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PubMed2001

Coordinately up-regulated genes in ovarian cancer.

A better understanding of the molecular circuitry in normal ovarian tissues and in ovarian cancer will likely provide new targets for diagnosis and therapy. Recently, much has been learned about the genes expressed in ovarian cancer through studies with cDNA arrays and serial analysis of gene expression. However, these methods do not allow highly quantitative analysis of gene expression on a large number of specimens. Here, we have used quantitative real-time RT-PCR in a panel of 39 microdissected ovarian carcinomas of various subtypes to systematically analyze the expression of 13 genes, many of which were previously identified as up-regulated in a subset of ovarian cancers by serial analyses of gene expression. The genes analyzed are glutathione peroxidase 3 (GPX3), apolipoprotein J/clusterin, insulin-like growth factor-binding protein 2, epithelial cell adhesion molecule/GA733-2, Kop protease inhibitor, matrix gla protein, tissue inhibitor of metalloproteinase 3, folate receptor 1, S100A2, signal transducer and activator of transcription 1, secretory leukocyte protease inhibitor, apolipoprotein E, and ceruloplasmin. All of the genes were found overexpressed, some at extremely high levels, in the vast majority of ovarian carcinomas irrespective of the subtype. Interestingly, GPX3 was found at much higher levels in tumors with clear cell histology and may represent a biomarker for this subtype. Some of the genes studied here may thus represent targets for early detection ovarian cancer. The gene expression patterns were not associated with age at diagnosis, stage, or K-ras mutation status in ovarian cancer. We find that several genes are coordinately regulated in ovarian cancer, likely representing the fact that many genes are activated as part of common signaling pathways or that extensive cross-talk exists between several pathways in ovarian cancer. A statistical analysis shows that genes commonly up-regulated in ovarian cancer may result from the aberrant activation of a limited number of pathways, providing promising targets for novel therapeutic strategies.

Biomarkers, Tumor↗
PubMed2005

Identification of differentially expressed proteins in oral squamous cell carcinoma using a global proteomic approach.

A number of protein markers for oral cancer are still not applicable in large populations. Proteomic technologies provide excellent tools for rapid screening of a large number of potential biomarkers in malignant cells. To gain insight into the molecular mechanisms of carcinogenesis and to identify potential biomarkers for oral squamous cell carcinomas (OSCCs), we performed proteomic profiling between human normal oral keratinocytes (HNOKs) and OSCC-derived cell lines (HSC-2 and HSC-3) using fluorescent two-dimensional difference in-gel electrophoresis. Proteins with a > or =2-fold change in expression were considered significant. The spots of interest were digested and identified by matrix-assisted laser desorption/ionization time-of-flight peptide mass finger-printing. Twenty-two proteins were identified as differentially expressed between the HNOKs and OSCC-derived cell lines. Of these, 9 spots were up-regulated and 13 were down-regulated in OSCC-derived cell lines compared to the HNOKs. These spots included the cancer-related proteins; annexin A1, heat shock protein 27, lamin A/C, interleukin 1 receptor antagonist, serine proteinase inhibitor clade B5, stathmin 1, and superoxide dismutase 2. Our results are a first step toward identifying a protein profile of HNOKs and OSCC-derived cell lines. The identified proteins in this experiment may be used in future studies of carcinogenesis or as diagnostic markers and therapeutic targets for OSCC.

Adult↗
PubMed2006

Advances in molecular classification of renal neoplasms.

Kidney neoplasms are classified by light microscopy using the World Health Organization (WHO) system. The WHO system defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. In adults, the common malignant subtypes are variants of renal cell carcinoma (RCC). Histopathologic classification is critical for clinical management of RCC, but is becoming more complex with recognition of novel tumor subtypes, development of procedures yielding small diagnostic biopsies, and emergence of molecular therapies directed at tumor gene activity. Therefore, classification systems based on gene expression are likely to become essential for diagnosis, prognosis and treatment of kidney tumors. Recent DNA microarray studies have shown that clinically relevant renal tumor subtypes are characterized by distinct gene expression profiles, which are useful for discovery of novel diagnostic and prognostic biomarkers. In this review, we summarize the WHO classification system for renal tumors, general applications of microarray technology in cancer research, and specific microarray studies that have advanced knowledge of renal tumor diagnosis, prognosis, therapy and pathobiology.

Carcinoma, Renal Cell↗
PubMed2005

Survival trees for analyzing clinical outcome in lung adenocarcinomas based on gene expression profiles: identification of neogenin and diacylglycerol kinase alpha expression as critical factors.

