Identification of a 3-Gene Prognostic Index for Papillary Thyroid Carcinoma.

The accurate determination of the risk of cancer recurrence is a critical unmet need in managing thyroid cancer (TC). Although numerous studies have successfully demonstrated the use of high throughput molecular diagnostics in TC prediction, it has not been successfully applied in routine clinical use, particularly in Chinese patients. In our study, we objective to screen for characteristic genes specific to PTC and establish an accurate model for diagnosis and prognostic evaluation of PTC. We screen the differentially expressed genes by Python 3.6 in The Cancer Genome Atlas (TCGA) database. We discovered a three-gene signature Gap junction protein beta 4 (GJB4), Ripply transcriptional repressor 3 (RIPPLY3), and Adrenoceptor alpha 1B (ADRA1B) that had a statistically significant difference. Then we used Gene Expression Omnibus (GEO) database to establish a diagnostic and prognostic model to verify the three-gene signature. For experimental validation, immunohistochemistry in tissue microarrays showed that thyroid samples' proteins expressed by this three-gene are differentially expressed. Our protocol discovered a robust three-gene signature that can distinguish prognosis, which will have daily clinical application.

Prioritizing Susceptible Genes for Thyroid Cancer Based on Gene Interaction Network.

Thyroid cancer ranks second in the incidence rate of endocrine malignant cancer. Thyroid cancer is usually asymptomatic at the initial stage, which makes patients easily miss the early treatment time. Combining genetic testing with imaging can greatly improve the diagnostic efficiency of thyroid cancer. Researchers have discovered many genes related to thyroid cancer. However, the effects of these genes on thyroid cancer are different. We hypothesize that there is a stronger interaction between the core genes that cause thyroid cancer. Based on this hypothesis, we constructed an interaction network of thyroid cancer-related genes. We traversed the network through random walks, and sorted thyroid cancer-related genes through ADNN which is fusion of Adaboost and deep neural network (DNN). In addition, we discovered more thyroid cancer-related genes by ADNN. In order to verify the accuracy of ADNN, we conducted a fivefold cross-validation. ADNN achieved AUC of 0.85 and AUPR of 0.81, which are more accurate than other methods.

ALDH5A1 acts as a tumour promoter and has a prognostic impact in papillary thyroid carcinoma.

Thyroid cancer is the most common endocrine carcinoma, with papillary thyroid carcinoma (PTC) accounting for 80%-90% of thyroid cancers. Accumulating studies reported that mitochondria plays an important role in the regulation of cell proliferation. ALDH5A1, may function as an oncogene or tumour suppressor in various human cancers, and the role of ALDH5A1 in PTC is still unclear. The aim of this study was to investigate the clinical significance of ALDH5A1 expression and its functions in PTC. In this present study, we studied ALDH5A1 expression on primary papillary thyroid carcinoma (PTC) in The Cancer Genome Atlas (TCGA) database. Results showed that the levels of ALDH5A1 were found positively correlated with tumour stage, metastasis, lymph node stage, and higher levels of ALDH5A1 demonstrated poor disease-free survival (DFS). Immunohistochemistry (IHC) revealed that significantly higher expression of ALDH5A1 was found in PTC tissues. On the other hand, knockdown of ALDH5A1 significantly inhibited PTC cell proliferation, migration and invasion detection found the migration and invasion of cells also were hindered when ALDH5A1 level was reduced. The knockdown of ALDH5A1 inhibited the expression of Vimentin and promoted the expression of E-cadherin. In brief, knockdown of ALDH5A1may act as a novel molecular target for the prevention and treatment of PTC. SIGNIFICANCE OF THE STUDY: The present study focused on the role and the potential mechanism of ALDH5A1 in papillary thyroid carcinoma. We demonstrated that reduced expression of ALDH5A1 might inhibit the progression of TC by inhibiting cell proliferation, migration and invasion and reversing epithelial-mesenchymal transition (EMT). The findings ensured the interaction relation between ALDH5A1 and EMT in PTC, providing a novel biological marker for PTC and enriching the potential strategies for TC treatment.

MicroRNA-3690 promotes cell proliferation and cell cycle progression by altering DKK3 expression in human thyroid cancer.

An increasing amount of evidence has demonstrated the importance of microRNAs (miRNAs/miRs) in the tumorigenesis of malignant types of cancer, and data retrieved from The Cancer Genome Atlas database revealed that miR-3690 was upregulated in thyroid cancer (TC). The present study focused on the biological function and mechanism of miR-3690 in TC, demonstrating that miR-3690 expression was significantly elevated in TC cells and clinical tissues. Functional studies indicated that miR-3690 acted as an oncogene in TC by promoting cell proliferation, colony formation and cell cycle progression in association with the increased expression of cyclin E and c-myc. Mechanistically, prediction software indicated that Dickkopf-related protein 3 (DKK3) was a target of miR-3690, which was confirmed by the results of luciferase reporter assays and western blotting. DKK3 silencing abrogated the functions of miR-3690-in on TC cell proliferation. Collectively, the findings of the present study demonstrated that miR-3690 promoted TC cell proliferation and indicated miR-3690 as a potential biomarker and therapeutic target for TC.

