Dataset Information

Proteomic analysis discovers the differential expression of novel proteins and phosphoproteins in meningioma including NEK9, HK2 and SET and deregulation of RNA metabolism.

ABSTRACT:

Background

Meningioma is the most frequent primary intracranial tumour. Surgical resection remains the main therapeutic option as pharmacological intervention is hampered by poor knowledge of their proteomic signature. There is an urgent need to identify new therapeutic targets and biomarkers of meningioma.

Methods

We performed proteomic profiling of grade I, II and III frozen meningioma specimens and three normal healthy human meninges using LC-MS/MS to analyse global proteins, enriched phosphoproteins and phosphopeptides. Differential expression and functional annotation of proteins was completed using Perseus, IPA® and DAVID. We validated differential expression of proteins and phosphoproteins by Western blot on a meningioma validation set and by immunohistochemistry.

Findings

We quantified 3888 proteins and 3074 phosphoproteins across all meningioma grades and normal meninges. Bioinformatics analysis revealed commonly upregulated proteins and phosphoproteins to be enriched in Gene Ontology terms associated with RNA metabolism. Validation studies confirmed significant overexpression of proteins such as EGFR and CKAP4 across all grades, as well as the aberrant activation of the downstream PI3K/AKT pathway, which seems differential between grades. Further, we validated upregulation of the total and activated phosphorylated form of the NIMA-related kinase, NEK9, involved in mitotic progression. Novel proteins identified and validated in meningioma included the nuclear proto-oncogene SET, the splicing factor SF2/ASF and the higher-grade specific protein, HK2, involved in cellular metabolism.

Interpretation

Overall, we generated a proteomic thesaurus of meningiomas for the identification of potential biomarkers and therapeutic targets. FUND: This study was supported by Brain Tumour Research.

SUBMITTER: Dunn J

PROVIDER: S-EPMC6412084 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

Proteomic analysis discovers the differential expression of novel proteins and phosphoproteins in meningioma including NEK9, HK2 and SET and deregulation of RNA metabolism.

Dunn Jemma J Ferluga Sara S Sharma Vikram V Futschik Matthias M Hilton David A DA Adams Claire L CL Lasonder Edwin E Hanemann C Oliver CO

EBioMedicine 20181226

<h4>Background</h4>Meningioma is the most frequent primary intracranial tumour. Surgical resection remains the main therapeutic option as pharmacological intervention is hampered by poor knowledge of their proteomic signature. There is an urgent need to identify new therapeutic targets and biomarkers of meningioma.<h4>Methods</h4>We performed proteomic profiling of grade I, II and III frozen meningioma specimens and three normal healthy human meninges using LC-MS/MS to analyse global proteins, e ...[more]

PMID: 30594554

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Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.

Dataset Information

Proteomic analysis discovers the differential expression of novel proteins and phosphoproteins in meningioma including NEK9, HK2 and SET and deregulation of RNA metabolism.

Background

Methods

Findings

Interpretation

Publications

Proteomic analysis discovers the differential expression of novel proteins and phosphoproteins in meningioma including NEK9, HK2 and SET and deregulation of RNA metabolism.

Similar Datasets

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