Dataset Information

Proteomic Signatures of Thymomas.

ABSTRACT: Based on the histological features and outcome, the current WHO classification separates thymomas into A, AB, B1, B2 and B3 subtypes. It is hypothesized that the type A thymomas are derived from the thymic medulla while the type B thymomas are derived from the cortex. Due to occasional histological overlap between the tumor subtypes creating difficulties in their separation, the aim of this study was to provide their proteomic characterization and identify potential immunohistochemical markers aiding in tissue diagnosis. Pair-wise comparison of neoplastic and normal thymus by liquid chromatography tandem mass spectrometry (LC-MS/MS) of formalin fixed paraffin embedded tissue revealed 61 proteins differentially expressed in thymomas compared to normal tissue. Hierarchical clustering showed distinct segregation of subtypes AB, B1 and B2 from that of A and B3. Most notably, desmoyokin, a protein that is encoded by the AHNAK gene, was associated with type A thymomas and medulla of normal thymus, by LC-MS/MS and immunohistochemistry. In this global proteomic characterization of the thymoma, several proteins unique to different thymic compartments and thymoma subtypes were identified. Among differentially expressed proteins, desmoyokin is a marker specific for thymic medulla and is potentially promising immunohistochemical marker in separation of type A and B3 thymomas.

SUBMITTER: Wang L

PROVIDER: S-EPMC5104371 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Proteomic Signatures of Thymomas.

Wang Linan L Branson Owen E OE Shilo Konstantin K Hitchcock Charles L CL Freitas Michael A MA

PloS one 20161110 11

Based on the histological features and outcome, the current WHO classification separates thymomas into A, AB, B1, B2 and B3 subtypes. It is hypothesized that the type A thymomas are derived from the thymic medulla while the type B thymomas are derived from the cortex. Due to occasional histological overlap between the tumor subtypes creating difficulties in their separation, the aim of this study was to provide their proteomic characterization and identify potential immunohistochemical markers a ...[more]

PMID: 27832160

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Project description:PurposeThe aim of this study is to develop and compare performance of radiomics signatures using texture features extracted from noncontrast enhanced CT (NECT) and contrast enhanced CT (CECT) images for preoperative predicting risk categorization and clinical stage of thymomas.Materials and methodsBetween January 2010 and October 2018, 199 patients with surgical resection and histopathologically confirmed thymoma were enrolled in this retrospective study. We extracted 841 radiomics features separately from volume of interest (VOI) in NECT and CECT images. The features with poor reproducibility and highly redundancy were removed. Then a least absolute shrinkage and selection operator method (LASSO) logistic regression model with 10-fold cross validation was used for further feature selection and radiomics signatures build. The predictive performances of radiomics signatures were assessed by receiver operating characteristic (ROC) analysis. The areas under the receiver operating characteristic curve (AUC) between radiomics signatures were compared by using Delong test.ResultIn differentiating high risk thymomas from low risk thymomas, the AUC, sensitivity, and specificity were 0.801(95% CI 0.740-0.863), 0.752 and 0.767 for radiomics signature based on NECT images, and 0.827 (95% CI 0.771 -0.884), 0.798, and 0.722 for radiomics signature based on CECT images. But there was no significant difference (p=0.365) between them. In differentiating advanced stage thymomas from early stage thymomas, the AUC, sensitivity, and specificity were 0.829 (95%CI 0.757-0.900), 0.712, and 0.806 for radiomics signature based on NECT images and 0.860 (95%CI 0.803-0.917), 0.699, and 0.889 for radiomics signature based on CECT images. There was no significant difference (p=0.069) between them. The accuracy was 0.819 for radiomics signature based on NECT images, 0.869 for radiomics signature based on CECT images, and 0.779 for radiologists. Both radiomics signatures had a better performance than radiologists. But there was significant difference (p = 0.025) only between CECT radiomics signature and radiologists.ConclusionRadiomics signatures based on texture analysis from NECT and CECT images could be utilized as noninvasive biomarkers for differentiating high risk thymomas from low risk thymomas and advanced stage thymomas from early stage thymoma. As a quantitative method, radiomics signature can provide complementary diagnostic information and help to plan personalized treatment for patients with thymomas.

Project description:ObjectiveIdiopathic subglottic stenosis (iSGS) is a chronic inflammatory condition that causes dyspnea and affects middle-aged women of White race and non-Latino or Hispanic ethnicity. To better characterize its phenotype and pathogenesis, we assessed the proteomic and genomic methylation signatures of subglottic tissue collected from iSGS patients compared to controls.Study designMolecular analysis of clinical biospecimens.MethodsWe collected subglottic tissue biopsies from 12 patients during direct laryngoscopy, immediately prior to surgical treatment of iSGS; as well as from 4 age-, sex-, and race/ethnicity-matched control patients undergoing other direct laryngoscopic procedures. We isolated protein and genomic DNA, acquired proteomic data using label-free quantitative mass spectrometry techniques, and acquired genome-wide methylation data using bisulfite conversion and a microarray platform. We compared molecular profiles across the iSGS and control groups, and with respect to clinical course in the iSGS group. Eight of the 12 iSGS patients underwent subsequent blood collection and plasma isolation for further assessment.ResultsProteomic analysis revealed 42 differentially abundant proteins in the iSGS biopsies compared to controls, inferring enrichment of biological pathways associated with early wound healing, innate immunity, matrix remodeling, and metabolism. Proteome-based hierarchical clustering organized patients into two iSGS and one control subgroups. Methylation analysis revealed five hypermethylated genes in the iSGS biopsies compared to controls, including the biotin recycling enzyme biotinidase (BTD). Follow-up analysis showed elevated plasma BTD activity in iSGS patients compared to both controls and published normative data.ConclusioniSGS exhibits distinct proteomic and genomic methylation signatures. These signatures expand current understanding of the iSGS phenotype, support the possibility of disease subgroups, and should inform the direction of future experimental studies.Level of evidenceNot applicable Laryngoscope, 131:E540-E546, 2021.

Dataset Information

Proteomic Signatures of Thymomas.

Publications

Proteomic Signatures of Thymomas.

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