A multidimensional integration analysis reveals potential bridging targets in the process of colorectal cancer liver metastasis
Bibliographic record
Abstract
Approximately 9% of cancer-related deaths are caused by colorectal cancer. Liver metastasis is a major factor for the high colorectal cancer mortality rate. However, the molecular mechanism underlying colorectal cancer liver metastasis remains unclear. Using a global and multidimensional integration approach, we studied sequencing data, protein-protein interactions, and regulation of transcription factor and non-coding RNAs in primary tumor samples and liver metastasis samples to unveil the potential bridging molecules and the regulators that functionally link different stages of colorectal cancer liver metastasis. Primary tumor samples and liver metastasis samples had modules with significant overlap and crosstalk from which we identified several bridging genes (e.g. KNG1 and COX5B), transcription factors (e.g. E2F4 and CDX2), microRNAs (e.g. miR-590-3p and miR-203) and lncRNAs (e.g. lincIRX5 and lincFOXF1) that may play an important role in the process of colorectal cancer liver metastasis. This study enhances our understanding of the genetic alterations and transcriptional regulation that drive the metastatic process, but also provides the methodology to guide the studies on other metastatic cancers.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".