Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Diagnosis of colorectal cancer using Raman spectroscopy of laser-trapped single living epithelial cells

A single-cell diagnostic technique for epithelial cancers is developed by utilizing laser trapping and Raman spectroscopy to differentiate cancerous and normal epithelial cells. Single-cell suspensions were prepared from surgically removed human colorectal tissues following standard primary culture protocols and examined in a near-infrared laser-trapping Raman spectroscopy system, where living epithelial cells were investigated one by one. A diagnostic model was built on the spectral data obtained from 8 patients and validated by the data from 2 new patients. Our technique has potential applications from epithelial cancer diagnosis to the study of cell dynamics of carcinogenesis. (c) 2006 Optical Society of America.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Co-expression of CD173 (H2) and CD174 (Lewis Y) with CD44 suggests that fucosylated histo-blood group antigens are markers of breast cancer-initiating cells

Histo-blood group antigens CD173 (H2) and CD174 (Lewis Y) are known to be developmentally regulated carbohydrate antigens which are expressed to a varying degree on many human carcinomas. We hypothesized that they might represent markers of cancer-initiating cells (or cancer stem cells, CSC). In order to test this hypothesis, we examined the co-expression of CD173 and CD174 with stem cell markers CD44 and CD133 by flow cytometry analysis, immunocytochemistry, and immunohistochemistry on cell lines and tissue sections from breast cancer. In three breast cancer cell lines, the percentage of CD173(+)/CD44(+) cells ranged from 17% to > 60% and of CD174(+)/CD44(+) from 21% to 57%. In breast cancer tissue sections from 15 patients, up to 50% of tumor cells simultaneously expressed CD173, CD174, and CD44 antigens. Co-expression of CD173 and CD174 with CD133 was also observed, but to a lesser percentage. Co-immunoprecipitation and sandwich ELISA experiments on breast cancer cell lines suggested that CD173 and CD174 are carried on the CD44 molecule. The results show that in these tissues CD173 (H2) and CD174 (LeY) are associated with CD44 expression, suggesting that these carbohydrate antigens are markers of cancer-initiating cells or of early progenitors of breast carcinomas.

A Cancer Recognition Method Based on DNA Microarray

The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on Biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Pre-irradiation with low-dose C-12(6+) beam significantly enhances the efficacy of AdCMV-p53 gene therapy in human non-small lung cancer

The combination of ionizing radiation and gene therapy has been investigated. However, there are very few reports about the combination of heavy-ion irradiation and gene therapy. To determine if the pre-exposure to low-dose heavy ion beam enhances the suppression of AdCMV-p53 on non-small lung cancer (NSLC), the cells pre-irradiated or non-irradiated were infected with 20, 40 MOI of AdCMV-p53. Survival fraction and the relative biology effect (RBE) were determined by clonogenic assay. The results showed that the proportions of p53 positive cells in C-12(6+) beam induced AdCMV-p53 infected cells were more than 90%, which were significantly more than those in gamma-ray induced AdCMV-p53 infected cells. The pre-exposure to low-dose 12C6+ beam significantly prevented the G(0)/G(1) arrest and activated G(2)/M checkpoints. The pre-exposure to C-12(6+) beam significantly improved cell to apoptosis. RBEs for the C-12(6+)+ AdCMV-p53 infection groups were 30%-60%,20% -130% and 30%-70% more than those for the C-12(6+)_irradiated only, AdCMV-p53 infected only, and gamma-irradiation induced AdCMVp53 infected groups, respectively. The data suggested that the pre-exposure to low-dose C-12(6+) beam significantly promotes exogenous p53 expression in NSLC, and the suppression of AdCMV-p53 gene therapy on NSLC.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Aberrant expression of X-linked genes RbAp46, Rsk4, and Cldn2 in breast cancer

The consequence of activation status or gain/loss of an X-chromosome in terms of the expression of tumor suppressor genes or oncogenes in breast cancer has not been clearly addressed. In this study, we investigated the activation status of the X-chromosomes in a panel of human breast cancer cell lines, human breast carcinoma, and adjacent mammary tissues and a panel of murine mammary epithelial sublines ranging from low to high invasive potentials. Results show that most human breast cancer cell lines were homozygous, but both benign cell lines were heterozygous for highly polymorphic X-loci (IDS and G6PD). On the other hand, 60% of human breast carcinoma cases were heterozygous for either IDS or G6PD markers. Investigation of the activation status of heterozygous cell lines revealed the presence of only one active X-chromosome, whereas most heterozygous human breast carcinoma cases had two active X-chromosomes. Furthermore, we determined whether or not an additional active X-chromosome affects expression levels of tumor suppressor genes and oncogenes. Reverse transcription-PCR data show high expression of putative tumor suppressor genes Rsk4 and RbAp46 in 47% and 79% of breast carcinoma cases, respectively, whereas Cldn2 was down-regulated in 52% of breast cancer cases compared with normal adjacent tissues. Consistent with mRNA expression, immunostaining for these proteins also showed a similar pattern. In conclusion, our data suggest that high expression of RbAp46 is likely to have a role in the development or progression of human breast cancer. The activation status of the X-chromosome may influence the expression levels of X-linked oncogenes or tumor suppressor genes.

Reversed-phase high-performance liquid chromatographic investigation of urinary normal and modified nucleosides of cancer patients

Post-transcriptional modifications in RNA give rise to free modified ribonucleosides circulating in the blood stream and excreted in urine. Due to their abnormal levels in conjunction with several tumor diseases, they have been suggested as possible tumor markers. The developed RP-HPLC method has been applied to analyze the urinary nucleosides in 34 urinary samples from 15 kinds of cancer patients. The statistical analyses showed the urinary nucleoside excretion, especially modified nucleoside levels, in cancer patients were significantly higher than those in normal healthy volunteers. Factor analysis was used to classify the patients with cancer and normal healthy humans. It was found that using 15 urinary nucleoside levels or only five modified nucleoside levels as data vectors the factor analysis plot displayed two almost separate clusters representing each group. (C) 1999 Elsevier Science B.V. All rights reserved.