Cytologic and DNA-cytometric early diagnosis of oral cancer.

OBJECTIVE: The aim of this prospective study was to report on the diagnostic accuracy of conventional oral exfoliative cytology taken from white-spotted, ulcerated or other suspicious oral lesions in our clinic. In addition we checked DNA-image cytometry as an adjuvant diagnostic tool. Our hypothesis is that DNA-aneuploidy is a sensitive and specific marker for the early identification of tumor cells in oral brushings. STUDY DESIGN: 251 cytological diagnoses obtained from exfoliative smears of 181 patients from macroscopically suspicious lesions of the oral mucosa and from clinically seemingly benign oral lesions which were excised for establishing histological diagnoses were compared with histological and/or clinical follow-ups of the respective patients. Additionally nuclear DNA-contents were measured after Feulgen restaining using a TV image analysis system. RESULTS: Sensitivity of our cytological diagnosis on oral smears for the detection of cancer cells was 94.6%, specificity 99.5%, positive predictive value 98.1% and negative predictive value 98.5%. DNA-aneuploidy was assumed if abnormal DNA-stemlines or cells with DNA-content greater 9c were observed. On this basis the prevalence of DNA-aneuploidy in smears of oral squamous cell carcinomas in situ or invasive carcinomas was 96.4%. Sensitivity of DNA-aneuploidy in oral smears for the detection of cancer cells was 96.4%, specificity 100%, positive predictive value 100% and negative 99.0%. The combination of both techniques increased the sensitivity to 98.2%, specificity to 100%, positive predictive value to 100% and negative to 99.5%. CONCLUSIONS: Brush cytology of all visible oral lesions, if they are clinically considered as suspicious for cancer, are an easily practicable, cheap, non-invasive, painless, safe and accurate screening method for detection of oral precancerous lesions, carcinoma in situ or invasive squamous cell carcinoma in all stages. We conclude that DNA-image cytometry is a very sensitive, highly specific and objective adjuvant tool for the early identification of neoplastic epithelial cells in oral smears.     

Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data--a potential tool for multi-parametric diagnosis

A method for predictive metabolite profiling based on resolution of GC-MS data followed by multivariate data analysis is presented and applied to three different biofluid data sets (rat urine, aspen leaf extracts, and human blood plasma). Hierarchical multivariate curve resolution (H-MCR) was used to simultaneously resolve the GC-MS data into pure profiles, describing the relative metabolite concentrations between samples, for multivariate analysis. Here, we present an extension of the H-MCR method allowing treatment of independent samples according to processing parameters estimated from a set of training samples. Predictions or inclusion of the new samples, based on their metabolite profiles, into an existing model could then be carried out, which is a requirement for a working application within, e.g., clinical diagnosis. Apart from allowing treatment and prediction of independent samples the proposed method also reduces the time for the curve resolution process since only a subset of representative samples have to be processed while the remaining samples can be treated according to the obtained processing parameters. The time required for resolving the 30 training samples in the rat urine example was approximately 13 h, while the treatment of the 30 test samples according to the training parameters required only approximately 30 s per sample (approximately 15 min in total). In addition, the presented results show that the suggested approach works for describing metabolic changes in different biofluids, indicating that this is a general approach for high-throughput predictive metabolite profiling, which could have important applications in areas such as plant functional genomics, drug toxicity, treatment efficacy and early disease diagnosis.     

Holistic metabonomic profiling of urine affords potential early diagnosis for bladder and kidney cancers

The clinical exploration of urinary metabonomic analysis on discriminating between the top-two-incidence urological cancers, bladder and kidney cancers (BC and KC), is still virgin land. In this study, we discovered and evaluated the clinical utility of holistic metabonomic profiling and current single biomarker methods, and ultimately suggested a potential screening test for BC and KC. Urine metabonomic profiling for 19 BC patients, 25 KC patients, and 24 healthy controls was carried out using an LC–MS based platform, which utilized both reversed phase chromatography and hydrophilic interaction chromatography. The holistic method that refers to orthogonal partial least-squares-discriminant analysis based on all qualified profile data, successfully classified BC, KC and healthy control groups, showing 100 % sensitivity and specificity. Taurine, hippuric acid, phenylacetylglutamine and carnitine species contributed most to the BC and KC discrimination. The predictive power of each above metabolite is evaluated using receiver operator characteristic technique. Hippuric acid was found 10-fold decrease in concentration relative healthy controls, and performed the best as a biomarker for BC diagnosis, with its sensitivity and specificity of 78.9 and 86.5 %, respectively. Carnitine C10:3 was found 1.5-fold decrease, and reached 84.0 % of sensitivity and 60.5 % of specificity for KC diagnosis. In view of both sensitivity and specificity, the holistic method is more accurate for detecting BC and KC, than current single metabonomic biomarker methods, and it could be advocated as a prescreen to other forms of more invasive or uncomfortable screening. Moreover, this approach also demonstrates attractive performance in diagnosis of early stage (ES) BC and KC patients.     

