Expression Profiles of Cuproptosis-Related Genes Determine Distinct Subtypes of Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent subtype of pancreatic cancer and one of the most malignant tumors worldwide. Due to the heterogeneity of its genomics and proteomics, the prognosis of PDAC remains disappointing despite advances in surgery and medicines. Recently, a novel form of programmed cell death, cuproptosis, was proposed, although its role in PDAC has not been investigated. This study aimed to quantify the expression of cuproptosis-related genes and characterize the novel subtypes of PDAC. Methods: To evaluate the pattern of cuproptosis in PDAC, the gene expression data and clinical information of 372 samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus cluster analysis was performed using the transcriptional levels, genetic alterations, and individual prognostic values of seven pre-selected cuproptosis-related genes (DLAT, LIPT1, FDX1, DLD, PDHB, PDHA1, and LIAS) to identify the novel subtypes associated with cuproptosis in PDAC. A univariate Cox regression analysis was used to determine the significant prognostic indicators and cuproptosis scores among the differentially expressed genes (DEGs) between the dividing subclusters, followed by a principal component analysis. The prognostic values, immune profiles, treatment sensitivities, and cuproptosis scores were evaluated between the different subgroups. Results: Seven cuproptosis-related genes showed aberrant expression levels and genetic alterations in the PDAC tumor microenvironment. Among them, LIPT1, LIAS, DLAT, PDHA1, and DLD were significantly correlated with overall survival. Based on the expression profiles of the seven cuproptosis-related genes, three cuproptosis clusters (Clusters A, B, and C) were identified, which were represented by different clinicopathologic features, gene expression levels, and biological processes. A total of 686 DEGs were identified among the three cuproptosis clusters, of which 35 prognosis-related DEGs were selected to further classify the PDAC samples into two subgroups with different survival rates, clinicopathologic features, immune infiltration levels, and drug sensitivities. Higher cuproptosis scores were associated with a significantly poorer prognosis. Conclusion: The cuproptosis subtypes, scores, and relevant genes represent valuable information for assessing the heterogeneity, treatment, and prognosis of 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 | 0.000 | 0.000 |
| 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".