A 14-gene gemcitabine resistance gene signature is significantly associated with the prognosis of pancreatic cancer patients
Bibliographic record
Abstract
To identify a gemcitabine resistance-associated gene signature for risk stratification and prognosis prediction in pancreatic cancer. Pearson correlation analysis was performed with gemcitabine half maximal inhibitory concentration (IC50) data of 17 primary pancreatic cancer lines from Genomics of Drug Sensitivity in Cancer (GDSC) and the transcriptomic data from GDSC and Broad Institute Cancer Cell Line Encyclopedia, followed by risk stratification, expression evaluation, overall survival (OS) prediction, clinical data validation and nomogram establishment. Our biomarker discovery effort identified a 14-gene signature, most of which featured differential expression. The 14-gene signature was associated with poor OS in E-MTAB-6134 (HR 2.37; 95% CI 1.75-3.2; p < 0.0001), pancreatic cancer-Canada (PACA-CA) (HR 1.76; 95% CI 1.31-2.37; p = 0.00015), and 4 other independent validation cohorts: pancreatic cancer-Australia (PACA-AU) (HR 1.9; 95% CI 1.38-2.61; p < 0.0001), The Cancer Genome Atlas (TCGA) (HR 1.73; 95% CI 1.11-2.69; p = 0.014), GSE85916 (HR 1.97; 95% CI 1.14-3.42; p = 0.014) and GSE62452 (HR 1.82; 95% CI 1.02-3.24; p = 0.039). Multivariate analysis revealed that the 14-gene risk score was an independent pancreatic cancer outcome predictor in E-MTAB-6134 (p < 0.001) and TCGA (p = 0.006). A nomogram including the 14-gene was established for eventual clinical translation. We identified a novel gemcitabine resistance gene signature for risk stratification and robust categorization of pancreatic cancer patients with poor prognosis.
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 | 0.001 |
| 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".