Efforts aimed at deciphering the molecular basis of complex disease are underpinned by the availability of high throughput strategies for the identification of biomolecules that drive the disease process. The completion of the human genome-sequencing project, coupled to major technological developments, has afforded investigators myriad opportunities for multidimensional analysis of biological systems. Nowhere has this research explosion been more evident than in the field of transcriptomics. Affordable access and availability to the technology that supports such investigations has led to a significant increase in the amount of data generated. As most biological distinctions are now observed at a genomic level, a large amount of expression information is now openly available via public databases. Furthermore, numerous computational based methods have been developed to harness the power of these data. In this review we provide a brief overview of in silico methodologies for the analysis of differential gene expression such as Serial Analysis of Gene Expression and Digital Differential Display. The performance of these strategies, at both an operational and result/output level is assessed and compared. The key considerations that must be made when completing an in silico expression analysis are also presented as a roadmap to facilitate biologists. Furthermore, to highlight the importance of these in silico methodologies in contemporary biomedical research, examples of current studies using these approaches are discussed. The overriding goal of this review is to present the scientific community with a critical overview of these strategies, so that they can be effectively added to the tool box of biomedical researchers focused on identifying the molecular mechanisms of disease.
Gliomas are the most common and aggressive primary brain tumors for which unfortunately no effective treatment modalities exist despite advances in molecular biology as the knowledge base to unravel the extremely complex molecular mechanisms of tumorigenesis is limited. In this study an attempt has been made to understand the molecular pathological basis of tumorigenesis which led to an identification of an oncogene, CDC2, and an epigenetic strategy has been evaluated to control the tumorigensis by downregulating this oncogene.
Tissue microarrays were utilized to investigate the expression of genes in a large number of tumor samples and to identify overexpressed genes which could be potentially causing tumorigenesis. Retroviral vectors expressing short hairpin RNAs (shRNAs) targeted against CDC2 were designed and transducted into human glioma cell line ex vivo in order to downregulate the expression of CDC2. Real-Time PCR was used to determine the level of CDC2 mRNA. Western Blotting was used to determine the level of expression of CDC2 protein as measure to quantify down regulation of CDC2 expression along with use of flow cytometry to investigate effect of shRNAs on cell cycles and detection of apoptosis. Following ex vivo study, viral particles containing small interfering RNA for CDC2 were subsequently injected into xenogeneic graft tumor of nude mice and the weight of human glioma xenografts, survival and resulting phenotypic changes of target gene were investigated.
Human glioma tissue microarrays indicated the positive expression rates of CDC2/CyclinB1 with a positive correlation with pathologic grades (r = 0.982, r = 0.959, respectively). Retroviral vectors expressing short hairpin RNAs (shRNAs) against CDC2 caused efficient deletion of CDC2, cellular G2/M arrest concluding in apoptosis and inhibition of proliferation in human glioma cells U251 and SHG-44 cell lines ex vivo. And the viral particles containing small interfering RNA for CDC2 were subsequently injected into subcutaneous and intracranial xenogeneic graft tuomrs of nude mice. For subcutaneous tumors, injection of CDC2-shRNA retroviruses significantly decreased tumor weight and volume compared with control. Immunohistochemistry indicated that CDC2 are negative and TUNEL are positive in tumors treated with recombinant retrovirus. For mice implanted with intracranial gliomas, treatment of CDC2-shRNA retroviruses increased survival times compared with control.
CDC2 gene plays an important role in the proliferation of human gliomas. Downregulation of CDC2 could potentialy inhibit human gliomas cells growth ex vivo and in vivo. From these results, it was suggested that CDC2 might be a potential target on gene therapy of human gliomas.
Classical Hodgkin lymphoma is considered a highly curable disease; however, 20% of patients cannot be cured with standard first-line chemotherapy and have a dismal outcome. Current clinical parameters do not allow accurate risk stratification, and personalized therapies are lacking. In fact, Hodgkin lymphoma (HL) is often over- or undertreated because of this lack of accurate risk stratification. In recent years, the early detection of chemoresistance by fluorodeoxyglucose positron emission tomography has become the most important prognostic tool in the management of HL. However, to date, no prognostic scores or molecular markers are available for the early identification of patients at very high risk of failure of induction therapy. In the last decade, many important advances have been made in understanding the biology of HL. In particular, the development of new molecular profiling technologies, such as SNP arrays, comparative genomic hybridization, and gene-expression profiling, have allowed the identification of new prognostic factors that may be useful for risk stratification and predicting response to chemotherapy. In this review, we focus on the prognostic tools and biomarkers that are available for newly diagnosed HL, and we highlight recent advances in the genomic characterization of classical HL and potential targets for therapy.
Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies.
We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic (miR-21 and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs were assessed.
In the array experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue.
Our study demonstrates that the top six most stably expressed miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) described herein should be validated as suitable reference genes in both high-throughput and lower throughput RT-qPCR colorectal miRNA studies.
Colorectal carcinoma continues to be a leading cause of cancer morbidity and mortality despite widespread adoption of screening methods. Targeted detection and therapy using recent advances in our knowledge of in vivo cancer biomarkers promise to significantly improve methods for early detection, risk stratification, and therapeutic intervention. The behavior of molecular targets in transformed tissues is being comprehensively assessed using new techniques of gene expression profiling and high throughput analyses. The identification of promising targets is stimulating the development of novel molecular probes, including significant progress in the field of activatable and peptide probes. These probes are being evaluated in small animal models of colorectal neoplasia and recently in the clinic. Furthermore, innovations in optical imaging instrumentation are resulting in the scaling down of size for endoscope compatibility. Advances in target identification, probe development, and novel instruments are progressing rapidly, and the integration of these technologies has a promising future in molecular medicine.
colon; adenocarcinoma; targeted; molecular imaging; biomarker; endoscopy
Underlying molecular genetic mechanisms of diseases can be deciphered with unbiased strategies using recently developed technologies enabling genome-wide scale investigations. These technologies have been applied in scanning for genetic variations, gene expression profiles, and epigenetic changes for oral and craniofacial diseases. However, these approaches as applied to oral and craniofacial conditions are in the initial stages, and challenges remain to be overcome, including analysis of high throughput data and their interpretation. Here, we review methodology and studies using genome-wide approaches in oral and craniofacial diseases and suggest future directions.
genome-wide; genomics; craniofacial; microarray
To provide a critical overview of gene expression profiling methodology and discuss areas of future development.
Gene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges.
Study Design and Setting:
We consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.
Some studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.
microarray; gene expression profiling; classification; experimental design; technical challenges; statistical issues
Advances in molecular biology and recombinant DNA technologies have contributed to our understanding of the molecular basis of many diseases. Now the possibility of gene transfer into normal cells to produce a gene product of therapeutic potential, or into diseased cells to correct the pathologic alteration, promises to revolutionize medical practice. In contemporary medicine, many therapeutic strategies focus on the link between a biochemical deficiency and the ensuing disorder. The treatment of noninfectious disease is often based on replacement therapy; medication is given to compensate for biochemical defects and to prevent or reverse the progression of disease. Although conventional therapies seldom alter the fundamental cause of a disease, gene therapy potentially could correct, at a molecular level, the genetic abnormalities contributing to its pathogenesis. Treatment directed at specific molecular alterations associated with the development of neurologic disease provides expectations of more effective and less toxic therapy. The development of gene therapy for nervous system tumors has progressed rapidly and may be prototypical in the development of therapies for inherited and acquired disorders of the nervous system. We describe possible strategies for using gene therapy to treat nervous system disorders, and we review recent advances in gene therapy for nervous system tumors.
Advances in DNA sequencing technologies, as well as refined bioinformatics methods for interpretation of complex datasets, have provided the opportunity to comprehensively assess gene expression in tumours and their surrounding microenvironment. More recently, these advances have highlighted the interplay between the immune effector mechanisms and breast cancer cell biology, emphasizing the long-recognized link between immunity and cancer. Studying immune-associated genes has not only resulted in further stratification within the broad pathological types of breast cancers, but also provided further biological insights into the complex heterogeneity within breast cancer subgroups. On the basis that anti-cancer therapies can modify the host-tumour interaction, investigators have focused their attention on the predictive value of immune parameters as markers of therapeutic anti-tumour response. We discuss the current status of immune signatures in breast cancer and some of the fundamental limitations that need to be overcome to move these discoveries into clinic.
