Abstract 88: Comparative tumorigenesis of progranulin and fibroblast growth factor 4 and in adrenocortical carcinoma cells.
Bibliographic record
Abstract
Abstract Adrenal carcinomas are almost always fatal, but the mechanisms that influence transition from low malignancy adenomas to metastatic carcinomas are ill defined. SW-13 adrenocortical carcinoma cells are not tumorigenic in nude mice and do not support anchorage-independent growth at low cell density unless stimulated by secreted forms of fibroblast growth factors (FGF) family, such as FGF4, or the growth factor-like proteins progranulin (PGRN) which is over produced by many cancers. This growth factor-dependent acquisition of tumorigenesis may model the transition from benign to malignant adrenocortical cancer. Both PGRN and FGF4 were shown to stimulate phosphorylation of MEK1/2 in SW-13 cells. The aim of this study is to investigate the tumorigenic mechanisms of PGRN and FGF, in particular to identify “core” malignancy responses shared by both tumorigenic pathways. We employed Affymetrix Human Genome U133A microarrays to examine the transcriptome profiles in SW-13 cells with elevated PGRN or FGF4. We employed Ingenuity Pathway Analysis (IPA®) to visualize differentially expressed genes in the context of biological pathways. In SW-13 cells with elevated PGRN, those transcripts that were significantly up-regulated were related to cell morphology, cellular assembly and organization, nervous system development and function, carbohydrate metabolism, molecular transport. In cells with elevated FGF4, significantly up-regulated molecules were associated with lipid metabolism, molecular transport, small molecule biochemistry, cell death. 80 transcripts that were up-regulated in common by FGF and PGRN were identified, and were significantly associated with gene expression, cell death, cellular development. 28 of those genes were related to cancer, including nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha, v-rel reticuloendotheliosis viral oncogene homolog (avian), BCL2-associated X protein, early growth response 4, nuclear factor of kappa light polypeptide gene enhancer in B-cells 2 (p49/p100), etc. 65 FGF and PGRN common down-regulated genes were identified, and were significantly associated with reproductive system development and function, cellular development, cellular growth and proliferation. We found, however, that the majority of genes were regulated differentially between PGRN and FGF. Our results suggest that PGRN and FGF4 share common signals which contribute to tumorgenesis, but also reveal that the common malignant phenotype attributed to both PGRN and FGF4 differs significantly at the transcriptional level suggesting unique pathways linking PGRN and FGF to tumor progression in these cells. Citation Format: Yonghua Zhang, Amin Ismail, Andrew Bateman. Comparative tumorigenesis of progranulin and fibroblast growth factor 4 and in adrenocortical carcinoma cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 88. doi:10.1158/1538-7445.AM2013-88
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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) | 0.002 | 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".