Identification of Risk Molecular Subtype of Colon Cancer with Lymphovascular Invasion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Although surgical resection generally yields excellent outcomes, a number of patients with colon cancer still have relapse or metastasis after surgery. Adjuvant chemotherapy in tumor stage III has been demonstrated to eradicate micrometastasis and improve survival, whereas the benefits of adjuvant chemotherapy in tumor stage II remain controversial. The leading cause is the lack of understanding of the molecular basis of underlying metastatic mechanisms. Objective: This study aimed to identify molecular subtype(s) of colon cancer with a high risk of metastasis and provide potential biomarkers for prognostic prediction in tumor stage II. Method: Based on the assumption that colon cancer evolves because of the stepwise accumulation of a series of genetic mutations, we performed a systematic investigation on the molecular basis of colon cancer through applying restart random walk on the PPI network. To compare functional similarity of patients, we extracted mutation-propagating modules of each patient and calculated their enrichment score in 50 hallmark gene sets. According to functional similarity matrix, we classified colon cancers with positive lymphovascular invasion and the prognosis of molecular subtypes. We determined the molecular characteristics of subtypes by enrichment analysis of subtype-specific genetic mutations. Additionally, we identified potential biomarkers for predicting patients with a high risk of metastasis in stage II through differential analysis of miRNA expression profiles of subtypes. Then we used two independent data sets to construct a random forest classifier and performed 10-fold cross-validation of miRNA biomarkers. Results: Firstly, we identified two molecular subtypes of colon cancer with positive lymphovascular invasion as well as their associated biological characteristics: LVI1=Canonical subtype (110, 85%); LVI2=Metastatic subtype (20, 15%). Secondly, we identified 11 miRNA biomarkers for predicting patients with a high risk of metastasis in tumor stage II. Conclusion: Our findings put forward a detailed classification for colon cancer and provided risk biomarkers for stage II patients to determine whether to take adjuvant chemotherapy after surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it