Butylated Neuropeptide Antagonist Targeting Hypoxia-Induced GRPR Overexpression in Small Cell Lung Cancer
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumour with limited treatment options and a poor prognosis. Hypoxia, a hallmark of solid tumours, contributes to therapeutic resistance and tumour progression. Gastrin-releasing peptide receptor (GRPR) is known to be overexpressed in SCLC; however, its regulation under hypoxic conditions is not well described. In this study, we demonstrate that hypoxia significantly enhances GRPR expression in SCLC cell lines, COR-L24 and DMS79, as confirmed by Western blot, immunofluorescence, and flow cytometric analysis of binding with fluorescein isothiocyanate-labelled bombesin (BBN-FITC), a known GRPR ligand. To exploit this upregulation, we synthesised a previously discovered butylated neuropeptide antagonist (BU peptide) using a new method of solid-phase peptide synthesis (SPPS) by Boc chemistry and evaluated its therapeutic potential. BU peptide exhibited potent, dose-dependent cytotoxicity in both cell lines, with significantly greater efficacy under hypoxic conditions compared to normoxia. Mechanistic studies revealed that BU peptide inhibits GRP-GRPR-mediated activation of the PI3K/Akt and MAPK/ERK signalling pathways, known to be key regulators of tumour cell survival and proliferation. Moreover, BU peptide induced robust caspase 3/7-mediated apoptosis, especially under hypoxic conditions. These findings suggest that GRPR is a hypoxia-inducible target in SCLC and demonstrate that a synthetically optimised BU peptide antagonist exerts selective efficacy against hypoxic tumour cells, outperforming conventional chemotherapy agents. These findings provide new mechanistic insights into SCLC and suggest translational potential to inform the development of future treatment strategies for this and other hypoxia-driven malignancies.
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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) | 0.001 | 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".