Cigarette smoke metabolically promotes cancer, via autophagy and premature aging in the host stromal microenvironment
Bibliographic record
Abstract
Cigarette smoke has been directly implicated in the disease pathogenesis of a plethora of different human cancer subtypes, including breast cancers. The prevailing view is that cigarette smoke acts as a mutagen and DNA damaging agent in normal epithelial cells, driving tumor initiation. However, its potential negative metabolic effects on the normal stromal microenvironment have been largely ignored. Here, we propose a new mechanism by which carcinogen-rich cigarette smoke may promote cancer growth, by metabolically "fertilizing" the host microenvironment. More specifically, we show that cigarette smoke exposure is indeed sufficient to drive the onset of the cancer-associated fibroblast phenotype via the induction of DNA damage, autophagy and mitophagy in the tumor stroma. In turn, cigarette smoke exposure induces premature aging and mitochondrial dysfunction in stromal fibroblasts, leading to the secretion of high-energy mitochondrial fuels, such as L-lactate and ketone bodies. Hence, cigarette smoke induces catabolism in the local microenvironment, directly fueling oxidative mitochondrial metabolism (OXPHOS) in neighboring epithelial cancer cells, actively promoting anabolic tumor growth. Remarkably, these autophagic-senescent fibroblasts increased breast cancer tumor growth in vivo by up to 4-fold. Importantly, we show that cigarette smoke-induced metabolic reprogramming of the fibroblastic stroma occurs independently of tumor neo-angiogenesis. We discuss the possible implications of our current findings for the prevention of aging-associated human diseases and, especially, common epithelial cancers, as we show that cigarette smoke can systemically accelerate aging in the host microenvironment. Finally, our current findings are consistent with the idea that cigarette smoke induces the "reverse Warburg effect," thereby fueling "two-compartment tumor metabolism" and oxidative mitochondrial metabolism in epithelial cancer cells.
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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".