The Role of Chromosomal Instability and Epigenetics in Colorectal Cancers Lacking<i>β</i>-Catenin/TCF Regulated Transcription
Bibliographic record
Abstract
All colorectal cancer cell lines except RKO displayed active β-catenin/TCF regulated transcription. This feature of RKO was noted in familial colon cancers; hence our aim was to dissect its carcinogenic mechanism. MFISH and CGH revealed distinct instability of chromosome structure in RKO. Gene expression microarray of RKO versus 7 colon cancer lines (with active Wnt signaling) and 3 normal specimens revealed 611 differentially expressed genes. The majority of the tested gene loci were susceptible to LOH in primary tumors with various β-catenin localizations as a surrogate marker for β-catenin activation. The immunohistochemistry of selected genes (IFI16, RGS4, MCTP1, DGKI, OBCAM/OPCML, and GLIPR1) confirmed that they were differentially expressed in clinical specimens. Since epigenetic mechanisms can contribute to expression changes, selected target genes were evaluated for promoter methylation in patient specimens from sporadic and hereditary colorectal cancers. CMTM3, DGKI, and OPCML were frequently hypermethylated in both groups, whereas KLK10, EPCAM, and DLC1 displayed subgroup specificity. The overall fraction of hypermethylated genes was higher in tumors with membranous β-catenin. We identified novel genes in colorectal carcinogenesis that might be useful in personalized tumor profiling. Tumors with inactive Wnt signaling are a heterogeneous group displaying interaction of chromosomal instability, Wnt signaling, and epigenetics.
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.
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 | 0.000 |
| 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) | 0.001 | 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".