Identification of Corneal Neovascularization–Related Long Noncoding RNAs Through Microarray Analysis
Bibliographic record
Abstract
PURPOSE: To reveal the role of long noncoding RNAs (lncRNAs) in corneal neovascularization (CN). METHODS: We established a murine CN model and performed lncRNA expression profiling to identify differentially expressed lncRNAs between normal and vascularized corneas. Based on Pearson correlation analysis, an lncRNA/mRNA coexpression network was constructed. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of lncRNA-coexpressed mRNAs were conducted to determine the related biological modules and pathological pathways. Real-time polymerase chain reactions were carried out to detect the expression pattern of lncRNA in the clinical samples. RESULTS: A total of 154 differentially expressed lncRNAs were identified between vascularized and normal corneas, including 60 downregulated lncRNAs and 94 upregulated lncRNAs. GO enrichment analysis of lncRNA-coexpressed mRNAs indicated that the biological modules were correlated with extracellular region, DNA binding, and immune response. KEGG pathway analysis indicated that "pathways in cancer" was the most enriched signaling pathway. Moreover, the human ortholog of NR_033585 and lincRNA:chr8:129102060-129109035 reverse strand was found to be differentially expressed between vascularized and avascular corneas. CONCLUSIONS: This study provides a novel insight into CN pathogenesis. The intervention of dysregulated lncRNAs may become potential targets for the prevention and treatment of ocular vascular diseases.
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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 | 0.001 | 0.001 |
| 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".