Non-coding RNAs and regulation of the PI3K signaling pathway in lung cancer: Recent insights and potential clinical applications
Bibliographic record
Abstract
Lung cancer (LC) is one of the most common causes of cancer-related death worldwide. It has been demonstrated that the prognosis of current drug treatments is affected by a variety of factors, including late stage, tumor recurrence, inaccessibility to appropriate treatments, and, most importantly, chemotherapy resistance. Non-coding RNAs (ncRNAs) contribute to tumor development, with some acting as tumor suppressors and others as oncogenes. The phosphoinositide 3-kinase (PI3Ks)/AKT serine/threonine kinase pathway is one of the most important common targets of ncRNAs in cancer, which is widely applied to modulate the cell cycle and a variety of biological processes, including cell growth, mobility survival, metabolic activity, and protein production. Discovering the biology of ncRNA-PI3K/AKT signaling may lead to advances in cancer diagnosis and treatment. As a result, we investigated the expression and role of PI3K/AKT-related ncRNAs in clinical characteristics of lung cancer, as well as their functions as potential biomarkers in lung cancer diagnosis, prognosis, and treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".