Recurrent Focal Copy-Number Changes and Loss of Heterozygosity Implicate Two Noncoding RNAs and One Tumor Suppressor Gene at Chromosome 3q13.31 in Osteosarcoma
Bibliographic record
Abstract
Osteosarcomas are copy number alteration (CNA)-rich malignant bone tumors. Using microarrays, fluorescence in situ hybridization, and quantitative PCR, we characterize a focal region of chr3q13.31 (osteo3q13.31) harboring CNAs in 80% of osteosarcomas. As such, osteo3q13.31 is the most altered region in osteosarcoma and contests the view that CNAs in osteosarcoma are nonrecurrent. Most (67%) osteo3q13.31 CNAs are deletions, with 75% of these monoallelic and frequently accompanied by loss of heterozygosity (LOH) in flanking DNA. Notably, these CNAs often involve the noncoding RNAs LOC285194 and BC040587 and, in some cases, a tumor suppressor gene that encodes the limbic system-associated membrane protein (LSAMP). Ubiquitous changes occur in these genes in osteosarcoma, usually involving loss of expression. Underscoring their functional significance, expression of these genes is correlated with the presence of osteo3q13.31 CNAs. Focal osteo3q13.31 CNAs and LOH are also common in cell lines from other cancers, identifying osteo3q13.31 as a generalized candidate region for tumor suppressor genes. Osteo3q13.31 genes may function as a unit, given significant correlation in their expression despite the great genetic distances between them. In support of this notion, depleting either LSAMP or LOC285194 promoted proliferation of normal osteoblasts by regulation of apoptotic and cell-cycle transcripts and also VEGF receptor 1. Moreover, genetic deletions of LOC285194 or BC040587 were also associated with poor survival of osteosarcoma patients. Our findings identify osteo3q13.31 as a novel region of cooperatively acting tumor suppressor genes.
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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) | 0.001 | 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".