Factors influencing early diagnosis of cancer of the oral cavity

Factors associated with stage at time of diagnosis and with interval between recognition of the first symptom and histologic diagnosis were assessed in a consecutive series of patients with primary epithelial tumours of the oral cavity. Of the 160 patients 55% had stage I or II disease. The proportion was significantly higher among patients with a high socioeconomic status, those with low levels of alcohol consumption and those who regularly received dental care. The interval between recognition of the first symptom and diagnosis was not significantly related to these factors, but it was shorter for the men. These relations were specific to the patients with cancer of the oral cavity, not being seen in those with other head and neck tumours. Dental practitioners are an important source of early diagnosis of oral cavity cancers. The impact of the disease might thus be lessened by more regular dental care.     

Application of L-EDA in metabonomics data handling: global metabolite profiling and potential biomarker discovery of epithelial ovarian cancer prognosis

Solution capacity limited estimation of distribution algorithm (L-EDA) is proposed and applied to ovarian cancer prognosis biomarker discovery to expatiate on its potential in metabonomics studies. Sera from healthy women, epithelial ovarian cancer (EOC), recurrent EOC and non-recurrent EOC patients were analyzed by liquid chromatography-mass spectrometry. The metabolite data were processed by L-EDA to discover potential EOC prognosis biomarkers. After L-EDA filtration, 78 out of 714 variables were selected, and the relationships among four groups were visualized by principle component analysis, it was observed that with the L-EDA filtered variables, non-recurrent EOC and recurrent EOC groups could be separated, which was not possible with the initial data. Five metabolites (six variables) with P?<?0.05 in Wilcoxon test were discovered as potential EOC prognosis biomarkers, and their classification accuracy rates were 86.9% for recurrent EOC and non-recurrent EOC, and 88.7% for healthy?+?non-recurrent EOC and EOC?+?recurrent EOC. The results show that L-EDA is a powerful tool for potential biomarker discovery in metabonomics study.     

The potential of metabolite profiling as a selection tool for genotype discrimination in Populus

Differences between wild-type Populus tremulaxalba and two transgenic lines with modified lignin monomer composition, were interrogated using metabolic profiling. Analysis of metabolite abundance data by GC-MS, coupled with principal components analysis (PCA), successfully differentiated between lines that had distinct phenotypes, whether samples were taken from the cambial zone or non-lignifying suspension tissue cultures. Interestingly, the GC-MS analysis detected relatively few phenolic metabolites in cambial extracts, although a single metabolite associated with the differentiation between lines was directly related to the phenylpropanoid pathway or other down-stream aspects of lignin biosynthesis. In fact, carbohydrates, which have only an indirect relationship with the modified lignin monomer composition, featured strongly in the line-differentiating aspects of the statistical analysis. Traditional HPLC analysis was employed to verify the GC-MS data. These findings demonstrate that metabolic traits can be dissected reliably and accurately by metabolomic analyses, enabling the discrimination of individual genotypes of the same tree species that exhibit marked differences in industrially relevant wood traits. Furthermore, this validates the potential of using metabolite profiling techniques for marker generation in the context of plant/tree breeding for industrial applications.     

miRNA profiling for diagnosis and prognosis of human cancer

MicroRNAs (miRNAs) are a recently discovered class of small (approximately 18-24 nt) nucleic acids that negatively regulate gene expression. This novel class of molecules modulates a wide array of growth and differentiation processes in human cancers. High throughput analyses, utilizing the solid phase, array platform, or liquid phase, bead-based hybridization have variously demonstrated that miRNA expression was commonly dysregulated in human cancer. miRNA expression profiling has shown promise in defining malignant status in retrospective studies. Considerable disagreement remains with respect to the miRNA signature for a specific cancer cell type, which appears to depend largely on the analytical platform. Nonetheless, various internally controlled studies have successfully identified the histotype of tumors of unknown origin according to miRNA expression profile. The evaluation of miRNAs expression may also be of prognostic value, as best exemplified by the correlation of let-7 and mir-155 levels with disease survival in nonsmall cell lung cancer.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

miRNA expression profiling for identification of potential breast cancer biomarkers

