Integrated analysis of genome‐wide copy number alterations and gene expression in microsatellite stable, CpG island methylator phenotype‐negative colon cancer
Bibliographic record
Abstract
Microsatellite stable (MSS), CpG island methylator phenotype (CIMP)-negative colorectal tumors, the most prevalent molecular subtype of colorectal cancer, are associated with extensive copy number alteration (CNA) events and aneuploidy. We report on the identification of characteristic recurrent CNA (with frequency >25%) events and associated gene expression profiles for a total of 40 paired tumor and adjacent normal colon tissues using genome-wide microarrays. We observed recurrent CNAs, namely gains at 1q, 7p, 7q, 8p12-11, 8q, 12p13, 13q, 20p, 20q, Xp, and Xq and losses at 1p36, 1p31, 1p21, 4p15-12, 4q12-35, 5q21-22, 6q26, 8p, 14q, 15q11-12, 17p, 18p, 18q, 21q21-22, and 22q. Within these genomic regions we identified 356 genes with significant differential expression (P < 0.0001 and ±1.5-fold change) in the tumor compared to adjacent normal tissue. Gene ontology and pathway analyses indicated that many of these genes were involved in functional mechanisms that regulate cell cycle, cell death, and metabolism. An amplicon present in >70% of the tumor samples at 20q11-20q13 contained several cancer-related genes (AHCY, POFUT1, RPN2, TH1L, and PRPF6) that were upregulated and demonstrated a significant linear correlation (P < 0.05) for gene dosage and gene expression. Copy number loss at 8p, a CNA associated with adenocarcinoma and poor prognosis, was observed in >50% of the tumor samples and demonstrated a significant linear correlation for gene dosage and gene expression for two potential tumor suppressor genes, MTUS1 (8p22) and PPP2CB (8p12). The results from our integration analysis illustrate the complex relationship between genomic alterations and gene expression in colon cancer.
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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 | 0.000 | 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 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".