Genomic biomarkers; Immune stromal interface; Breast cancer
Cervical cancer (CC) as a single diagnostic entity exhibits differences in clinical behavior and poor outcomes in response to therapy in advanced tumors. Although infection of high-risk human papillomavirus is recognized as an important initiating event in cervical tumorigenesis, stratification of CC into subclasses for progression and response to treatment remains elusive. Existing knowledge of genetic, epigenetic and transcriptional alterations is inadequate in addressing the issues of diagnosis, progression and response to treatment. Recent technological advances in high-throughput genomics and the application of integrative approaches have greatly accelerated gene discovery, facilitating the identification of molecular targets. In this article, we discuss the results obtained by preliminary integrative analysis of DNA copy number increases and gene expression, utilizing the two most common copy number-gained regions of 5p and 20q in identifying gene targets in CC. These analyses provide insights into the roles of genes such as RNASEN, POLS and SKP2 on 5p, KIF3B, RALY and E2F1 at 20q11.2 and CSE1L, ZNF313 and B4GALT5 at 20q13.13. Future integrative applications using additional datasets, such as mutations, DNA methylation and clinical outcomes, will raise the promise of accomplishing the identification of biological pathways and molecular targets for therapies for patients with CC.
amplification; cervical carcinoma; chromosome 5p; chromosome 20q; chromosome alteration; gene expression; integrative genomics; precancerous lesion; single nucleotide polymorphism array
Metabolomics is the systematic study of the unique chemical fingerprints of small-molecules, or metabolite profiles, that are related to a variety of cellular metabolic processes in a cell, organ, or organism. While mRNA gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell, metabolic profiling provides direct and indirect physiologic insights that can potentially be detectable in a wide range of biospecimens. Although not specific to cardiac conditions, translating metabolomics to cardiovascular biomarkers has followed the traditional path of biomarker discovery from identification and confirmation to clinical validation and bedside testing. With technological advances in metabolomic tools (such as nuclear magnetic resonance spectroscopy and mass spectrometry) and more sophisticated bioinformatics and analytical techniques, the ability to measure low-molecular-weight metabolites in biospecimens provides a unique insight into established and novel metabolic pathways. Systemic metabolomics may provide physiologic understanding of cardiovascular disease states beyond traditional profiling, and may involve descriptions of metabolic responses of an individual or population to therapeutic interventions or environmental exposures.
Diffuse large B cell lymphoma (DLBCL) is a subtype of non-Hodgkin lymphoma characterized by a markedly heterogeneous clinical course and response to therapy that is not appreciated with standard histopathologic and immunophenotypic evaluations. Recent studies have focused on the use of genome-scale expression profiles that provide a snap fingerprint of the tumor and identifying tumors with similar genetic alterations and clinical features. Gene expression studies have the ability to recognize distinct subgroups of patients based on similar molecular characteristics and markedly different outcomes that were independent of the International Prognostic Index (IPI). Further, DNA microarray studies also allow identification of new prognostic biomarkers in DLBCL. However, new methods for immunohistochemical analysis of tissue microarray and RNA extraction from paraffin-embedded blocks are required to overcome the major pitfall of this technology - the requirement for fresh tissue. Herein, we summarize the progress made in better prediction of prognosis of DLBCL patients as a result of gene expression profiling.
Using high-throughput gene-expression profiling technology, we can now gain a better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup of the cancer cells. This abnormal genetic makeup can have profound effects on cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeutic agents and radiation. These approaches will lead to better molecular subclassification of tumours, the basis of personalized medicine. We have, to date, done whole-genome microarray gene-expression profiling on several hundreds of kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used to identify both cytogenetic abnormalities and molecular pathways that are deregulated in renal cell carcinoma (RCC). For example, we have identified the deregulation of the VHL-hypoxia pathway in clear-cell RCC, as previously known, and the c-Myc pathway in aggressive papillary RCC. Besides the more common clear-cell, papillary and chromophobe RCCs, we are currently characterizing the molecular signatures of rarer forms of renal neoplasia such as carcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosome Xp11 translocations associated with papillary RCC, renal medullary carcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response.
