.

Algorithms↗
PubMed2006

Development of biosensors for cancer clinical testing.

Biosensors are devices that combine a biochemical recognition/binding element (ligand) with a signal conversion unit (transducer). Biosensors are already used for several clinical applications, for example for electrochemical measurement of blood glucose concentrations. Application of biosensors in cancer clinical testing has several potential advantages over other clinical analysis methods including increased assay speed and flexibility, capability for multi-target analyses, automation, reduced costs of diagnostic testing and a potential to bring molecular diagnostic assays to community health care systems and to underserved populations. They have the potential for facilitating Point of Care Testing (POCT), where state-of-the-art molecular analysis is carried out without requiring a state-of-the-art laboratory. However, not many biosensors have been developed for cancer-related testing. One major challenge in harnessing the potential of biosensors is that cancer is a very complex set of diseases. Tumors vary widely in etiology and pathogenesis. Oncologists rely heavily on histological characterization of tumors and a few biomarkers that have demonstrated clinical utility to aid in patient management decisions. New genomic and proteomic molecular tools are being used to profile tumors and produce "molecular signatures." These signatures include genetic and epigenetic signatures, changes in gene expression, protein profiles and post-translational modifications of proteins. These molecular signatures provide new opportunities for utilizing biosensors. Biosensors have enormous potential to deliver the promise of new molecular diagnostic strategies to patients. This article describes some of the basic elements of cancer biology and cancer biomarkers relevant for the development of biosensors for cancer clinical testing, along with the challenges in using this approach.

Animals↗
PubMed2006

Expression profiling the developing mammalian enteric nervous system identifies marker and candidate Hirschsprung disease genes.

The enteric nervous system (ENS) is composed of neurons and glial cells, organized as interconnected ganglia within the gut wall, which controls peristalsis of the gut wall and secretions from its glands. The Ret receptor tyrosine kinase is expressed throughout enteric neurogenesis and is required for normal ENS development; humans with mutations in the RET locus have Hirschsprung disease (HSCR, an absence of ganglia in the colon), and mice lacking Ret have total intestinal aganglionosis. The Ret mutant mouse provides a tool for identifying genes implicated in development of the ENS. By using RNA from WT and Ret mutant (aganglionic) gut tissue and DNA microarrays, we have conducted a differential screen for ENS-expressed genes and have identified hundreds of candidate ENS-expressed genes. Forty-seven genes were selected for further analysis, representing diverse functional classes. We show that all of the analyzed genes are expressed in the ENS and that the screen was sensitive enough to identify genes marking only subpopulations of ENS cells. Our screen, therefore, was reliable and sensitive and has identified many previously undescribed genes for studying ENS development. Moreover, two of the genes identified in our screen Arhgef3 and Ctnnal1, have human homologues that map to previously identified HSCR susceptibility loci, thus representing excellent candidates for HSCR genes. This comprehensive profile of ENS gene expression refines our understanding of ENS development and serves as a resource for future developmental, biochemical, and human genetic studies.

Animals↗
PubMed2004

Predicting continuous values of prognostic markers in breast cancer from microarray gene expression profiles.

The prognostic and treatment-predictive markers currently in use for breast cancer are commonly based on the protein levels of individual genes (e.g., steroid receptors) or aspects of the tumor phenotype, such as histological grade and percentage of cells in the DNA synthesis phase of the cell cycle. Microarrays have previously been used to classify binary classes in breast cancer such as estrogen receptor (ER)-alpha status. To test whether the properties and specific values of conventional prognostic markers are encoded within tumor gene expression profiles, we have analyzed 48 well-characterized primary tumors from lymph node-negative breast cancer patients using 6728-element cDNA microarrays. In the present study, we used artificial neural networks trained with tumor gene expression data to predict the ER protein values on a continuous scale. Furthermore, we determined a gene expression profile-directed threshold for ER protein level to redefine the cutoff between ER-positive and ER-negative classes that may be more biologically relevant. With a similar approach, we studied the prediction of other prognostic parameters such as percentage cells in the S phase of the cell cycle (SPF), histological grade, DNA ploidy status, and progesterone receptor status. Interestingly, there was a consistent reciprocal relationship in expression levels of the genes important for both ER and SPF prediction. This and similar studies may be used to increase our understanding of the biology underlying these markers as well as to improve the currently available prognostic markers for breast cancer.

Biomarkers↗
PubMed2001

Individual metabolism should guide agriculture toward foods for improved health and nutrition.

Genomics and bioinformatics have the vast potential to identify genes that cause disease by investigating whole-genome databases. Comparison of an individual's geno-type with a genomic database will allow the prescription of drugs to be tailored to an individual's genotype. This same bioinformatic approach, applied to the study of human metabolites, has the potential to identify and validate targets to improve personalized nutritional health and thus serve to define the added value for the next generation of foods and crops. Advances in high-throughput analytic chemistry and computing technologies make the creation of a vast database of metabolites possible for several subsets of metabolites, including lipids and organic acids. In creating integrative databases of metabolites for bioinformatic investigation, the current concept of measuring single biomarkers must be expanded to 3 dimensions to 1) include a highly comprehensive set of metabolite measurements (a profile) by multiparallel analyses, 2) measure the metabolic profile of individuals over time rather than simply in the fasted state, and 3) integrate these metabolic profiles with genomic, expression, and proteomic databases. Application of the knowledge of individual metabolism will revolutionize the ability of nutrition to deliver health benefits through food in the same way that knowledge of genomics will revolutionize individual treatment of dis-ease with pharmaceuticals.

