Molecular cytogenetic analysis of head and neck squamous cell carcinoma: By comparative genomic hybridization, spectral karyotyping, and expression array analysis
Bibliographic record
Abstract
BACKGROUND: A combination of molecular cytogenetic and expression array analysis has been performed on head and neck squamous cell carcinoma (HNSCC) of the oral cavity and supraglottis. These studies were performed to identify consensus regions of chromosomal imbalance and structural rearrangement to determine whether genes located in these genomic regions are subject to alterations in gene expression. Such combinatorial studies may help to identify recurrent patterns of altered gene expression in the context of specific chromosomal changes. METHODS: Comparative genomic hybridization (CGH) was used to identify net genomic imbalances and spectral karyotyping (SKY) to visualize the numerical and structural chromosomal changes in metaphase preparations. Expression microarray analysis of HNSCC cell lines and primary tongue tumors was also performed to identify genes that were commonly overexpressed or underexpressed compared with adjacent normal tissue. RESULTS: CGH detected gains at 3q (64%), 8q (45%) and 6q22-qter (45%) and losses at 18q22-qter (27%). SKY analysis of seven cell lines identified frequent structural rearrangement of the following chromosomal regions: 3q, 5p13-q11.2, 5q32-q34, 7p12-q11.2, 8p12-q12, 9p, 10p, 13p13-q12, 14q11.1-q11.2, 15p13-q11.2, 16p11.1-q11.1, 18q22-q23, and 22p13-q11.2. Consistent deregulation of interleukin 8, integrin alpha-6, c-MYC, epithelial discoidin domain receptor 1, and sterol regulatory element binding protein were apparent by expression analysis. Interestingly, some of these genes map to regions of genomic imbalance and chromosomal rearrangement as determined by our molecular cytogenetic analysis. CONCLUSIONS: In this small study, a combinatorial analysis using SKY, CGH, and microarray provides a model linking the changes in gene expression to changes in chromosomal dosage and structure. This approach has identified a subset of genetic changes that provide new opportunities for investigating the genetic basis of tumorigenesis in HNSCC.
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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 | 0.001 |
| 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) | 0.001 | 0.001 |
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".