miRNA and proteomic dysregulation in non-small cell lung cancer in response to cigarette smoke
Bibliographic record
Abstract
Dysregulation of miRNAs is well associated with the development of non-small cell lung cancer (NSCLC). It is imperative that dysregulation of miRNAs by cigarette smoke will affect the expression of their targets, either leading to the activation of oncoproteins or suppression of tumor suppressor proteins. In this study, we have carried out miRNA sequencing and SILAC-based proteomics analysis of H358 cells chronically exposed to cigarette smoke condensate. miRNA sequencing resulted in the identification of 208 miRNAs, of which 6 miRNAs were found to be significantly dysregulated (fold change ≥ 4, p-value ≤ 0.05) in H358-smoke exposed cells. Proteomic analysis of the smoke exposed cells compared to the parental cells resulted in the quantification of 2,396 proteins, of which 681 proteins were found to be differentially expressed (fold change ≥ 2). Gene ontology based analysis of target proteins revealed enrichment of proteins involved in biological processes driving metabolism and a decrease in expression of proteins associated with immune response in the cells exposed to cigarette smoke. Pathway analysis using Ingenuity Pathway Analysis (IPA) revealed activation of ERK/MAPK and integrin signaling and repression of RhoGDI signaling in H358 smoke exposed cells. We also identified 5 novel miRNA in H358 smoke exposed cells using unassigned reads of small RNA-Seq dataset. In summary, this study indicates that chronic exposure to cigarette smoke leads to widespread dysregulation of miRNAs and their targets, resulting in signaling aberrations in NSCLC. The miRNAs and their targets identified in the study need to be further investigated to explore their role as potential targets and/or molecular markers in NSCLC especially in smokers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".