ImmunoMet Oncogenesis: A New Concept to Understand the Molecular Drivers of Cancer
Bibliographic record
Abstract
The appearance of cancer progresses through a multistep process that includes genetic, epigenetic, mutational, inflammatory and metabolic disturbances to signaling pathways within an organ. The combined influence of these changes will dictate the growth properties of the cells; the direction of further malignancy depends on the severity of these "disturbances". The molecular mechanisms driving abnormal inflammation and metabolism are beginning to be identified and, in some cases, are quite prominent in pre-condition states of cancer and are significant drivers of the malignant phenotype. As such, utilizing signaling pathways linked to inflammation and metabolism as biomarkers of cancer is an emerging method and includes pathways beyond those well characterized to drive metabolism or inflammation. In this review, we will discuss several emerging elements influencing proliferation, inflammation and metabolism that may play a part as drivers of the cancer phenotype. These include AMPK and leptin (linked to metabolism), NOD2/RIPK2, TAK1 (linked to inflammation), lactate and pyruvate transporters (monocarboxylate transporter [MCT], linked to mitochondrial biogenesis and metabolism) and RASSF1A (linked to proliferation, cell death, cell cycle control, inflammation and epigenetics). We speculate that the aforementioned elements are important drivers of carcinogenesis that should be collectively referenced as being involved in "ImmunoMET Oncogenesis", a new tripartite description of the role of elements in driving cancer. This term would suggest that for a better understanding of cancer, we need to understand how proliferation, inflammation and metabolic pathways are impacted and how they influence classical drivers of malignant transformation in order to drive ImmunoMET oncogenesis and the malignant state.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".