Identification of <i>SMURF1</i> as a possible target for 7q21.3‐22.1 amplification detected in a pancreatic cancer cell line by in‐house array‐based comparative genomic hybridization
Bibliographic record
Abstract
Pancreatic cancer (PC) cell lines provide a useful starting point for the discovery and functional analysis of genes driving the genesis and progression of this lethal cancer. To increase our understanding of the gene copy number changes in pancreatic carcinomas and to identify key amplification and deletion targets, we applied genome-wide array-based comparative genomic hybridization using in-house array (MCG Cancer Array-800) to 24 PC cell lines. Overall, the analyses revealed high genomic complexity, with several copy number changes detected in each line. Homozygous deletions (log(2)ratio < -2) of eight genes (clones) were seen in 14 of the 24 cell lines, whereas high-level amplifications (log(2)ratio > 2) of 10 genes (clones) were detected in seven lines. Among them, we focused on high-level amplification at 7q22.1, because target genes for this alteration remain unknown. Through precise mapping of the altered region by fluorescence in situ hybridization, determination of the expression status of genes located within those regions, and functional analysis using knockdown of the gene expression or the ectopic overexpression approach in PC cell lines, as well as immunohistochemical analyses of candidates in primary tumors of PC, we successfully identified SMURF1 as having the greatest potential as a 7q21.3-22.1 amplification target. SMURF1 may work as a growth-promoting gene in PC through overexpression and might be a good candidate as a therapeutic target. Our results suggest that array-based comparative genomic hybridization analysis combined with further genetic and functional examinations is a useful approach for identifying novel tumor-associated genes involved in the pathogenesis of this lethal disease.
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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 | 0.000 |
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".