Biomarkers↗
PubMedDate not supplied

[Chronobiological evaluation of effects biomarkers and sampling].

Occupational exposure to oxidants is often associated with an increase in the levels of oxidative DNA damage in urine. Besides 8-hydroxy-2'-deoxyguanosine (8-oxo-dG), other products of position-8 guanine oxidation have been identified in urine, including 8-hydroxy-guanine (8-oxo-G) and 8-hydroxyguanosine (8-oxy-Guo). The aim of the present study was the characterization of these effect biomarkers in terms of inter- and intra-individuals varaibility, as well as in terms of their excretion profile during a 24 h-period. Urine samples were collected from 11 volunteers (6 samples/day). Urine concentrations of 8-oxo-G, 8-oxo-Guo, and 8-oxo-dG were determined by liquid chromatography-tandem mass spectrometry. The inter-individual variability, expressed as variation coefficient, was 85-150% for 8-oxo-G, 20-45% for 8-oxo-Guo, and 30-45% for 8-oxo-dG. The statistical anaysis for repeated measurements showed that none of the biomarkers was affected by significant variation during the day (one-way ANOVA, p < 0.05), thus excluding the existence of a circadian rhythm. We conclude that the sampling time is not critical for the assessment of oxidative DNA damage in urine.

Adult↗
PubMed2004

Identification of novel genes associated with the response to 5-FU treatment in gastric cancer cell lines using a cDNA microarray.

The 5-Fluorouracil (5-FU) is an anticancer drug that is widely used in the treatment of cancer. To identify novel genes associated with 5-FU in gastric cancer, the time-dependent expression profiling of genes in response to 5-FU was examined in 5-FU sensitive and/or resistant gastric cancer cell lines using a 'KUGI 14 K cDNA chip' containing 14,081 unigenes obtained from human gastric cancer cell lines and tissues. By this analysis, we obtained 13 genes which are directly associated with sensitivity or resistance to 5-FU. Of these genes, 11 were found to be commonly up-regulated only in the 5-FU sensitive cell lines, and 2 were oppositely regulated in both of 5-FU sensitive and resistant cell lines. These genes were determined to be involved in cell surface, apoptosis, cell cycle and signal transduction. Of these genes, the expression levels of ZFP100, 4F2hc, FLJ11021, CSTF3, PPP1R14A, DDB2, C6orf139, CDKN1A, HOXC11 and FLJ38860 were confirmed by semi-quantitative RT-PCR. In addition, seven genes containing RRMI, UP1 and K-EST0037597 were found to be commonly up-regulated in both cell lines. In addition, the expression of genes such as TP, OPRT, TS and DPD, which have been previously known to be involved in 5-FU metabolism, were examined in both of 5-FU sensitive and resistant cell lines. These results provide not only predictive biomarkers for 5-FU sensitivity or resistance to human gastric cancer, but also a new molecular basis for understanding the mechanism of cellular cytoxicity to 5-FU.

Antimetabolites, Antineoplastic↗
PubMed2006

Molecular profiling techniques and bioinformatics in cancer research.

AIMS: Our aim was to describe the commonly used molecular profiling techniques in cancer research, to examine their limitations and to discuss the challenges of bioinformatics. METHODS: A literature search was performed using the PubMed database to identify publications relevant to this review. Citations from these articles were also examined to yield further relevant publications. RESULTS: We describe the use of DNA microarrays, comparative genomic hybridisation, tissue microarrays and digital differential display. The limitations of these technologies, their contribution to cancer research and the challenges of bioinformatics are also discussed. CONCLUSIONS: Although these high throughput technologies each have their own limitations they are rapidly developing and contributing significantly to our understanding of cancer genetics. They have also led to the emergence of bioinformatics as a rapidly developing and vital field.

Biomarkers, Tumor↗
PubMed2006

Serum biomarkers for detection of breast cancers: A prospective study.

Using surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF), Li et al. [Clin Chem 48(8): 1296-1304, 2002] identified 3 serum biomarkers, BC1 (4.3 kDa), BC2 (8.1 kDa) and BC3 (8.9 kDa), whose combination significantly detects breast cancer patients from non-cancer controls. This work aimed to validate these biomarkers in an independent prospective study. We screened 89 serum samples including 49 breast cancers at pT1-4N0M0 (n = 23), pT1-4N1-3M0 (n = 17) or pT1-4N0-3M1 (n = 9) stages, 13 benign breast diseases and 27 healthy women. The BC2 biomarker significance was not recovered. However, we found 2 peaks that we named BC1a (4286 Da) and BC1b (4302 Da), that could correspond to Li's BC1 since they significantly decrease in breast cancers (p < 0.00007 and p < 0.0002, respectively). Similarly, BC3a (8919 Da) and BC3b (8961 Da) are significantly increased in breast cancers (p < 0.02 and p < 0.0002, respectively) and could correspond to the Li's BC3. For each biomarker we defined stringent (no errors) and flexible (less than 10% errors) cut-off values and tested the power of the combined BC1a/BC1b/BC3a/BC3b stringent and flexible profiles to discriminate breast cancers. They identified 33% and 45% cancers, respectively. Applied to the same series, Ca 15.3 test identified 22% patients. Interestingly, in association with the BC1a/BC1b/BC3a/BC3b profiles, Ca 15.3 improved the number of detected cancers indicating that it is an independent parameter. Collectively, our data partially validate those of Li's study and confirm that the BC1 and BC3 biomarkers are helpful for breast cancer diagnosis.

Adenocarcinoma, Mucinous↗
PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