We present survival trees as an exploratory tool for revealing new insights into gene expression profiles in combination with clinical patient data. Survival trees partition the patient data studied into groups with similar survival outcomes and identify characteristic genetic profiles within these groups. We demonstrate the application of survival trees in a study involving the expression profiles of 3,588 genes in 211 lung adenocarcinoma patients. The survival tree identified a group of early-stage cancer patients with relatively low survival rates and another group of advanced-stage patients with remarkably good survival outcome. For both groups, the tree identified characteristic expression profiles of genes that might play a role in cancerogenesis and disease progression, notably the genes for the netrin receptor neogenin and the Ras/Rho kinase modulator diacylglycerol kinase alpha.

Adenocarcinoma↗
PubMed2005

Determination of tumour marker genes from gene expression data.

Cancer classification has traditionally been based on the morphological study of tumours. However, tumours with similar histological appearances can exhibit different responses to therapy, indicating differences in tumour characteristics on the molecular level. Thus, development of a novel, reliable and precise method for classification of tumours is essential for more successful diagnosis and treatment. The high-throughput gene expression data obtained using microarray technology are currently being investigated for diagnostic applications. However, these large datasets introduce a range of challenges, making data analysis a major part of every experiment for any application, including cancer classification and diagnosis. One of the major concerns in the application of microarrays to tumour diagnostics is the fact that the expression levels of many genes are not measurably affected by carcinogenic changes in the cells. Thus, a crucial step in the application of microarrays to cancer diagnostics is the selection of diagnostic marker genes from the gene expression profiles. These molecular markers give valuable additional information for tumour diagnosis, prognosis and therapy development.

Biomarkers, Tumor↗
PubMed2006

Identification of tumor markers and differentiation markers for molecular diagnosis of lung adenocarcinoma.

To identify tumor markers and differentiation markers for lung adenocarcinoma (AdC), we analysed expression profiles of 14,500 genes against three cases of type II alveolar epithelial cells, bronchiolar epithelial cells, and bronchial epithelial cells, respectively, and 10 cases of AdC cells isolated by laser capture microdissection. Hierarchical clustering analysis indicated that AdC cells and noncancerous lung epithelial cells are significantly different in their expression profiles, and that different sets of differentiation markers are expressed among alveolar, bronchiolar and bronchial epithelial cells. Nine genes were identified as being highly expressed in AdC cells, but not expressed in noncancerous lung epithelial cells. Sixteen genes were identified as differentiation markers for lung epithelial cells. Real-time RT-PCR analysis of 45 lung AdC cases further revealed that expression of four tumor markers in AdC cells was significantly higher than that in noncancerous lung cells and that expression of ten differentiation markers was retained in a considerable fraction of lung AdC cases. Five tumor markers and seven differentiation markers were not expressed in peripheral blood cells. Similarities and differences in expression profiles between normal epithelial cells from different lung respiratory compartments and AdC cells demonstrated in this study will be informative for the molecular diagnosis of lung AdC.

Adenocarcinoma↗
PubMed2006

Identification of differentially expressed genes in human bladder cancer through genome-wide gene expression profiling.

Large-scale gene expression profiling is an effective strategy for understanding the progression of bladder cancer (BC). The aim of this study was to identify genes that are expressed differently in the course of BC progression and to establish new biomarkers for BC. Specimens from 21 patients with pathologically confirmed superficial (n = 10) or invasive (n = 11) BC and 4 normal bladder samples were studied; samples from 14 of the 21 BC samples were subjected to microarray analysis. The validity of the microarray results was verified by real-time RT-PCR. Of the 136 up-regulated genes we detected, 21 were present in all 14 BCs examined (100%), 44 in 13 (92.9%), and the other 71 in 12 BCs (85.7%). Of 69 down-regulated genes, 25 were found in all 14 BCs (100%), 22 in 13 (92.9%), and the other 22 in 12 BCs (85.7%). Functional annotation revealed that of the up-regulated genes, 36% were involved in metabolism and 14% in transcription and processing; 25% of the down-regulated genes were linked to cell adhesion/surface and 21% to cytoskeleton/cell membrane. Real-time RT-PCR confirmed the microarray results obtained for the 6 most highly up- and the 2 most highly down-regulated genes. Among the 6 most highly up-regulated genes, CKS2 was the only gene with a significantly greater level of up-regulation in invasive than in superficial BC (p = 0.04). To confirm this result, we subjected all 21 BC samples to real-time PCR assay for CKS2. We found a considerable difference between superficial and invasive BC (p = 0.001). Interestingly, there was a considerable difference between the normal bladder and invasive BC (p = 0.001) and less difference between the normal bladder and superficial BC (p = 0.005). We identified several genes as promising candidates for diagnostic biomarkers of human BC and the CKS2 gene not only as a potential biomarker for diagnosing, but also for staging human BC. This is the first report demonstrating that CKS2 expression is strongly correlated with the progression of human BC.

