Comprehensive serial analysis of gene expression of the cervical epithelium transcriptome
Bibliographic record
Abstract
3715 Background: Approximately 500,000 women are diagnosed with cervical cancer worldwide each year and more than half of them will die from this disease. Investigation of genes expressed in preinvasive lesions compared to those expressed by normal cervical epithelium will yield insight to the early stages of disease. As such, establishing a baseline which to compare to, is important in elucidating the abnormal biology of disease. In this study we consider the normal cervical tissue transcriptome and investigate the similarities and differences in relation to CIN III by Long-SAGE (L-SAGE). Objectives: (1) to characterize global gene expression patterns in normal cervical tissue and CIN III by functional group (2) to establish a gene signature unique to normal cervical tissue (3) to identify genes highly expressed in normal cervical tissue showing altered expression in CIN III. Materials and Methods: Two normal and two CIN III cervical biopsies collected just prior to LEEP were collected at the BCCA in Vancouver and the LEEP specimen was used to establish the biopsy grade by a pathologist. Biopsy specimens were stored in RNAlater and were used for L-SAGE library construction. Libraries were sequenced and individual sequence tags mapped using NCBI SAGE Genie. High expressers in normal libraries (> 500 tags per million) were characterized and investigated for changes in expression level in CIN III using a modified Z-Test. Results: Four SAGE libraries were sequenced to an average of 172,848 tags deep. 118 unique tags were highly expressed in normal cervical tissue while 107 of these mapped to unique genes. The most highly expressed gene families were, ribosomal, calcium-binding and keratinizing in normal cervical epithelia. The same genes were assessed for expression in CIN III. Five genes showed altered expression in CIN III when compared to normal and three were confirmed in a new tissue panel by QRT PCR. In addition, we have identified twelve unique Human Papillomavirus 16 (HPV 16) tags in the CIN III libraries. Conclusion: Gene expression changes that parallel the progression stages will help to furthering our understanding of cervical cancer development and identify novel treatment targets. Establishing a baseline which to compare such aberrations is essential for this effort to proceed. We have focused our investigation on those tags most abundantly expressed in normal cervical tissue and have evaluated these genes in terms of tissue specificity, conserved expression and altered expression in CIN III lesions. This work is supported by Genome Canada and NIH P01 CA82710-06.
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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 | 0.000 | 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".