Development of epithelial-mesenchymal transition-related lncRNA signature for predicting survival and immune microenvironment in pancreatic cancerwithexperiment validation
Bibliographic record
Abstract
Long non-coding RNAs (LncRNAs) have crucial function in epithelial–mesenchymal transition (EMT) in pancreatic cancer. It is necessary to comprehensively analyze the potential role of EMT-related lncRNA in pancreatic cancer. In the present study, genomic data of pancreatic cancer from the TCGA database were downloaded and we found 368 EMT-related lncRNAs. According to the expression characteristics of prognostic-related lncRNAs, all samples could be divided into two clusters with different clinical outcomes and different tumor microenvironments. Moreover, an eleven EMT-related lncRNAs signature was established and verified. Patients with pancreatic cancer in the high-risk group had a shorter overall survival than those in the low-risk group and the signature could act as an independent prognostic factor. Further analysis suggested that the EMT-related lncRNAs might affect the prognosis of patients through immune mechanisms. All findings indicated that the signature and eleven lncRNAs might serve as potential prognostic biomarkers and therapeutic targets in the treatment of pancreatic cancer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".