Dataset Information

Immunohistochemical and genetic markers to distinguish hemangiopericytoma and meningioma.

ABSTRACT: Hemangiopericytoma (HPC) and meningioma, for their morphology immunohistochemical markers similarity, were usually confused especially before surgery. This study aimed to develop a panel of biomarkers to differentiate HPC from meningioma. Real-time PCR and immunohistochemical staining were employed to determine the levels of p53, bcl-2, c-myc, vimentin, CD34, FVIIIa, MGMT and reticular fiber in 15 meningiomas, HPCs and their normal controls. We found that, in the mRNA expression level, both Bcl-2 and c-myc were high in HPC and meningiomas, but bcl-2 was higher in HPC than in meningiomas, c-myc was lower in HPC than in meningiomas. In protein expression level, reticular fibers were around most HPC tumor cells but observed null in meningiomas; CD34 and FVIIIa were both found positive in HPCs but negative in meningiomas; MGMT was weak focal in HPC but strong diffuse in meningiomas. In conclusion, bcl-2, c-myc, and MGMT could be employed as the new panels of biomarkers for distinguishing HPC from meningiomas.

SUBMITTER: Zhao P

PROVIDER: S-EPMC4443052 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Immunohistochemical and genetic markers to distinguish hemangiopericytoma and meningioma.

Zhao Penglai P Zhu Tongming T Tang Qisheng Q Liu Hongyi H Zhu Jianhong J Zhang Wenbin W

International journal of clinical and experimental medicine 20150315 3

Hemangiopericytoma (HPC) and meningioma, for their morphology immunohistochemical markers similarity, were usually confused especially before surgery. This study aimed to develop a panel of biomarkers to differentiate HPC from meningioma. Real-time PCR and immunohistochemical staining were employed to determine the levels of p53, bcl-2, c-myc, vimentin, CD34, FVIIIa, MGMT and reticular fiber in 15 meningiomas, HPCs and their normal controls. We found that, in the mRNA expression level, both Bcl- ...[more]

PMID: 26064218

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Project description:Meningiomas, the most common adult brain tumors, recur in up to half of cases. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors which constitute the majority of cases. Moreover, attempts to find molecular markers to predict their recurrence have been impeded by low or heterogenous genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. We apply gene co-expression networks to a cohort of 252 adult patients from the publicly available genetic repository Gene Expression Omnibus. Resultant gene clusters ("modules") were represented by the first principle component of their expression, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using two independent samples: one merged microarray-based cohort with a total of 108 patients and one RNA-seq-based cohort with 145 patients, using the same modules. We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes (AUC of 0.81?±?0.09 and 0.77?±?0.10 in external validation using microarray and RNA-seq data, respectively). This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research.

Dataset Information

Immunohistochemical and genetic markers to distinguish hemangiopericytoma and meningioma.

Publications

Immunohistochemical and genetic markers to distinguish hemangiopericytoma and meningioma.

Similar Datasets

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