Conventional clinical and pathologic risk factors in stage II colon cancer provide limited prognostic information and do not predict response to adjuvant 5-fluorouracil-based chemotherapy. New prognostic and predictive biomarkers are needed to identify patients with highest recurrence risk who will receive the greatest absolute risk reduction from adjuvant chemotherapy. We review below the evidence for conventional risk factors in node-negative colon cancer patients, followed by a discussion of promising new molecular and genetic markers in this malignancy. Gene expression profiling is an emerging tool with both prognostic and predictive potential in oncology. For stage II colon cancer patients, the Oncotype DX Colon Cancer test is now commercially available as a prognostic marker, and the ColoPrint assay is expected to be released later this year. Current evidence for both of these assays is described below, concluding with a discussion of potential future directions for gene expression profiling in colon cancer risk stratification and treatment decision-making.
Stage II colon cancer; gene signature; gene expression profile; biomarker; recurrence
Unravelling the path from genotype to phenotype, as it is influenced by an organism's environment, is one of the central goals in biology. Gene expression profiling by means of microarrays has become very prominent in this endeavour, although resources exist only for relatively few model systems. As genomics has matured into a comparative research program, expression profiling now also provides a powerful tool for non-traditional model systems to elucidate the molecular basis of complex traits.
Here we present a microarray constructed with ~4500 features, derived from a brain-specific cDNA library for the African cichlid fish Astatotilapia burtoni (Perciformes). Heterologous hybridization, targeting RNA to an array constructed for a different species, is used for eight different fish species. We quantified the concordance in gene expression profiles across these species (number of genes and fold-changes). Although most robust when target RNA is derived from closely related species (<10 MA divergence time), our results showed consistent profiles for other closely related taxa (~65 MA divergence time) and, to a lesser extent, even very distantly related species (>200 MA divergence time).
This strategy overcomes some of the restrictions imposed on model systems that are of importance for evolutionary and ecological studies, but for which only limited sequence information is available. Our work validates the use of expression profiling for functional genomics within a comparative framework and provides a foundation for the molecular and cellular analysis of complex traits in a wide range of organisms.
In recent years, omic analyses have been proposed as possible approaches to diagnosis, in particular for tumours, as they should be able to provide quantitative tools to detect and measure abnormalities in gene and protein expression, through the evaluation of transcription and translation products in the abnormal vs normal tissues. Unfortunately, this approach proved to be much less powerful than expected, due to both intrinsic technical limits and the nature itself of the pathological tissues to be investigated, the heterogeneity deriving from polyclonality and tissue phenotype variability between patients being a major limiting factor in the search for unique omic biomarkers. Especially in the last few years, the application of refined techniques for investigating gene expression in situ has greatly increased the diagnostic/prognostic potential of histochemistry, while the progress in light microscopy technology and in the methods for imaging molecules in vivo have provided valuable tools for elucidating the molecular events and the basic mechanisms leading to a pathological condition. Histochemical techniques thus remain irreplaceable in pathologist's armamentarium, and it may be expected that even in the future histochemistry will keep a leading position among the methodological approaches for clinical pathology.
histochemistry; clinical pathology
Deciphering olfactory encoding requires a thorough description of the ligands that activate each odorant receptor (OR). In mammalian systems, however, ligands are known for fewer than 50 of over 1400 human and mouse ORs, greatly limiting our understanding of olfactory coding. We performed high-throughput screening of 93 odorants against 464 ORs expressed in heterologous cells and identified agonists for 52 mouse and 10 human ORs. We used the resulting interaction profiles to develop a predictive model relating physicochemical odorant properties, OR sequences, and their interactions. Our results provide a basis for translating odorants into receptor neuron responses and unraveling mammalian odor coding.
Diffuse gliomas are a heterogenous group of neoplasms traditionally classified as grades II to IV based on histologic features, and with prognosis determined mainly by histologic grade and pretreatment clinical factors. Our understanding of the molecular basis of glioma initiation, tumor progression, and treatment failure is rapidly evolving. A molecular profile of diffuse gliomas is emerging. Studies evaluating gene expression and DNA methylation profile have found multiple glioma subtypes and an association between subtype and survival. The recent discovery of isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) mutations in glioma has provided reproducible prognostic biomarkers and novel therapeutic targets. Glioblastomas that exhibit CpG island hypermethylator phenotype, proneural gene expression, or IDH1 mutation identify a subset of patients with markedly improved prognosis. Accumulated evidence supports the stratification of both low-grade and anaplastic diffuse gliomas into prognostic groups using 1p/19q codeletion and IDH mutation status. A classification scheme incorporating clinical, pathologic, and molecular information may facilitate improved prognostication for patients treated in the clinic, the development of more effective clinical trials, and rational testing of targeted therapeutics.
Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.
To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome.
In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides.
Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.
Rheumatic diseases are a diverse group of disorders. Most of these diseases are heterogeneous in nature and show varying responsiveness to treatment. Because our understanding of the molecular complexity of rheumatic diseases is incomplete and criteria for categorization are limited, we mainly refer to them in terms of group averages. The advent of DNA microarray technology has provided a powerful tool to gain insight into the molecular complexity of these diseases; this technology facilitates open-ended survey to identify comprehensively the genes and biological pathways that are associated with clinically defined conditions. During the past decade, encouraging results have been generated in the molecular description of complex rheumatic diseases, such as rheumatoid arthritis, systemic lupus erythematosus, Sjögren syndrome and systemic sclerosis. Here, we describe developments in genomics research during the past decade that have contributed to our knowledge of pathogenesis, and to the identification of biomarkers for diagnosis, patient stratification and prognostication.
An update on the most recent data on promising biological prognostic and/or predictive markers in patients with colorectal cancer is provided.
Rapidly growing insights into the molecular biology of colorectal cancer (CRC) and recent developments in gene sequencing and molecular diagnostics have led to high expectations for the identification of molecular markers to be used in optimized and tailored treatment regimens. However, many of the published data on molecular biomarkers are contradictory in their findings and the current reality is that no molecular marker, other than the KRAS gene in the case of epidermal growth factor receptor (EGFR)- targeted therapy for metastatic disease, has made it into clinical practice. Many markers investigated suffer from technical shortcomings, resulting from lack of quantitative techniques to capture the impact of the molecular alteration. This understanding has recently led to the more comprehensive approaches of global gene expression profiling or genome-wide analysis to determine prognostic and predictive signatures in tumors. In this review, an update of the most recent data on promising biological prognostic and/or predictive markers, including microsatellite instability, epidermal growth factor receptor, KRAS, BRAF, CpG island methylator phenotype, cytotoxic T lymphocytes, forkhead box P3–positive T cells, receptor for hyaluronic acid–mediated motility, phosphatase and tensin homolog, and T-cell originated protein kinase, in patients with CRC is provided.
Colorectal cancer; Biomarkers; Prognostic; Overall survival; Disease-free survival
The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.
biomarker; bladder cancer detection; molecular signature; oncoproteomics; urinalysis
Scientific advances are raising expectations that patient-tailored treatment will soon be available. The development of resulting clinical approaches needs to be based on well-designed experimental and observational procedures that provide data to which proper biostatistical analyses are applied. Gene expression microarray and related technology are rapidly evolving. It is providing extremely large gene expression profiles containing many thousands of measurements. Choosing a subset from these gene expression measurements to include in a gene expression signature is one of the many challenges needing to be met. Choice of this signature depends on many factors, including the selection of patients in the training set. So the reliability and reproducibility of the resultant prognostic gene signature needs to be evaluated, in such a way as to be relevant to the clinical setting. A relatively straightforward approach is based on cross validation, with separate selection of genes at each iteration to avoid selection bias. Within this approach we developed two different methods, one based on forward selection, the other on genes that were statistically significant in all training blocks of data. We demonstrate our approach to gene signature evaluation with a well-known breast cancer data set.
DNA microarray technology allows for a quick and easy comparison of complete transcriptomes, resulting in improved molecular insight in fluctuations of gene expression. After emergence of the microarray technology about a decade ago, the technique has now matured and has become routine in many molecular biology laboratories. Numerous studies have been performed that have provided global transcription patterns of many organisms under a wide range of conditions. Initially, implementation of this high-throughput technology has lead to high expectations for ground breaking discoveries. Here an evaluation is performed of the insight that transcriptome analysis has brought about in the field of hyperthermophilic archaea. The examples that will be discussed have been selected on the basis of their impact, in terms of either biological insight or technological progress.