RESUMEN
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.
RESUMEN
Purpose: Prunella vulgaris (PV), a traditional Chinese medicine, has been used to treat patients with thyroid disease for centuries in China. The purpose of the present study was to investigate its bioactive ingredients and mechanisms against Hashimoto's thyroiditis (HT) using network pharmacology and molecular docking technology to provide some basis for experimental research. Methods: Ingredients of the PV formula were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Additionally, HT-related genes were retrieved from the UniProt and GeneCards databases. Cytoscape constructed networks for visualization. A protein-protein interaction (PPI) network analysis was constructed, and a PPI network was built using the Search Tool for the Retrieval of Interacting Genes (STRING) database. These key targets of PV were enriched and analyzed by molecular docking verification, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Results: The compound-target network included 11 compounds and 66 target genes. Key targets contained Jun proto-oncogene (JUN), hsp90aa1.1 (AKI), mitogen-activated protein kinase 1 (MAPK1), and tumor protein p53 (TP53). The main pathways included the AGE-RAGE signaling pathway, the TNF signaling pathway, the PI3K-Akt signaling pathway, and the mitogen-activated protein kinase signaling pathway. The molecular docking results revealed that the main compound identified in the Prunella vulgaris was luteolin, followed by kaempferol, which had a strong affinity for HT. Conclusion: Molecular docking studies indicated that luteolin and kaempferol were bioactive compounds of PV and might play an essential role in treating HT by regulating multiple signaling pathways.
RESUMEN
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.
RESUMEN
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.