Potential five-mRNA signature model for the prediction of prognosis in patients with papillary thyroid carcinoma.

Although the mortality rate of papillary thyroid carcinoma (PTC) is relatively low, the recurrence rates of PTC remain high. The high recurrence rates are related to the difficulties in treatment. Gene expression profiles has provided novel insights into potential therapeutic targets and molecular biomarkers of PTC. The aim of the present study was to identify mRNA signatures which may categorize PTCs into high-and low-risk subgroups and aid with the predictions for prognoses. The mRNA expression profiles of PTC and normal thyroid tissue samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed mRNAs were identified using the 'EdgeR' software package. Gene signatures associated with the overall survival of PTC were selected, and enrichment analysis was performed to explore the biological pathways and functions of the prognostic mRNAs using the Database for Visualization, Annotation and Integration Discovery. A signature model was established to investigate a specific and robust risk stratification for PTC. A total of 1,085 differentially expressed mRNAs were identified between the PTC and normal thyroid tissue samples. Among them, 361 mRNAs were associated with overall survival (P<0.05). A 5-mRNA prognostic signature for PTC (ADRA1B, RIPPLY3, PCOLCE, TEKT1 and SALL3) was identified to classify the patients into high-and low-risk subgroups. These prognostic mRNAs were enriched in Gene Ontology terms such as 'calcium ion binding', 'enzyme inhibitor activity', 'carbohydrate binding', 'transcriptional activator activity', 'RNA polymerase II core promoter proximal region sequence-specific binding' and 'glutathione transferase activity', and Kyoto Encyclopedia of Genes and Genomes signaling pathways such as 'pertussis', 'ascorbate and aldarate metabolism', 'systemic lupus erythematosus', 'drug metabolism-cytochrome P450 and 'complement and coagulation cascades'. The 5-mRNA signature model may be useful during consultations with patients with PTC to improve the prediction of their prognosis. In addition, the prognostic signature identified in the present study may reveal novel therapeutic targets for patients with PTC.

Targeting of TLE3 by miR-3677 in human breast cancer promotes cell proliferation, migration and invasion.

Numerous studies have indicated an important function of microRNAs (miRs) in breast cancer (BC) progression, oncogenesis and metastasis. However, the function of miR-3677, which has been revealed to be upregulated in BC [The Cancer Genome Atlas (TCGA) data], has not been investigated to date. In the present study, miR-3677 was revealed to be upregulated in BC as determined using TCGA. miR-3677 was significantly upregulated in BC tissues and cell lines compared with those noted in adjacent non-cancerous tissues and primary normal breast cells (P<0.05). The overexpression of miR-3677 promoted the cell proliferation, migration and invasion of BC cells. Using bioinformatics algorithms and luciferase assays, a novel target gene for miR-3677, namely transducin-like enhancer of Split3 (TLE3), was identified. Silencing of TLE3 in miR-3677-transfected BC cells suppressed their proliferation and migration. An inverse correlation was observed between miR-3677 and TLE3 expression levels in human BC tissues. In conclusion, the present study demonstrated that miR-3677 promoted BC cell proliferation, migration and invasion by inhibiting TLE3 expression, which provided a novel mechanism and a promising therapeutic target for patients with BC.

[Identification of Dendrobium varieties by Fourier transform infrared spectroscopy combined with spectral retrieval].

The infrared spectral of stems of 165 trees of 23 Dendrobium varieties were obtained by means of Fourier transform infrared spectroscopy technique. The spectra show that the spectra of all the samples were similar, and the main components of stem of Dendrobium is cellulose. By the spectral professional software Omnic8.0, three spectral databases were constructed. Lib01 includes of the average spectral of the first four trees of every variety, while Lib02 and Lib03 are constructed from the first-derivative spectra and the second-derivative spectra of average spectra, separately. The correlation search, the square difference retrieval and the square differential difference retrieval of the spectra are performed with the spectral database Lib01 in the specified range of 1 800-500 cm(-1), and the yield correct rate of 92.7%, 74.5% and 92.7%, respectively. The square differential difference retrieval of the first-derivative spectra and the second-derivative spectra is carried out with Lib02 and Lib03 in the same specified range 1 800-500 cm(-1), and shows correct rate of 93.9% for the former and 90.3% for the later. The results show that the first-derivative spectral retrieval of square differential difference algorithm is more suitabe for discerning Dendrobium varieties, and FTIR combining with the spectral retrieval method can identify different varieties of Dendrobium, and the correlation retrieval, the square differential retrieval, the first-derivative spectra and second-derivative spectra retrieval in the specified spectral range are effective and simple way of distinguishing different varieties of Dendrobium.