ROLE OF EPIGENETIC MODIFICATIONS IN TUMOR SUPPRESSOR GENE SILENCING AMONG PATIENTS WITH SPORADIC COLORECTAL CANCER: NARRATIVE REVIEW
Bibliographic record
Abstract
Background: Sporadic colorectal cancer (CRC), which constitutes the majority of CRC cases, is primarily driven by acquired molecular changes rather than inherited mutations. Among these, epigenetic alterations—specifically the silencing of tumor suppressor genes—play a crucial role in colorectal tumorigenesis. Understanding these reversible and dynamic modifications offers significant potential for enhancing early detection, prognosis, and targeted therapy. Objective: This narrative review aims to explore the role of epigenetic modifications in the silencing of tumor suppressor genes in patients with sporadic colorectal cancer, synthesizing current findings and identifying implications for clinical practice and future research. Main Discussion Points: Key epigenetic mechanisms discussed include promoter hypermethylation of genes such as MLH1, MGMT, and CDKN2A; long-range epigenetic silencing across chromosomal regions; histone modification patterns contributing to chromatin inactivation; and microRNA suppression involved in early tumorigenesis. The review also highlights variability in study methodologies, population-specific differences, and gaps in longitudinal and functional research. Limitations related to sample size, design, and generalizability are critically analyzed. Conclusion: Epigenetic silencing of tumor suppressor genes is a central event in sporadic CRC development, with clear implications for biomarker discovery and therapeutic targeting. While current evidence is promising, more standardized and mechanistically focused studies are needed to translate these findings into clinical applications and guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".