Metabolic Profiling of Formalin-Fixed Paraffin-Embedded Tissues Discriminates Normal Colon from Colorectal Cancer
Bibliographic record
Abstract
Abstract Accumulating evidence suggests that metabolic reprogramming has a critical role in carcinogenesis and tumor progression. The usefulness of formalin-fixed paraffin-embedded (FFPE) tissue material for metabolomics analysis as compared with fresh frozen tissue material remains unclear. LC/MS-MS–based metabolomics analysis was performed on 11 pairs of matched tumor and normal tissues in both FFPE and fresh frozen tissue materials from patients with colorectal carcinoma. Permutation t test was applied to identify metabolites with differential abundance between tumor and normal tissues. A total of 200 metabolites were detected in the FFPE samples and 536 in the fresh frozen samples. The preservation of metabolites in FFPE samples was diverse according to classes and chemical characteristics, ranging from 78% (energy) to 0% (peptides). Compared with the normal tissues, 34 (17%) and 174 (32%) metabolites were either accumulated or depleted in the tumor tissues derived from FFPE and fresh frozen samples, respectively. Among them, 15 metabolites were common in both FFPE and fresh frozen samples. Notably, branched chain amino acids were highly accumulated in tumor tissues. Using KEGG pathway analyses, glyoxylate and dicarboxylate metabolism, arginine and proline, glycerophospholipid, and glycine, serine, and threonine metabolism pathways distinguishing tumor from normal tissues were found in both FFPE and fresh frozen samples. This study demonstrates that informative data of metabolic profiles can be retrieved from FFPE tissue materials. Implications: Our findings suggest potential value of metabolic profiling using FFPE tumor tissues and may help to shape future translational studies through developing treatment strategies targeting metabolites.
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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".