DNA fingerprinting tags novel altered chromosomal regions and identifies the involvement of SOX5 in the progression of prostate cancer
Bibliographic record
Abstract
Identification of genomic alterations associated with the progression of prostate cancer may facilitate the better understanding of the development of this highly variable disease. Matched normal, premalignant high-grade prostatic intraepithelial neoplasia and invasive prostate carcinoma cells were procured by laser capture microdissection (LCM) from human radical prostatectomy specimens. From these cells, comparative DNA fingerprints were generated by a modified PCR-based technique called scanning of microdissected archival lesion (SMAL)-PCR. Recurrent polymorphic fingerprint fragments were used in tagging altered chromosomal regions. Altered regions were found at cytobands 1p31.3, 1q44, 2p23.1, 3p26.3, 3q22.3, 4q22.3, 4q35.2, 5q23.2, 8q22.3, 8q24.13, 9q21.3, 9q22.32, 10q11.21, 11p13, 12p12.1, 13q12.1, 16q12.2 and 18q21.31. Candidate genes in the surrounding area that may possibly harbor mutations that change normal prostatic cells to progress into their tumor stages were proposed. Of these fragments, a 420 bp alteration, absent in all 26 normal samples screened, was observed in 2 tumors. This fragment was cloned, sequenced and localized to chromosome 12p12.1. Within this region, candidate gene sex determining region Y-box 5 (SOX5) was proposed. Further studies of SOX5 in cell lines, xenografts and human prostate specimens, at both the RNA and protein levels, found overexpression of the gene in tumors. This overexpression was then subsequently found by fluorescent in situ hybridization to be caused by amplification of the region. In conclusion, our results suggest LCM coupled with SMAL-PCR DNA fingerprinting is a useful method for the screening and identification of chromosomal regions and genes associated with cancer development. Further, overexpression of SOX5 is associated with prostate tumor progression and early development of distant metastasis.
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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 | 0.000 |
| 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".