Abstract 2001: GATA2: Potential role as a prostate cancer metastasis-driving gene
Bibliographic record
Abstract
Abstract Metastasis is thought to result from changes in the expression of specific metastasis-driving genes leading to a cascade of activated downstream genes that set the metastatic process in motion. The present study was aimed at identifying such metastasis-driving genes in prostate cancer for potential therapy and identification of primary prostate cancers that are likely to metastasize. To this end, a differential gene expression profile was established of metastatic LTL-313H and non-metastatic LTL-313B prostate cancer tissue xenografts, derived from one patient's specimen using sub-renal capsule grafting technology. The profile was then subjected to integrative analysis using the Ingenuity Upstream Regulator Analysis tool. Six candidate master regulatory genes were identified, including GATA2, a gene encoding a pioneer factor in prostate cancer. Elevated GATA2 expression in clinical metastatic prostate cancer tissues was found to correlate with poor patient prognosis. Furthermore, GATA2 gene silencing in human prostate cancer LNCaP cells led to a marked reduction in cell migration, tissue invasion, focal adhesion disassembly and to a drastic change in cell transcriptome. Furthermore, 582 genes were identified that are (i) differentially expressed after GATA2 gene silencing in LNCaP cells and (ii) whose changes in gene expression significantly correlated with changes in GATA2 expressions in a MSKCC prostate cancer patient cohort. Taken together, the data suggest that GATA2 could represent a prostate cancer metastasis-driving gene and that the expression patterns of GATA2 and its associated-genes could serve as signatures (biomarkers) for poor prognosis in prostate cancer. Citation Format: Yan Ting Chiang, Kendric Wang, Francesco Crea, Colin Collins, Peter Gout, Yuzhuo Wang. GATA2: Potential role as a prostate cancer metastasis-driving gene. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2001. doi:10.1158/1538-7445.AM2014-2001
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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 | 0.000 |
| 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) | 0.003 | 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".