Colorectal Cancer: Pathogenesis and Targeted Therapy
Bibliographic record
Abstract
Colorectal cancer (CRC) ranks among the most prevalent malignant neoplasms globally. A growing body of evidence underscores the pivotal roles of genetic alterations and dysregulated epigenetic modifications in the pathogenesis of CRC. In recent years, the reprogramming of tumor cell metabolism has been increasingly acknowledged as a hallmark of cancer. Substantial evidence suggests a crosstalk between tumor cell metabolic reprogramming and epigenetic modifications, highlighting a complex interplay between metabolism and the epigenetic genome that warrants further investigation. Biomarkers associated with the pathogenesis and metabolic characteristics of CRC hold significant clinical implications. Nevertheless, elucidating the genetic, epigenetic, and metabolic landscapes of CRC continues to pose considerable challenges. Here, we attempt to summarize the key genes driving the onset and progression of CRC and the related epigenetic regulators, clarify the roles of gene expression and signaling pathways in tumor metabolism regulation, and explore the potential crosstalk between epigenetic events and tumor metabolic reprogramming, providing a comprehensive mechanistic explanation for the malignant progression of CRC. Finally, by integrating reliable targets from genetics, epigenetics, and metabolic processes that hold promise for translation into clinical practice, we aim to offer more strategies to overcome the bottlenecks in CRC treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".