Long non-coding RNAs and exosomal lncRNAs: Potential functions in lung cancer progression, drug resistance and tumor microenvironment remodeling
Bibliographic record
Abstract
Among the different kinds of tumors threatening human life, lung cancer is one that is commonly observed in both males and females. The aggressive behavior of lung cancer and interactions occurring in tumor microenvironment enhances the malignancy of this tumor. The lung tumor cells have demonstrated capacity in developing chemo- and radio-resistance. LncRNAs are a category of non-coding RNAs that do not encode proteins, but their aberrant expression is responsible for tumor development, especially lung cancer. In the present review, we focus on both lncRNAs and exosomal lncRNAs in lung cancer, and their ability in regulating proliferation and metastasis. Cell cycle progression and molecular mechanisms related to lung cancer metastasis such as EMT and MMPs are regulated by lncRNAs. LncRNAs interact with miRNAs, STAT, Wnt, EZH2, PTEN and PI3K/Akt signaling pathways to affect progression of lung cancer cells. LncRNAs demonstrate both tumor-suppressor and tumor-promoting functions in lung cancer. They can be considered as biomarkers in lung cancer and especially exosomal lncRNAs present in body fluids are potential tools for minimally invasive diagnosis. Furthermore, we discuss regulation of lncRNAs by anti-cancer drugs and genetic tools as well as the role of these factors in therapy response of lung cancer cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".