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Gene expression profiling of the human prostate zones

2006· article· en· W1997035295 on OpenAlexaff
Leonie van der Heul-Nieuwenhuijsen, Peter J.M. Hendriksen, Theodorus van der Kwast, Guido Jenster

Bibliographic record

VenueBritish Journal of Urology · 2006
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMount Sinai Hospital
FundersErasmus+
KeywordsStromal cellProstateGene expression profilingProstate cancerMicroarrayMicroarray analysis techniquesBiologyGene expressionGeneHyperplasiaPCA3Reverse transcription polymerase chain reactionCancer researchPathologyMedicineCancerGeneticsEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate differences in gene expression in different zones of the prostate by microarray analyses, to better understand why aggressive tumours predominantly occur in the peripheral zone (PZ), whereas benign prostatic hyperplasia (BPH) occurs almost exclusively in the transition zone (TZ). MATERIALS AND METHODS: Expression profiling of both prostate zones was done by microarray analysis. Reverse transcription-polymerase chain reaction (RT-PCR) of the top 18 genes confirmed the microarray analyses. RT-PCR with common cell-type markers indicated that the differential expression between the zones was not caused by an unequal distribution of different cell types. Primary stromal and epithelial prostate cells were used to study cell type expression in the 12 highest differentially expressed zonal-specific genes. RESULTS: In all, 346 genes were identified as preferentially expressed in the TZ or PZ. A few of the TZ-specific genes, including ASPA, FLJ10970 and COCH, were also stroma-specific. Comparisons with other microarray studies showed that gene expression profiles of prostate cancer and BPH correlate with the expression profiles of the PZ and TZ, respectively. CONCLUSION: Gene expression differs between the PZ and TZ of the prostate, and stromal-epithelial interactions might be responsible for the distinct zonal localization of prostate diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.259
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations74
Published2006
Admission routes1
Has abstractyes

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