Osteopontin induces multiple changes in gene expression that reflect the six “hallmarks of cancer” in a model of breast cancer progression
Bibliographic record
Abstract
Tumor progression is a multistep process, which enables cells to evolve from benign to malignant tumors. This progression has been suggested to depend on six essential characteristics identified as the "hallmarks of cancer," which include: self-sufficiency in growth signals, insensitivity to growth-inhibitory signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis. Osteopontin (OPN) is an integrin-binding protein that has been shown to be associated with the progression of several cancer types, and to play an important functional role in various aspects of malignancy, particularly tissue invasion and metastasis. Here we studied genes regulated by OPN in a model of human breast cancer using oligonucleotide microarray technology by comparing the gene-expression profiles of 21NT mammary carcinoma cells transfected to overexpress OPN versus mock-transfected control cells. From over 12,000 human genes, we identified 99 known human genes differentially regulated by OPN whose expression changed by at least 1.5-fold and showed statistically significant differences in mean expression levels between groups. Functional classification of these genes into the hallmarks of cancer categories showed that OPN can affect the expression of genes involved in all six categories in this model. Furthermore, we were able to validate the expression of 18/19 selected candidate genes by quantitative real-time PCR, further supporting our microarray findings. This study provides the first evidence that OPN can lead to numerous gene expression changes that influence multiple aspects of tumor progression and malignant growth.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".