Clinicopathological Features of KRAS-Mutated Colon Cancer: An Analytical Cross-Sectional Study
Bibliographic record
Abstract
Background: Colon cancer is a leading neoplasm worldwide, with 35% to 45% of colorectal cancer (CRC) patients exhibiting mutations in the Kirsten rat sarcoma oncogene (KRAS). This mutation affects disease development and serves as a biomarker for early detection, prognosis, and treatment. The objective of the present study was to identify the clinicopathological characteristics of colon cancer patients with KRAS mutations. Methods: An analytical cross-sectional study involving patients with CRC was conducted. The study variables included sex, age, tumor location, KRAS and B-Raf proto-oncogene (BRAF) mutations, and the presence of metastases. Results: The study involved 51 patients, with a mean (standard deviation) age of 61.4 ± 11.0 years. The most common tumor location was the sigmoid colon (35.3%), and 45.1% of patients were classified as tumor, node, metastasis (TNM) stage III with lymph node dissemination. Genetic analysis revealed that 35% of patients had KRAS mutations, while 32% had BRAF mutations. Notably, 61.1% of KRAS-positive patients also had BRAF mutations compared to 15.1% of KRAS-negative patients (P = 0.02). Conclusions: KRAS-positive patients predominantly had tumors in the sigmoid colon. The coexistence of KRAS and BRAF mutations suggests a potential molecular interaction influencing disease progression. These findings highlight a distinct genomic pattern and the need for further research into its clinical implications.
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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 | 0.001 |
| 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) | 0.002 | 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".