Abstract 4830: Identification of differentially expressed genes and protein in bone invasive non-invasive meningiomas
Bibliographic record
Abstract
Abstract Introduction: Meningiomas are cranial tumors arising from the meningeal coverings of the brain and while the majority grow intradurally, some can invade the underlying bone causing hyperostosis and invasion into neuronal structures. The surgical resection of such bone-invading tumors is challenging and repeat surgery is often required, resulting in significant patient morbidity. To date there has been very limited studies focused on the molecular pathophysiology of bone invading meningiomas. Our aim was to use an integrative analysis approach, performing RNA microarray and tissue microarray analysis to identify differentially expressed protein and genes involved in bone tropism of meningioma cells. Methods: We chose radiological features to define two distinct bone invading meningioma population and their control counterpart. 1) spheno-orbital meningioma and control counterpart non-bone invading sphenoid wing meningioma and 2) transbasal meningioma and control counterpart anterior skull base meningioma with no bone invasion. We identified 57 patients satisfying these bone invading classifications operated on in the past ten years at our institution. RNA was extracted for microarray analysis from paraffin-embedded tissue sections of invasive and non-invasive meningiomas and processed on Illumina Whole Genome DASL assay. Data were analyzed using Multi Expression Viewer Software (MEV). Quantitative real-time PCR (RT-qPCR) was used to verify micro-array data. We further verified our data using Tissue microarray (TMA) and examined commercially available antibodies involved in bone invasion (osteopontin, MMP2 and integrin-β1). TMA scoring was carried out with two independent observers using percentage and intensity staining.Three different meningioma cell lines (IOMMA-Lee, CH157-MN and F5 cells) were used to verify microarray results and in vitro and in vivo functional studies. Results: RNA microarray data analysis identified 222 differentially expressed genes (92 genes over-expressed and 130 genes under expressed), amongst which we can refer to over expression of PDGFRα, MMP16, MMP19, Matrilin4 and ADAMTS4 in bone-invasive relative to non-invasive meningiomas. Upregulation of these genes were verified using quantitative real-time PCR (RT-qPCR) analysis in both tumor specimens and meningioma cell lines.TMA analysis identified increased expression of both MMP2 and integrin-β1 in tumor cells of non-invasive meningiomas, and increase of vascular MMP2 expression in non-invasive compared to invasive meningiomas. Conclusions: Our results identify novel differentially expressed proteins and genes in bone-invading meningiomas compared to non-invasive meningiomas. Our baseline in-vivo mechanistic results will help design new therapeutic strategies that can control bone-invading meningioma progression and provide the basis for translation to clinical studies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4830. doi:10.1158/1538-7445.AM2011-4830
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".