To identify micro RNA (miRNA) biomarker candidates for early detection of breast cancer and detection of minimal residual breast cancer, we performed miRNA expression profiling in pooled RNA samples from breast tumors, and from bone marrow mononuclear cells, peripheral blood mononuclear cells and plasma from healthy controls. We found substantially higher levels of five miRNAs in the breast tumors compared to the normal samples. However, validation of these miRNA levels, and seven other candidates selected from the literature, in individual samples from healthy controls and patients with non-metastatic breast cancer did not suggest further examination of their biomarker potential.     

O-glycan profiling of serum glycan for potential renal cancer biomarkers

Serum was obtained from 25 male renal cell carcinoma (RCC) patients and 21 healthy males. O-glycans were released by a β-elimination reaction and purified by graphitized carbon cartridge solid phase extraction, then profiled by matrix-assisted laser desorption/ionisation-time of flight mass spectrometry. After noise removal and peak alignment, 1372 peaks were extracted from 200000 data points. Feature peaks were analyzed by calculation of differential sensitivity and specificity. The combination of two feature peaks was chosen as a biomarker and could clearly differentiate RCC and normal samples in our study group.     

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.     

Humoral immunity directed against tumor-associated antigens as potential biomarkers for the early diagnosis of cancer

Over the past decade, it has been demonstrated that cancer is immunogenic, and multiple tumor antigens have been identified in cancer patients. It is now possible to potentially harness the immune response elicited by cancer growth as a potential diagnostic tool. Humoral immunity, or the development of autoantibodies against tumor-associated proteins, may be used as a marker for cancer exposure. Unlike circulating proteins that are shed by bulky tumors, serum autoantibodies are detectable even when antigen expression is minimal. This paper will review the methods used for tumor antigen discovery and overview what is known about autoantibodies targeting common cancer antigens with a focus on breast cancer. Data will be presented modeling the use of tumor antigen associated autoantibodies as a breast cancer diagnostic. The endogenous humoral immune response present in cancer patients may allow the identification of individuals exposed to the malignant transformation of somatic cells.     

In vivo 13C NMR metabolite profiling: potential for understanding and assessing conifer seed quality

High-resolution 13C MAS NMR spectroscopy was used to profile a range of primary and secondary metabolites in vivo in intact whole seeds of eight different conifer species native to North America, including six of the Pinaceae family and two of the Cupressaceae family. In vivo 13C NMR provided information on the total seed oil content and fatty acid composition of the major storage lipids in a non-destructive manner. In addition, a number of monoterpenes were identified in the 13C NMR spectra of conifer seeds containing oleoresin; these compounds showed marked variability in individual seeds of Pacific silver fir within the same seed lot. In imbibed conifer seeds, the 13C NMR spectra showed the presence of considerable amounts of dissolved sucrose presumed to play a protective role in the desiccation-tolerance of seeds. The free amino acids arginine and asparagine, generated as a result of storage protein mobilization, were detected in vivo during seed germination and early seedling growth. The potential for NMR to profile metabolites in a non-destructive manner in single conifer seeds and seed populations is discussed. It is a powerful tool to evaluate seed quality because of its ability to assess reserve accumulation during seed development or at seed maturity; it can also be used to monitor reserve mobilization, which is critical for seedling emergence.     

前列腺癌生物标记与早期诊断

前列腺癌发病率高,肿瘤晚期难以治愈,这就致使早期诊断变得尤为重要。生物标记应用于诊断和治疗已经有50年的历史了,当今前列腺特异性抗原(PSA)作为唯一应用于临床的标记,一套依赖于PSA的诊断系统已经建立。然而标记本身的缺陷限制了系统的应用。基因组学和蛋白质组学技术的发展大大加速了前列腺癌相关生物分子的研究,为发现前列腺癌的生物标记提供了广阔的前景。以前列腺癌发生的遗传背景为基础,对目前的检测标记以及检测标记的标准作了介绍。