PATH-14. ALPHA CARDIAC ACTIN EXPRESSION IS OBSERVED IN AGGRESSIVE GLIOMA SUBTYPES AND GLIOBLASTOMA STEM CELLS
Bibliographic record
Abstract
Abstract BACKGROUND Alterations in actin subunit expression have previously been observed in multiple cancers. In glioblastoma (GBM), the expression of ACTC1 has been associated with a more invasive phenotype and with shorter survival. We sought to explore the diversity of actin subunit expression across glioma subtypes and patient derived glioblastoma stem cells (GSCs). METHODS Bioinformatic analysis of multiple glioma databases was performed to profile actin subunit (ACTA1, ACTA2, ACTC1, ACTG1, ACTG2, and ACTB) mRNA levels. Expression levels were also evaluated in normal brain in comparison to liver and heart tissue. Western blot was used to analyze protein expression in GSCs, surgical tissue and human fetal astrocytes. RESULTS The primary actin subunits expressed in normal brain are beta actin (ACTB) and gamma actin (ACTG1). RNA sequencing of tissue from multiple glioma subtypes or different brain regions reveals a global increase in ACTG1 and ACTB abundance in gliomas compared to normal brain. LGG-GCIMP high and LGG-co-deleted glioma subtypes have the lowest ACTC1 expression. LGG-GCIMP low (HR 9.75, P< 0.001), LGG-mesenchymal-like (HR11.1, P< 0.001), LGG-classic-like (HR10.96, P< 0.001) subtypes are associated with ACTC1 expression. ACTC1, ACTCB, and ACTG protein expression was observed in GSCs, freshly resected GBM tissue, and human fetal astrocytes. CONCLUSIONS Gliomas have a specific pattern of actin subunit expression that differs in actin subunit type and abundance when compared to normal adult brain. Expression of ACTC1 is found in aggressive glioma subtypes and is shared by GSCs and human fetal astrocytes. Investigation into the neurodevelopmental role of ACTC1 and its contribution to oncogenic transformation in GBM is warranted.
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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 | 0.000 |
| 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".