Arora, A and Patil, V and Kundu, P and Kondaiah, P and Hegde, AS and Arivazhagan, A and Santosh, V and Pal, D and Somasundaram, K (2019) Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma. In: Scientific Reports, 9 (1).
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Abstract
Despite advances in biology and treatment modalities, the prognosis of glioblastoma (GBM) remains poor. Serum reflects disease macroenvironment and thus provides a less invasive means to diagnose and monitor a diseased condition. By employing 4-plex iTRAQ methodology, we identified 40 proteins with differential abundance in GBM sera. The high abundance of serum S100A8/S100A9 was verified by multiple reaction monitoring (MRM). ELISA and MRM-based quantitation showed a significant positive correlation. Further, an integrated investigation using stromal, tumor purity and cell type scores demonstrated an enrichment of myeloid cell lineage in the GBM tumor microenvironment. Transcript levels of S100A8/S100A9 were found to be independent poor prognostic indicators in GBM. Medium levels of pre-operative and three-month post-operative follow-up serum S100A8 levels predicted poor prognosis in GBM patients who lived beyond median survival. In vitro experiments showed that recombinant S100A8/S100A9 proteins promoted integrin signalling dependent glioma cell migration and invasion up to a threshold level of concentrations. Thus, we have discovered GBM serum marker by iTRAQ and verified by MRM. We also demonstrate interplay between tumor micro and macroenvironment and identified S100A8 as a potential marker with diagnostic and prognostic value in GBM. © 2019, The Author(s).
| Item Type: | Journal Article |
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| Publication: | Scientific Reports |
| Additional Information: | cited By 0 |
| Department/Centre: | Division of Biological Sciences > Microbiology & Cell Biology Division of Biological Sciences > Molecular Reproduction, Development & Genetics Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 11 Mar 2019 10:44 |
| Last Modified: | 11 Mar 2019 10:44 |
| URI: | http://eprints.iisc.ac.in/id/eprint/61977 |
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