Serum level of co-expressed hub miRNAs as diagnostic and prognostic biomarkers for pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Background: Sensitive and specific non-invasive biomarkers are urgently needed in order to improve the survival of patients with pancreatic ductal adenocarcinoma (PDAC), which is the fourth leading cause of cancer-related death. We aim to identify serum hub miRNAs as potential diagnostic and prognostic biomarkers for PDAC. Methods: A total of 2578 serum miRNA expression data from 88 PDAC patients and 19 healthy subjects were downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was constructed and significant modules were extracted from the network by WGCNA R package. Network modules and hub miRNAs closely related to PDAC were identified. The prognostic value of hub miRNAs was assessed by Kaplan-Meier overall survival analysis. Results: Two modules strongly associated with PDAC were identified by WGCNA, which were labeled as turquoise and brown respectively. Within each module, twenty hub miRNAs were found. At the functional level, turquoise module was mainly associated with tumorigenesis pathways such as P53 and WNT signaling pathway, while the brown module was mostly related to the pathways of cancer such as RNA transport and MAPK signaling pathway. Utilizing overall survival analyses, five "real" miRNAs were able to stratify PDAC patients into low-risk and high-risk groups. Conclusions: The association of specific Hub miRNAs with the development of pancreatic cancer was established by WGCNA analysis. Five miRNAs (mir-16-2-3p, mir-890, mir-3201, mir-602, and mir-877) were identified as potential diagnostic and prognostic biomarkers for PDAC.
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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 | 0.001 | 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 | 0.001 | 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".