Prognostic Value and Significant Pathway Exploration Associated with TOP2A Involved in Papillary Thyroid Cancer.

BACKGROUND: Topoisomerase 2-alpha (TOP2A) has been identified as a hub gene that played an important role in the initiation and progression of thyroid carcinoma (THCA). However, the exact function of TOP2A in papillary thyroid cancer (PTC) remained elusive. The current study aimed to evaluate the TOP2A expression, prognosis significance and key signaling pathways involved in PTC. METHODS: We firstly evaluated the expression of TOP2A in PTC via UALCAN, cBioportal, HPA and LinkdedOmics databases. Genetic alteration of TOP2A in PTC was then explored in cBioportal. Prognostic impacts of TOP2A expression on disease-free survival (DFS) of PTC patients were subsequently evaluated using Kaplan-Meier plotter and Gepia databases. Taking gender, age, cancer stage, T, N and M stages into consideration, we compared survival difference between TOP2A high and low expression groups. KEGG pathway analysis in WebGestalt and GSEA analysis were further performed to reveal the potential TOP2A-associated signaling pathways involved in PTC. Finally, the upstream microRNAs of TOP2A were assessed using DIANA, TargetScan, miRDB and miRWALK database, followed by mechanism exploration of upstream microRNAs. RESULTS: 1) The mRNA and protein of TOP2A were highly expressed in PTC tissue compared with normal thyroid tissue. TOP2A expression was associated with patient's age, N stage and cancer stage (all P<0.05). TOP2A protein was mainly localized to nucleoplasm. 2) Most of samples occurred the missense substitution, and mutation site was located at K1199E. Nucleotide mutations were mainly presented as G>A (35.29%). 3) TOP2A high expression significantly influenced the DFS of PTC patients (P=0.015). Restricted survival analysis showed that TOP2A high expression caused poorer DFS of female patients (P=0.003) and those with age <60 years old (P=0.002), early clinical stage (P=0.012), N0 stage (P=0.002) or M0 stage (P=0.040). 4) Pathway analysis suggested that TOP2A positively participated in the cell cycle, oocyte meiosis and p53 signaling pathways (all P<0.05) involved in thyroid cancer. CONCLUSION: The expression of TOP2A was higher in PTC tissue, which resulted in a worse DFS of patients with PTC. TOP2A might act as an effective therapeutic target for PTC treatment.

Immune response biomarker profiling application on ProtoArray protein microarrays.

The development of autoantibodies is observed in autoimmune disorders and numerous cancers. Consequently, autoantibodies form the basis of potential diagnostic and prognostic assays, as well as approaches for monitoring disease progression and treatment response. The effective use of autoantigen biomarkers for these applications, however, is contingent upon the identification of not one but multiple biomarkers. This is a consequence of the observation that the development of autoantibodies to any given protein is typically seen only in a fraction of patients. We have previously demonstrated the utility of functional protein microarrays containing thousands of different human proteins (ProtoArrays) for discovering novel autoimmune biomarkers in serum and plasma. Here, we describe a protocol for detecting autoantibodies in urine.

Small molecule selectivity and specificity profiling using functional protein microarrays.

Small molecules interact with proteins to perturb their functions, a property that has been exploited both for research applications and to produce therapeutic agents for disease treatment. Commonly utilized approaches for identifying the target proteins for a small molecule have limitations in terms of throughput and resource consumption and lack a mechanism to broadly assess the selectivity profile of the small molecule. Here we describe how protein microarray technology can be applied to the study of small molecule-protein interactions using tritiated small molecules. Protein arrays comprising thousands of full-length functional proteins facilitate target identification for those small molecules discovered in cell-based phenotypic assays and both target validation and off-target binding assessment for compounds discovered in target-based screens. The assays are highly reproducible, sensitive, and scalable, and provide an enabling technology for small molecule selectivity profiling in the context of drug development.

Small molecule protein interaction profiling with functional protein microarrays.

Small molecules possess the ability to interact with proteins and perturb their specific functions, a property that has been exploited for numerous research applications and to produce therapeutic agents in disease treatment. However, commonly utilized mass spectrometry-based approaches for identifying the target proteins for a small molecule have a number of limitations, particularly in terms of throughput and time and resource consumption. In addition, current technologies lack a mechanism to broadly assess the selectivity profile of the small molecule, which may be important for understanding off-target effects of the compound. Protein microarray technology has emerged as a powerful tool in the systems biology arsenal. Here, we describe how protein microarray technology can be applied to the study of small molecule protein interactions, with sensitivity sufficient to detect interactions with low muM affinity. These assays are highly reproducible, sensitive, and scalable, and provide an enabling technology for small molecule selectivity profiling in the context of drug development.

Protein kinase substrate identification on functional protein arrays.

BACKGROUND: Over the last decade, kinases have emerged as attractive therapeutic targets for a number of different diseases, and numerous high throughput screening efforts in the pharmaceutical community are directed towards discovery of compounds that regulate kinase function. The emerging utility of systems biology approaches has necessitated the development of multiplex tools suitable for proteomic-scale experiments to replace lower throughput technologies such as mass spectroscopy for the study of protein phosphorylation. Recently, a new approach for identifying substrates of protein kinases has applied the miniaturized format of functional protein arrays to characterize phosphorylation for thousands of candidate protein substrates in a single experiment. This method involves the addition of protein kinases in solution to arrays of immobilized proteins to identify substrates using highly sensitive radioactive detection and hit identification algorithms. RESULTS: To date, the factors required for optimal performance of protein array-based kinase substrate identification have not been described. In the current study, we have carried out a detailed characterization of the protein array-based method for kinase substrate identification, including an examination of the effects of time, buffer compositions, and protein concentration on the results. The protein array approach was compared to standard solution-based assays for assessing substrate phosphorylation, and a correlation of greater than 80% was observed. The results presented here demonstrate how novel substrates for protein kinases can be quickly identified from arrays containing thousands of human proteins to provide new clues to protein kinase function. In addition, a pooling-deconvolution strategy was developed and applied that enhances characterization of specific kinase-substrate relationships and decreases reagent consumption. CONCLUSION: Functional protein microarrays are an important new tool that enables multiplex analysis of protein phosphorylation, and thus can be utilized to identify novel kinase substrates. Integrating this technology with a systems biology approach to cell signalling will help uncover new layers in our understanding of this essential class of enzymes.

Global analysis of protein phosphorylation in yeast.

Protein phosphorylation is estimated to affect 30% of the proteome and is a major regulatory mechanism that controls many basic cellular processes. Until recently, our biochemical understanding of protein phosphorylation on a global scale has been extremely limited; only one half of the yeast kinases have known in vivo substrates and the phosphorylating kinase is known for less than 160 phosphoproteins. Here we describe, with the use of proteome chip technology, the in vitro substrates recognized by most yeast protein kinases: we identified over 4,000 phosphorylation events involving 1,325 different proteins. These substrates represent a broad spectrum of different biochemical functions and cellular roles. Distinct sets of substrates were recognized by each protein kinase, including closely related kinases of the protein kinase A family and four cyclin-dependent kinases that vary only in their cyclin subunits. Although many substrates reside in the same cellular compartment or belong to the same functional category as their phosphorylating kinase, many others do not, indicating possible new roles for several kinases. Furthermore, integration of the phosphorylation results with protein-protein interaction and transcription factor binding data revealed novel regulatory modules. Our phosphorylation results have been assembled into a first-generation phosphorylation map for yeast. Because many yeast proteins and pathways are conserved, these results will provide insights into the mechanisms and roles of protein phosphorylation in many eukaryotes.