Airway Microbiota Is Associated with Upregulation of the PI3K Pathway in Lung Cancer
Bibliographic record
Abstract
RATIONALE: In lung cancer, upregulation of the PI3K (phosphoinositide 3-kinase) pathway is an early event that contributes to cell proliferation, survival, and tissue invasion. Upregulation of this pathway was recently described as associated with enrichment of the lower airways with bacteria identified as oral commensals. OBJECTIVES: We hypothesize that host-microbe interactions in the lower airways of subjects with lung cancer affect known cancer pathways. METHODS: Airway brushings were collected prospectively from subjects with lung nodules at time of diagnostic bronchoscopy, including 39 subjects with final lung cancer diagnoses and 36 subjects with noncancer diagnoses. In addition, samples from 10 healthy control subjects were included. 16S ribosomal RNA gene amplicon sequencing and paired transcriptome sequencing were performed on all airway samples. In addition, an in vitro model with airway epithelial cells exposed to bacteria/bacterial products was performed. MEASUREMENTS AND MAIN RESULTS: The composition of the lower airway transcriptome in the patients with cancer was significantly different from the control subjects, which included up-regulation of ERK (extracellular signal-regulated kinase) and PI3K signaling pathways. The lower airways of patients with lung cancer were enriched for oral taxa (Streptococcus and Veillonella), which was associated with up-regulation of the ERK and PI3K signaling pathways. In vitro exposure of airway epithelial cells to Veillonella, Prevotella, and Streptococcus led to upregulation of these same signaling pathways. CONCLUSIONS: The data presented here show that several transcriptomic signatures previously identified as relevant to lung cancer pathogenesis are associated with enrichment of the lower airway microbiota with oral commensals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".