HMGA2 Participates in Transformation in Human Lung Cancer
Bibliographic record
Abstract
Although previous studies have established a prominent role for HMGA1 (formerly HMG-I/Y) in aggressive human cancers, the role of HMGA2 (formerly HMGI-C) in malignant transformation has not been clearly defined. The HMGA gene family includes HMGA1, which encodes the HMGA1a and HMGA1b protein isoforms, and HMGA2, which encodes HMGA2. These chromatin-binding proteins function in transcriptional regulation and recent studies also suggest a role in cellular senescence. HMGA1 proteins also appear to participate in cell cycle regulation and malignant transformation, whereas HMGA2 has been implicated primarily in the pathogenesis of benign, mesenchymal tumors. Here, we show that overexpression of HMGA2 leads to a transformed phenotype in cultured lung cells derived from normal tissue. Conversely, inhibiting HMGA2 expression blocks the transformed phenotype in metastatic human non-small cell lung cancer cells. Moreover, we show that HMGA2 mRNA and protein are overexpressed in primary human lung cancers compared with normal tissue or indolent tumors. In addition, there is a statistically significant correlation between HMGA2 protein staining by immunohistochemical analysis and tumor grade (P < 0.001). Our results indicate that HMGA2 is an oncogene important in the pathogenesis of human lung cancer. Although additional studies with animal models are needed, these findings suggest that targeting HMGA2 could be therapeutically beneficial in lung cancer and other cancers characterized by increased HMGA2 expression.
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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 | 0.000 | 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) | 0.001 | 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".