Therapeutic Target Identification in Pancreatic Ductal Adenocarcinoma: A Comprehensive In-Silico Study employing WGCNA and Trader
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) is recognized as a highly aggressive fatal disease accounting for more than 90% of all pancreatic malignancies. Considering the limited effective treatment options and its low survival rate, studying PDAC's underlying mechanisms is of utmost importance. The present study focused on investigating PDAC expression data using WGCNA and Trader algorithms to shed light on the underlying mechanisms and identify the most reliable therapeutic candidates in PDAC. After analyzing a recently generated PDAC dataset (GSE132956), the obtained differentially expressed genes (DEGs) were subjected to different exploration steps. WGCNA was applied to cluster the co-expressed DEGs and found the disease's most correlated module and genes. The trader algorithm was utilized to analyze the constructed network of DEGs in STRING and identified the top 30 DEGs whose removal causes a maximum number of separate sub-networks. Hub genes were selected considering the lists of top identified nodes by the two algorithms. "Signaling by Rho GTPases," "Signaling by receptor tyrosine kinases," and "immune system" were top enriched gene ontology terms for the DEGs in the PDAC most correlated module and nine hub genes, including FYN, MAPK3, CDK2, SNRPG, GNAQ, PAK1, LPCAT4, MAP1LC3B, and FBN1 were identified by considering the top spotted DEGs by two algorithms. The findings provided evidence about the involvement of some pathways in the pathogenesis of PDAC and suggested several hub genes as therapeutic candidates via a comprehensive approach analyzing both the co-expression and PPI networks of DEGs in this 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".