Network Propagation-based Identification of Oligometastatic Biomarkersin Metastatic Colorectal Cancer
Bibliographic record
Abstract
Background: The oligometastatic disease has been proposed as an intermediate state between primary tumor and systemically metastatic disease, which has great potential curable with locoregional therapies. However, since no biomarker for the identification of patients with true oligometastatic disease is clinically available, the diagnosis of oligometastatic disease remains controversial. Objective: We aim to identify potential biomarkers of colorectal cancer patients with true oligometastatic states, who will benefit most from local therapy. Methods: This study retrospectively analyzed the transcriptome profiles and clinical parameters of 307 metastatic colorectal cancer patients. A novel network propagation method and network-based strategy were combined to identify oligometastatic biomarkers to predict the prognoses of metastatic colorectal cancer patients. Results: We defined two metastatic risk groups according to twelve oligometastatic biomarkers, which exhibit distinct prognoses, clinicopathological features, immunological characteristics, and biological mechanisms. The metastatic risk assessment model exhibited a more powerful capacity for survival prediction compared to traditional clinicopathological features. The low-MRS group was most consistent with an oligometastatic state, while the high-MRS might be a potential polymetastatic state, which leads to the divergence of their prognostic outcomes and response to treatments. We also identified 22 significant immune check genes between the high-MRS and low- MRS groups. The difference in molecular mechanism between the two metastatic risk groups was associated with focal adhesion, nucleocytoplasmic transport, Hippo, PI3K-Akt, TGF-β, and EMCreceptor interaction signaling pathways. Conclusion: Our study provided a molecular definition of the oligometastatic state in colorectal cancer, which contributes to precise treatment decision-making for advanced patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".