Aged↗
PubMed2006

Urinary profiling investigation of metabolites with cis-diol structure from cancer patients based on UPLC-MS and HPLC-MS as well as multivariate statistical analysis.

Nucleosides are an important class of metabolites and have been investigated as potential tumor biomarkers. A method based on ultra peerformance liquid chromatography (UPLC)-TOF MS was developed to analyze urinary nucleosides and other metabolites with cis-diol structure to distinguish between cancer patients and healthy persons and compare the results with those obtained by HPLC. The data showed that the UPLC method used about one third of the time required by HPLC and achieved a much better chromatographic resolution and increased sensitivity, the number of peaks detected by UV being 79 and 94 for HPLC and UPLC, respectively. With UPLC-TOF MS, more information was obtained about metabolites, the separation of cancer patients from healthy persons was significantly improved, and more potential biomarkers were found. The method based on UPLC-TOF MS is a powerful technique for the study of metabolite profiles.

Adult↗
PubMed2005

High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses.

Recent studies on gene molecular profiling using cDNA microarray in a relatively small series of breast cancer have identified biologically distinct groups with apparent clinical and prognostic relevance. The validation of such new taxonomies should be confirmed on larger series of cases prior to acceptance in clinical practice. The development of tissue microarray (TMA) technology provides methodology for high-throughput concomitant analyses of multiple proteins on large numbers of archival tumour samples. In our study, we have used immunohistochemistry techniques applied to TMA preparations of 1,076 cases of invasive breast cancer to study the combined protein expression profiles of a large panel of well-characterized commercially available biomarkers related to epithelial cell lineage, differentiation, hormone and growth factor receptors and gene products known to be altered in some forms of breast cancer. Using hierarchical clustering methodology, 5 groups with distinct patterns of protein expression were identified. A sixth group of only 4 cases was also identified but deemed too small for further detailed assessment. Further analysis of these clusters was performed using multiple layer perceptron (MLP)-artificial neural network (ANN) with a back propagation algorithm to identify key biomarkers driving the membership of each group. We have identified 2 large groups by their expression of luminal epithelial cell phenotypic characteristics, hormone receptors positivity, absence of basal epithelial phenotype characteristics and lack of c-erbB-2 protein overexpression. Two additional groups were characterized by high c-erbB-2 positivity and negative or weak hormone receptors expression but showed differences in MUC1 and E-cadherin expression. The final group was characterized by strong basal epithelial characteristics, p53 positivity, absent hormone receptors and weak to low luminal epithelial cytokeratin expression. In addition, we have identified significant differences between clusters identified in this series with respect to established prognostic factors including tumour grade, size and histologic tumour type as well as differences in patient outcomes. The different protein expression profiles identified in our study confirm the biologic heterogeneity of breast cancer and demonstrate the clinical relevance of classification in this manner. These observations could form the basis of revision of existing traditional classification systems for breast cancer.

Breast Neoplasms↗
PubMed2006

Therapeutic targets and biomarkers identified in cancer choline phospholipid metabolism.

Choline phospholipid metabolism is altered in a wide variety of cancers. The choline metabolite profile of tumors and cancer cells is characterized by an elevation of phosphocholine and total choline-containing compounds. Noninvasive magnetic resonance spectroscopy can be used to detect this elevation as an endogenous biomarker of cancer, or as a predictive biomarker for monitoring tumor response to novel targeted therapies. The enzymes directly causing this elevation, such as choline kinase, phospholipase C and phospholipase D may provide molecular targets for anticancer therapies. Signal transduction pathways that are activated in cancers, such as those mediated by the receptor tyrosine kinases breakpoint cluster region-abelson (Bcr-Abl), c-KIT or epidermal growth factor receptor (EGFR), correlate with the alterations in choline phospholipid metabolism of cancers, and also offer molecular targets for specific anticancer therapies. This review summarizes recently discovered molecular targets in choline phospholipid metabolism and signal transduction pathways, which may lead to novel anticancer therapies potentially being monitored by magnetic resonance spectroscopy techniques.

Animals↗
PubMed2005

Gliomas: advances in molecular analysis and characterization.

