Keywords: Spin-Valve Sensors ; Cell Tracking Velocimetry ; On-A-Chip ; Polymerase-Chain-Reaction ; Total Analysis Systems ; Iron-Oxide Nanoparticles ; Field-Flow Fractionation ; Cross-Coupling Reactions ; Circulating Tumor-Cells ; Mode Magnetophoretic Microseparator Reference LMIS2-ARTICLE-2010-004doi:10.1021/cr9001929View record in Web of Science Record created on 2010-01-20, modified on 2016-08-08

Microfluidic applications of magnetic particles for biological analysis and catalysis.

Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all cancers of the thyroid. It is also one of the few cancers with a rapidly increasing incidence. PTC is usually contained within the thyroid gland and generally biologically indolent. Prognosis of the cancer is excellent, with less than 2% mortality at 5 years. However, more than 25% of patients with PTC developed a recurrence during a long term follow-up. The present article provides an updated condensed overview of PTC, which focuses mainly on the molecular alterations involved and recent biomarker investigations.

/pdf/papillary-thyroid-cancer-genetic-alterations-and-molecular-wdyjm9xpxr.pdf

Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations.

Rapid DNA multi-analyte immunoassay on a magneto-resistance biosensor

Papillary thyroid carcinoma (PTC) is the fastest-growing disease caused by numerous molecular alterations in addition to previously reported DNA mutations. There is a compelling need to identify novel transcriptomic alterations that are associated with the pathogenesis of PTC with potential diagnostic and prognostic implications. Methods: We gathered and compared 242 expression profiles between paired PTC and adjacent normal tissues and identified and validated the coding and long non-coding RNAs (lncRNAs) associated with the extrathyroidal extension (ETE) of 655 PTC patients in two independent cohorts, followed by predicting their interactions with drugs. Co-expression, RNA interaction, Kaplan-Meier survival and multivariate Cox proportional regression analyses were performed to identify dysregulated lncRNAs and genes that correlated with clinical outcomes of PTC. Alternative splicing (AS), RNA circularization, and editing were also compared between transcriptomes to expand the repertoire of molecular alterations in PTC. Results: Numerous genes related to cellular microenvironment and steroid hormone response were associated with the ETE of PTC. Drug susceptibility predictions of the expression signature revealed two highly ranked compounds, 6-bromoindirubin-3'-oxime and lovastatin. Co-expression and RNA interaction analysis revealed the essential role of lncRNAs in PTC pathogenesis by modulating extracellular matrix and cell adhesion. Eight genes and two novel lncRNAs were identified that correlated with the aggressive nature and disease-free survival of PTC. Furthermore, this study provided the transcriptome-wide landscape of circRNAs in PTC and uncovered dissimilar expression profiles among circRNAs originating from the same host gene, suggesting the functional complexity of circRNAs in PTC carcinogenesis. The newly identified AS events in the SERPINA1 and FN1 genes may improve the sensitivity and specificity of these diagnostic biomarkers. Conclusions: Our study uncovered a comprehensive transcriptomic signature associated with the carcinogenesis and aggressive behavior of PTC, as well as presents a catalog of 10 potential biomarkers, which would facilitate PTC prognosis and development of new therapeutic strategies for this cancer.

Transcriptomic signature associated with carcinogenesis and aggressiveness of papillary thyroid carcinoma.

To evaluate the expression and differential diagnostic significance of CK19, TG, Ki67 and galectin-3 in papillary thyroid carcinoma (PTC) (metastatic and non metastatic), follicular adenoma and nodular goiter in patients from the northeastern part of China. 441 PTC specimens and 151 other benign thyroid specimens (97 cases of nodular goiter, 54 cases of nonmalignant follicular adenoma) were collected. Immunohistochemistry for CK19, TG, Ki67 and galectin-3 was performed. CK19, TG, Ki67 and galectin-3 expression was 96.37% (425/441), 82.77% (365/441), and 40.59% (179/441), 96.82% (427/441), respectively, for the PTC group and the expression of these markers in the benign thyroid lesions group was 25.83% (39/151), 79.47% (120/151), and 37.09% (56/151), 50.99% (77/151), respectively. The expression of CK19 and galectin-3 in PTC was much higher than that in the nonmalignant group (p 0.05). The diagnostic efficiency of CK19 and galectin-3 for PTC was 96.37% (537/592) and 84.63% (501/592). CK19 and galectin-3 expression rate in PTC was higher than that in benign disease cases. The diagnostic efficiency of CK19 for PTC was slightly better than galectin-3. The utilization of these markers combined with morphologic evaluation may be helpful in the differential diagnosis of papillary thyroid carcinoma in the northeastern region of China.

/pdf/diagnostic-significance-of-ck19-tg-ki67-and-galectin-3-4cco9dfdei.pdf

Diagnostic significance of CK19, TG, Ki67 and galectin-3 expression for papillary thyroid carcinoma in the northeastern region of China

Acceleration of incubation processes in DNA bio chips by magnetic particles

Background: Several DNA microarray based expression signatures for the different clinically relevant thyroid tumor entities have been described over the past few years. However, reproducibility of these signatures is generally low, mainly due to study biases, small sample sizes and the highly multivariate nature of microarrays. While there are new technologies available for a more accurate high throughput expression analysis, we show that there is still a lot of information to be gained from data deposited in public microarray databases. In this study we were aiming (1) to identify potential markers for papillary thyroid carcinomas through meta analysis of public microarray data and (2) to confirm these markers in an independent dataset using an independent technology. Methods: We adopted a meta analysis approach for four publicly available microarray datasets on papillary thyroid carcinoma (PTC) nodules versus nodular goitre (NG) from N2-frozen tissue. The methodology included merging of datasets, bias removal using distance weighted discrimination (DWD), feature selection/inference statistics, classification/crossvalidation and gene set enrichment analysis (GSEA). External Validation was performed on an independent dataset using an independent technology, quantitative RT-PCR (RT-qPCR) in our laboratory. Results: From meta analysis we identified one gene (SERPINA1) which identifies papillary thyroid carcinoma against benign nodules with 99% accuracy (n = 99, sensitivity = 0.98, specificity = 1, PPV = 1, NPV = 0.98). In the independent validation data, which included not only PTC and NG, but all major histological thyroid entities plus a few variants, SERPINA1 was again markedly up regulated (36-fold, p = 1:3*10 -10 ) in PTC and identification of papillary carcinoma was possible with 93% accuracy (n = 82, sensitivity = 1, specificity = 0.90, PPV = 0.76, NPV = 1). We also show that the extracellular matrix pathway is strongly activated in the meta analysis data, suggesting an important role of tumor-stroma interaction in the carcinogenesis of papillary thyroid carcinoma. Conclusions: We show that valuable new information can be gained from meta analysis of existing microarray data deposited in public repositories. While single microarray studies rarely exhibit a sample number which allows robust feature selection, this can be achieved by combining published data using DWD. This approach is not only efficient, but also very cost-effective. Independent validation shows the validity of the results from this meta analysis and confirms SERPINA1 as a potent mRNA marker for PTC in a total (meta analysis plus validation) of 181 samples.

/pdf/identification-of-serpina1-as-single-marker-for-papillary-3oq630fpo1.pdf

Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation

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Acceleration of incubation processes in DNA bio chips by magnetic particles

Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation