Integrative genomic and gene expression analysis of chromosome 7 identified novel oncogene loci in non-small cell lung cancer
Bibliographic record
Abstract
Lung cancer accounts for over a quarter of cancer deaths, with non-small cell lung cancer (NSCLC) accounting for approximately 80% of cases. Several genome studies have been undertaken in both cell models of NSCLC and clinical samples to identify alterations underlying disease behaviour, and many have identified recurring aberrations of chromosome 7. The presence of recurring chromosome 7 alterations that do not span the well-studied oncogenes EGFR (at 7p11.2) and MET (at 7q31.2) has raised the hypothesis of additional genes on this chromosome that contribute to tumourigenesis. In this study, we demonstrated that multiple loci on chromosome 7 are indeed amplified in NSCLC, and through integrative analysis of gene dosage alterations and parallel gene expression changes, we identified new lung cancer oncogene candidates, including FTSJ2, NUDT1, TAF6, and POLR2J. Activation of these key genes was confirmed in panels of clinical lung tumour tissue as compared with matched normal lung tissue. The detection of gene activation in multiple cohorts of samples strongly supports the presence of key genes involved in lung cancer that are distinct from the EGFR and MET loci on chromosome 7.
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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".