BACKGROUND: Gliomas represent the most common primary brain tumor. Despite recent advances in diagnostic imaging, neurosurgical technique, radiation therapy, and chemotherapy, significant advances in accurate prognosis and improved survival have not been achieved. Nevertheless, new developments in molecular biology could have potential impact on the clinical management of patients with these brain tumors. This review will describe the technological advances being used to enrich the classification of gliomas, present specific studies that have successfully used the new technologies to identify molecular subtypes of glioblastoma, and discuss the implications of such enhanced classification and molecular characterizations for the prediction of therapeutic response and the design of future brain tumor therapies. RESULTS: Innovative techniques using complementary DNA and oligonucleotide microarrays (gene chips), tissue microarrays (tissue chips), and differential immunoabsorption have provided high throughput and potentially comprehensive approaches for the molecular characterization of human gliomas. Alterations of several tumor suppressor genes and oncogenes have already been identified as being critical to glioma transformation and progression. These approaches have led to the subclassification of glioblastoma multiforme into distinct subtypes based on the molecular signatures of the tumors. CONCLUSIONS: Classifications of gliomas can now be enhanced with new techniques for comprehensive molecular characterization. Improved and efficient molecular profiling of brain tumors is advancing diagnosis/prognosis and identifying targets for novel and rational therapeutic approaches. Neurosurgeons and neuro-oncologists should be aware of these new developments so they can better advise and treat their patients.

Biomarkers, Tumor↗
PubMed2006

Immunohistochemical classification of de novo, transformed, and relapsed diffuse large B-cell lymphoma into germinal center B-cell and nongerminal center B-cell subtypes correlates with gene expression profile and patient survival.

CONTEXT: Diffuse large B-cell lymphoma (DLBCL) can be assigned to prognostic subgroups, including germinal center B-cell (GCB) and activated B-cell subgroups, by using gene expression profiling and, reportedly, immunohistochemistry for CD10, Bcl-6, and multiple myeloma-1/interferon regulatory factor-4 (MUM1/IRF4). OBJECTIVE: To compare 2 commercial MUM1/IRF4 antibody formulations for accuracy in subtyping DLBCL against gene expression profiling, compare subtyping to patient survival, and evaluate the usefulness of GCB and non-GCB subtyping in relapsed and transformed DLBCL. DESIGN: Evaluation of 2 commercial MUM1/IRF4 antibodies, ICSTAT/M17 and Mum-1p, by using 40 cases of de novo, relapsed, and transformed DLBCL; and comparison of the results obtained with gene expression profiling and survival. RESULTS: Immunohistochemistry predicted the gene expression profiling subtype 71.8% and 69.2% of the time overall with use of the Mum-1p and ICSTAT/M17 antibodies, respectively, and 100% and 91.7% of the time when MUM1/IRF4 expression determined subtype. Gene expression profiling and immunohistochemistry revealed nearly identical 5-year overall survival rates for the GCB vs non-GCB subtypes (68.0% for GCB vs 24.7% for non-GCB with use of gene expression profiling [P = .03] and 70.2% vs 18.4%, respectively, with use of immunohistochemistry [P < .001]). When de novo, transformed, and relapsed cases were analyzed separately, 5-year overall survival rates were also significantly different. CONCLUSIONS: Immunohistochemistry can be used to subclassify DLBCL, including a very small series of transformed and relapsed cases, into GCB and non-GCB subtypes and predict survival rates similar to those predicted by use of gene expression profiling. The 2 MUM1/IRF4 antibodies performed similarly.

B-Lymphocytes↗
PubMed2005

Congenital heart defects and abnormal maternal biomarkers of methionine and homocysteine metabolism.

BACKGROUND: It is well established that folic acid prevents neural tube defects. Although the mechanisms remain unclear, multivitamins containing folic acid may also protect against other birth defects, including congenital heart defects. OBJECTIVE: Our goal was to establish a maternal metabolic risk profile for nonsyndromic congenital heart defects that would enhance current preventive strategies. DESIGN: Using a case-control design, we measured biomarkers of the folate-dependent methionine and homocysteine pathway among a population-based sample of women whose pregnancies were affected by congenital heart defects (224 case subjects) or unaffected by any birth defect (90 control subjects). Plasma concentrations of folic acid, homocysteine, methionine, S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), vitamin B-12, and adenosine were compared, with control for lifestyle and sociodemographic variables. RESULTS: After covariate adjustment, case subjects had higher mean concentrations of homocysteine (P < 0.001) and SAH (P < 0.001) and lower mean concentrations of methionine (P = 0.019) and SAM (P = 0.014) than did control subjects. Vitamin B-12, folic acid, and adenosine concentrations did not differ significantly between case and control subjects. Homocysteine, SAH, and methionine were identified as the most important biomarkers predictive of case or control status. CONCLUSIONS: The basis for the observed abnormal metabolic profile among women whose pregnancies were affected by congenital heart defects cannot be defined without further analysis of relevant genetic and environmental factors. Nevertheless, a metabolic profile that is predictive of congenital heart defect risk would help to refine current nutritional intervention strategies to reduce risk and may provide mechanistic clues for further experimental studies.

