Bibliographic record
Abstract
It is proposed that neuropeptide production by tumours is an important part of a special process of oncogenic transformation rather than a pre-existing condition of progenitor cells; this concept is called Selective Tumour gene Expression of Peptides essential for Survival (STEPS). All small-cell lung cancers and breast cancers evidently express the vasopressin gene, and this gene seems to be structurally normal in all but exceptional cases. Vasopressin gene expression in cancer cells leads to the production of both normal and abnormal forms of tumour vasopressin mRNA and proteins. Although the necessary post-translational processing enzymes are expressed in these cells, most processing seems to be extragranular, and most of the protein products become components of the plasma membrane. Small-cell lung cancer and breast cancer cells also express normal genes for all vasopressin receptors and produce normal vasopressin receptor mRNAs and V1a and V1b receptor proteins, and the vasopressin-activated calcium mobilising (VACM) protein; plus both normal and abnormal forms of the V2 receptor. Through these receptors, vasopressin exercises multifaceted effects on tumour growth and metabolism. A normal protein vasopressin gene promoter seems to be present in small-cell lung cancer cells, and this promoter contains all of the transcriptional elements known to be involved in gene regulation within hypothalamic neurones. Since these elements largely account for regulation of tumour gene expression observed in vitro, it is likely that as yet unknown factors are selectively produced by tumours in vivo to account for the observed seemingly autonomous or unregulated production of hormone in tumour patients. Promoter elements thought to be responsible for selective vasopressin gene expression in small-cell lung cancer probably include an E-box and a neurone restrictive silencer element close to the transcription start site. It is possible that transcription factors acting at these same elements can explain selective vasopressin expression, not only in small-cell tumours, but also in all other tumours such as breast cancer. By extrapolation, similar mechanisms might also be responsible for the expression of additional features that characterize the 'neuroendocrine' profile of these cancers.
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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".