Role of MicroRNAs in Progression and Recurrence of Early-Stage Lung Adenocarcinoma
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, and the majority of cases (77%) are not diagnosed until the disease has spread to regional lymph nodes or distant sites. Even among Non-Small Cell Lung Cancer (NSCLC) adenocarcinoma patients who have been diagnosed early and where there has been no spread to lymph nodes, recurrence after surgical intervention is high. Improved understanding of the molecular alterations involved in aggressivity and recurrence of these tumors may provide better biomarkers for the identification of patients who would benefit from adjuvant chemotherapy. By comparing the expression of microRNAs in advanced Stage II/III tumors with those expressed in earlier Stage I tumors, we aimed to identify differentially expressed molecular biomarkers that could underly progression and recurrence of Stage I tumors. This pilot study utilized TaqMan qPCR assays to assess the expression of microRNAs in tumor tissue, matched normal tissue and plasma samples from Stage I and Stage II/III lung adenocarcinoma patients. Seven microRNAs were identified from plasma that could distinguish between patients with Stage I and Stage II/III adenocarcinoma. The up-regulation of miR-29a in plasma of patients with later-stage adenocarcinoma would result in enhanced expression of several molecules involved in integrin signaling, migration and proliferation. Analysis of differential expression of microRNAs in later-stage compared to early-stage lung adenocarcinoma implicates focal adhesion and ECM-receptor pathways in progression and recurrence. Plasma miR-29a is a promising biomarker that can be assessed non-invasively and whose clinical utility should be pursued.
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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 | 0.003 | 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 | 0.001 |
| 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".