Integrative Genomics Identified <i>RFC3</i> As an Amplified Candidate Oncogene in Esophageal Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: Esophageal adenocarcinoma (EAC) is a lethal malignancy that can develop from the premalignant condition, Barrett's esophagus (BE). Currently, there are no validated simple methods to predict which patients will progress to EAC. A better understanding of the genetic mechanisms driving EAC tumorigenesis is needed to identify new therapeutic targets and develop biomarkers capable of identifying high-risk patients that would benefit from aggressive neoadjuvant therapy. We employed an integrative genomics approach to identify novel genes involved in EAC biology that may serve as useful clinical markers. EXPERIMENTAL DESIGN: Whole genome tiling-path array comparative genomic hybridization was used to identify significant regions of copy number alteration in 20 EACs and 10 matching BE tissues. Copy number and gene expression data were integrated to identify candidate oncogenes within regions of amplification and multiple additional sample cohorts were assessed to validate candidate genes. RESULTS: We identified RFC3 as a novel, candidate oncogene activated by amplification in approximately 25% of EAC samples. RFC3 was also amplified in BE from a patient whose EAC harbored amplification and was differentially expressed between nonmalignant and EAC tissues. Copy number gains were detected in other cancer types and RFC3 knockdown inhibited proliferation and anchorage-independent growth of cancer cells with increased copy number but had little effect on those without. Moreover, high RFC3 expression was associated with poor patient outcome in multiple cancer types. CONCLUSIONS: RFC3 is a candidate oncogene amplified in EAC. RFC3 DNA amplification is also prevalent in other epithelial cancer types and RFC3 expression could serve as a prognostic marker.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".