Multi‐pronged proteomic analysis to study the glioma pathobiology using cerebrospinal fluid samples
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: Gliomas are one of the most aggressive and lethal brain tumors arising from neoplastic transformation of astrocytes and oligodendrocytes. A comprehensive quantitative analysis of proteome level differences in cerebrospinal fluid (CSF) across different grades of gliomas for a better understanding of glioma pathobiology is carried out. EXPERIMENTAL DESIGN: Glioma patients are diagnosed by radiology and histochemistry-based analyses. Differential proteomic analysis of high (n = 12) and low (n = 5) grade gliomas, and control (n = 3) samples is performed by using two complementary quantitative proteomic approaches; 2D-DIGE and iTRAQ. Further, comparative analysis of three IDH wild-type and five IDH mutants is performed to identify the proteome level differences between these two sub-classes. RESULTS: Level of several proteins including haptoglobin, transthyretin, osteopontin, vitronectin, complement factor H and different classes of immunoglobulins are found to be considerably increased in CSF of higher grades of gliomas. Subsequent bioinformatics analysis indicated that many of the dysregulated CSF proteins are associated with metabolism of lipids and lipoproteins, complement and coagulation cascades and extracellular matrix remodeling in gliomas. Intriguingly, CSF of glioma patients with IDH mutations exhibite increased levels of multiple proteins involved in response to oxidative stress. CONCLUSION AND CLINICAL RELEVANCE: To the best of our knowledge, this is the foremost proteome level investigation describing comprehensive proteome profiles of different grades of gliomas using proximal fluid (CSF); and thereby providing insights into disease pathobiology, which aided in identification of grade and sub-type specific alterations. Moreover, if validated in larger clinical cohorts, a panel of differentially abundant CSF proteins may serve as potential disease monitoring and prognostic markers for gliomas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it