Adult↗
PubMed2026

Profiler: an open web platform for multi-omics analysis.

MOTIVATION: High-throughput multi-omics technologies produce increasingly large and heterogeneous datasets that are difficult to analyze without advanced computational expertise. Existing bioinformatics tools are often fragmented or limited to specific omics types, hindering reproducibility and accessibility. There is a critical need for an integrated, user-friendly, and scalable platform capable of supporting multi-omics analyses across different data modalities. RESULTS: We present Profiler, an open-source, modular platform that unifies data import, quality control, preprocessing, statistical testing, machine and deep learning, biomarker discovery, pathway and drug-target enrichment, and survival modeling within a single reproducible environment. Built in Python with Streamlit, Profiler is available as both a web-based platform deployed on high-performance computing and a desktop version for local execution, enabling flexible usage across computational infrastructures. Profiler supports diverse omics modalities, including proteomics, transcriptomics, lipidomics, and electroencephalogram data. Through applications to glioblastoma proteomic, pancancer, and multi-omics datasets, Profiler reproduced known molecular subtypes, revealed potential therapeutic targets, and generated fully traceable analysis reports within minutes. By integrating advanced analytics behind an intuitive interface, Profiler democratizes multi-omics analysis and provides a robust, scalable foundation for systems biology and precision medicine research. AVAILABILITY AND IMPLEMENTATION: Profiler is open-source and freely available via its web platform (https://prism-profiler.univ-lille.fr) and GitHub (web version: https://github.com/yanisZirem/Profiler_v1_requests_datatests, desktop version: https://github.com/yanisZirem/prism-profiler), and archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.17478158).

Software↗
PubMed2006

Detection of hypopharyngeal squamous cell carcinoma using serum proteomics.

CONCLUSIONS: The combination of surface-enhanced laser desorption/ionization (SELDI) with bioinformatics tools could help find serum proteome biomarkers and establish a predictive model for early detection of hypopharyngeal squamous cell carcinoma (HSCC). OBJECTIVES: Proteomic profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an emerging technique to identify new biomarkers in biological fluids and to establish clinically useful diagnostic computational models. We used it to find new potential biomarkers and to establish a predictive model for early detection of HSCC. MATERIALS AND METHODS: One hundred serum samples including 48 from HSCC patients and 52 from normal controls which were divided into a training set and a blind testing set were treated on WCX2 and IMAC3 protein chip, and serum protein or peptide patterns were detected by SELDI-TOF-MS. The data of spectra were analyzed by Biomarker Wizard software to screen serum proteome biomarkers of HSCC. A decision tree classification algorithm and blind validation were determined by Biomarker Pattern Software (BPS). RESULTS: Ranging from 2 to 30 kDa, 45 potential biomarkers could differentiate HSCC patients from normal controls (p < 0.05). Among them four candidate protein peaks with m/z values of 7796, 4216, 5927, and 5361Da were selected to establish a predictive model by BPS with sensitivity of 94% and specificity of 89%. A sensitivity of 92% and specificity of 82% were validated in the blind testing set.

Adult↗
PubMed2004

Gene expression profiling for molecular staging and prognosis prediction in colorectal cancer.

A key issue for patients undergoing surgery for colorectal cancer is the accurate prediction of treatment outcome. Currently, classification of a tumor by histopathologic stage is the most accurate prognostic factor for the risk assessment of treatment failure. However, despite improved histologic techniques and the application of novel immunohistochemical and molecular techniques, it is still not possible to delineate the underlying biochemical and genetic events that predict clinical outcome for individual cancer patients. One reason for this lack of progress is that the factors which determine the metastatic potential of a primary tumor are still unknown. This reality, coupled to dramatic technological developments in the field of expression profiling, has started a paradigm shift in the staging of colorectal cancers. It has raised expectations that genetic and/or transcriptome profiling of the primary tumor will result in the identification of prognostic determinants relevant to the individual patient. In turn, this may allow a clinically relevant definition of patient subgroups based on individual molecular parameters for rational decision making regarding choice of therapy.

Biomarkers, Tumor↗