Inflammatory cytokines and specific factors influencing lung cancer progression
Bibliographic record
Abstract
Lung cancer (LC) is one of the leading causes of cancer-related morbidity and mortality worldwide. Inflammation is a driver of cancer initiation and progression, affecting processes such as angiogenesis, antiapoptotic pathways, and DNA adduct formation. Cytokines are small proteins that can accelerate or slow tumor growth by controlling associated signaling processes such as cell proliferation, metastasis, and apoptosis. This review reveals the role of tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), transforming growth factor-beta (TGF-β), and interleukins in LC. Macrophages play a role in non-small cell lung cancer (NSCLC) pathogenesis and are associated with poor prognosis. A nested case–control study revealed that elevated concentrations of IL-6 and IL-8 were strongly associated with the risk of LC. Specifically, the odds ratio (OR) for IL-6 and IL-8 in former smokers (fourth quartile vs. first quartile) was 2.70 (95% confidence interval [CI], 1.55–4.70) and 2.83 (95% CI, 1.18–6.75), respectively. Because C-reactive protein levels are elevated in patients with NSCLC with larger and higher-grade tumors, CRP has been identified as a systemic indicator of chronic inflammation. Insulin-like growth factors influence cellular signal transduction pathways and contribute to tumorigenesis. Soluble tumor necrosis factor receptors have been explored for their role in NSCLC prognosis, highlighting their association with chromogranin. Transient receptor potential cation channel, subfamily M, member 7 (TRPM7), urokinase plasminogen activator, matrix metalloproteinases, and monocyte chemoattractant protein-1 have been identified with a focus on their expression patterns and prognostic significance in LC tissues. Moreover, lung angiogenesis induces vascular endothelial growth factor, soluble intercellular adhesion molecule-1, myeloperoxidase, and tissue inhibitors of metalloproteinase expressions. In conclusion, this review thoroughly summarized the inflammatory cytokines and specific factors influencing LC, providing the basis for further research on potential treatment approaches. • Inflammation is the body's normal response to injury or infection. • Cytokines like tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), and interleukin (IL) regulate inflammation and immune function. • Chronic inflammation increases the risk of lung cancer (LC) and promotes tumor growth. • Targeting cytokines such as IL-6 and TNF-α may offer better therapies for LC. • Immune modulation through cytokine inhibition could reduce tumor-associated inflammation and enhance treatment efficacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".