Identification of interactive networks of gene expression associated with osteosarcoma oncogenesis by integrated molecular profiling
Bibliographic record
Abstract
Altered gene expression in tumors can be caused by copy number alterations to DNA or mutation affecting coding or regulatory regions of genes. However, epigenetic events may also influence gene expression. Malignant cells can show major disruptions in DNA methylation profiles, which are manifested as aberrant hypermethylation or as hypomethylation of gene promoters, as well as global genomic hypomethylation. In this study we performed integrative whole-genome analysis of DNA copy number, promoter methylation and gene expression using 10 osteosarcomas. We identified significant changes including: hypomethylation, gain, and overexpression of histone cluster 2 genes at chromosome 1q21.1-q21.3; loss of chromosome 8p21.2-p21.3 and underexpression of DOCK5 and TNFRSF10A/D genes; and amplification-related overexpression of RUNX2 at chromosome 6p12.3-p21.1. Amplification and overexpression of RUNX2 could disrupt G2/M cell cycle checkpoints, and downstream osteosarcoma-specific changes, such as failure of bone differentiation and genomic polyploidization. Failure of DOCK5-signaling, together with p53 and TNFRSF10A/D-related cell cycle and death pathways, may play a critical role in abrogating apoptosis. Our analyses show that the RUNX2 interactome may be constitutively activated in osteosarcoma, and that the downstream intracellular pathways are strongly associated with the regulation of osteoblast differentiation and control of cell cycle and apoptosis in osteosarcoma.
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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 | 0.001 |
| 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 | 0.000 |
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".