Mutation and Microsatellite Instability (MSI) Affect the Differential Gene Expression of Folic Acid and 5-Flourouracil Metabolism-Related Genes in Colorectal Carcinoma
Bibliographic record
Abstract
In colorectal carcinoma (CRC), 5-fluorouracil (5-FU) remains the cornerstone of adjuvant systemic therapy, with folic acid (FA) serving as an essential adjunct. Expression of genes related to the metabolism and action of 5-FU and FA can be influenced by patient- and tumor-specific biological factors. In this study, we explore differential gene expression profiles of 180 genes representing 14 different gene sets associated with different 5-FU and FA metabolism processes, at both gene and pathway levels across clinical and molecular subgroups. In 71 patients with CRC, paired tumors and normal colonic tissues were analyzed. In CRC tissue, several gene sets (including Cell Cycle Checkpoint, Oxidative Stress Response, and Signaling Pathway, etc.) were upregulated, while three gene sets (Apoptotic, Tumor Suppressor, and Endoplasmic Reticulum Stress) were downregulated. Kirsten rat sarcoma virus (KRAS), tumor protein p53 (TP53), and microsatellite instability (MSI) status impacted gene expression across molecular subgroups. At the individual gene level, among cell cycle genes, the BUB3 mitotic checkpoint protein (BUB3) was upregulated in MSI tumors compared to MSS, whereas SMAD family member 4 (SMAD4) was downregulated in MSS tumors compared to MSI. DNA fragmentation factor alpha (DFFA) was downregulated in MSI and upregulated in MSS. Notably, thymidylate synthetase (TYMS) was more upregulated in MSI tumors (1.65-fold; 95% CI: 1.27–2.13) compared to MSS (1.19-fold; 95% CI: 1.02–1.39). Dysregulation of these genes across these factors will broaden our understanding of 5-FU-based treatment in CRC. Furthermore, targeting dysregulated pathways could form the basis for improved precision therapies tailored to CRC subtypes.
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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 | 0.000 |
| 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".