Effects of SRC and STAT3 upon gap junctional, intercellular communication in lung cancer lines.
Bibliographic record
Abstract
BACKGROUND: We have previously demonstrated a positive correlation between SRC and its effector signal transducer and activator of transcription-3 (STAT3), and a reverse relation between SRC and gap junctional communication (GJIC) in seven non-small cell lung cancer (NSCLC) lines. Since a number of oncogenes besides SRC can affect GJIC, here we examined the actual contribution of the SRC/STAT3 axis to GJIC suppression. MATERIALS AND METHODS: SRC and STAT3 activity levels were examined in SK-LuCi-6, LC-T, QU-DB, SW-1573, BH-E, Calu-6, FR-E, SK-MES, H1299, BEN, WT-E, A549 and SHP-77 cells by western blott analysis, probing with antibodies specific for SRC-ptyr418 or STAT3-ptyr705. GJIC was examined by in situ electroporation. RESULTS: Confluence of all cultured NSCLC cells tested induces a dramatic increase in STAT3 activity, which is independent of SRC action. In addition, the LC-T line had high STAT3-705, despite the fact that SRC-418 expression was low, indicating that other, SRC-independent factors must be responsible for STAT3 activation and GJIC suppression in these cells; however, BH-E and SHP-77 cells with low GJIC, both SRC-418 and STAT3-705 expression were low, indicating that GJIC suppression can be independent of the SRC/STAT3 axis altogether. Our results also show that STAT3 inhibition does not restore GJIC in any of the examined lines, while in the non-transformed rat F111 fibroblast line which has extensive GJIC, STAT3 inhibition actually eliminated junctional permeability. CONCLUSION: Our results indicate a further level of complexity in the relationship between SRC, STAT3 and GJIC in NSCLC than what has been previously demonstrated. In addition, STAT3 is actually required for, rather than suppressing GJIC.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".