Mechanisms Contributing to the Comorbidity of COPD and Lung Cancer
Bibliographic record
Abstract
Lung cancer and chronic obstructive pulmonary disease (COPD) often co-occur, and individuals with COPD are at a higher risk of developing lung cancer. While the underlying mechanism for this risk is not well understood, its major contributing factors have been proposed to include genomic, immune, and microenvironment dysregulation. Here, we review the evidence and significant studies that explore the mechanisms underlying the heightened lung cancer risk in people with COPD. Genetic and epigenetic changes, as well as the aberrant expression of non-coding RNAs, predispose the lung epithelium to carcinogenesis by altering the expression of cancer- and immune-related genes. Oxidative stress generated by tobacco smoking plays a role in reducing genomic integrity, promoting epithelial-mesenchymal-transition, and generating a chronic inflammatory environment. This leads to abnormal immune responses that promote cancer development, though not all smokers develop lung cancer. Sex differences in the metabolism of tobacco smoke predispose females to developing COPD and accumulating damage from oxidative stress that poses a risk for the development of lung cancer. Dysregulation of the lung microenvironment and microbiome contributes to chronic inflammation, which is observed in COPD and known to facilitate cancer initiation in various tumor types. Further, there is a need to better characterize and identify the proportion of individuals with COPD who are at a high risk for developing lung cancer. We evaluate possible novel and individualized screening strategies, including biomarkers identified in genetic studies and exhaled breath condensate analysis. We also discuss the use of corticosteroids and statins as chemopreventive agents to prevent lung cancer. It is crucial that we optimize the current methods for the early detection and management of lung cancer and COPD in order to improve the health outcomes for a large affected population.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".