Expression Analysis of Five Different Long Non-Coding Ribonucleic Acids in Nonsmall-Cell Lung Carcinoma Tumor and Tumor-Derived Exosomes
Bibliographic record
Abstract
Long non-coding ribonucleic acids (LncRNAs) are recently known for their role in regulating gene expression and the development of cancer. Controversial results indicate a correlation between the tissue expression of LncRNA and LncRNA content of extracellular vesicles. The present study aimed to evaluate the expression of different LncRNAs in non-small cell lung cancer (NSCLC) patients in tumor tissue, adjacent non-cancerous tissue (ANCT), and exosome-mediated lncRNA. Tumor and ANCT, as well as serum samples of 168 patient with NSCLC, were collected. The GHSROS, HNF1A-AS1, HOTAIR, HMlincRNA717, and LINCRNA-p21 relative expressions in tumor tissue, ANCT, and serum exosomes were evaluated in NSCLC patients. Among 168 NSCLC samples, the expressions of GHSROS (REx = 3.64, p = 0.028), HNF1A-AS1 (REx = 2.97, p = 0.041), and HOTAIR (REx = 2.9, p = 0.0389) were upregulated, and the expressions of HMlincRNA717 (REx = −4.56, p = 0.0012) and LINCRNA-p21 (REx = −5.14, p = 0.00334) were downregulated in tumor tissue in contrast to ANCT. Moreover, similar statistical differences were seen in the exosome-derived RNA of tumor tissues in contrast to ANCT samples. A panel of the five lncRNAs demonstrated that the area under the curve (AUC) for exosome and tumor was 0.937 (standard error: 0.012, p value < 0.0001). LncRNAs GHSROS, HNF1A-AS1, and HOTAIR showed high expression in tumor tissue and exosome content in NSCLC, and a panel that consisted of all five lncRNAs improved diagnosis of NSCLC.
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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 | 0.000 | 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) | 0.001 | 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".