A biocomputational approach to the study of differential nature of E2 dependent breast cells
Bibliographic record
Abstract
4184 Introduction: Estrogen (E2) is pivotal to normal breast development and in initiation and progression of breast carcinoma. Despite identification of novel E2 targets in breast from SAGE and microarray (MA) generated gene expression data, differences in the adaptive mechanisms favoring proliferation in E2 responsive cancer cells remain unknown, more so in the context of normal breast (NB) cells. Objectives: to study the differential nature of two E2 responsive breast cell lines (E2 deprived), MCF7 and ZR75 from the publicly available SAGE and MA expression data using biocomputational tools. Data analysis: SAGE software extracted significant genes were also analyzed by Audic Claverie, Fisher and Chi2 statistical tests (IDEG6), yielding 230 (MCF7) and 134 (ZR75) genes (p≤0.05, MCF7/NB or ZR75/NB ≥ 5/≤ -5). DAVID tool annotated 133 (ZR75) and 227 (MCF7) genes. Classified genes included 32% (biological process P), 35% (molecular function F), and 32% (component C) leaving ∼ 68% (high) genes unclassified in both. However, GO term utilization (P, F, C) among the classified genes was comparable (P 32.3%, F 34.6%, C 32.3%-ZR75 and P 32.2%, F 32.6%, C 27.8%-MCF7). Pathway differences were also identified by GenMAPP tool. Further, SAGE dataset was compared with Affymetrix MA dataset for MCF7. Finding summary: The two cell lines differentially expressed 9 out of ∼ 75 common genes and 160 (MCF7) and 96 (ZR75) genes were specific. GenMAPP revealed the differences in proteosomal degradation pathways and utilization of energy pathways, such as glycolysis & gluconeogenesis and electron transport chain genes expressing proteins for complexes I, III, IV, V, MCF7 up-regulating more genes (Table 1). In contrast to MCF7, ZR75 down-regulated more immune response genes. Conclusions: while both E2 responsive cells can thrive under harsh E2 deprived conditions, ZR75 cells possess a greater capability of minimizing energy requirements, proteosomal degradation, and compromising immune responses, resembling the more aggressive ER negative breast cancer cells. In contrast, MCF7 cells highly utilize these pathways rendering them less aggressive. These differences can be exploited for specific targeting of hormone dependent breast cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".