Combining Bulk and Single-Cell RNA Sequencing Data to Identify RNA methylation and Autophagy-Related Signatures in Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Abstract Background Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung condition associated with RNA methylation and autophagy. However, the specific autophagy-related genes and RNA methylation regulators involved in COPD development remain unknown. Methods We analyzed COPD and non-COPD patients datasets obtained from the Gene Expression Omnibus database, including Tissue Sequencing Transcriptome (bulk-seq) and single-cell sequencing (scRNA-seq) data. Differentially expressed genes (DEGs) were identified through differential genetic analysis using non-COPD bulk-seq data as the control group and COPD samples were used as the experimental group. Animal experiments were conducted to validate the expression of key genes. COPD model mice were exposed to smoke for four months, and lung function and histopathological changes were assessed. The mRNA and protein expression levels of FTO, IGF2BP2, DDIT3, DNAJB1 , and YTHDF3 were measured using RT-qPCR and Western blotting, respectively. Results We identified FTO, IGF2BP2 , and YTHDF3 as key methylation genes, along with autophagy hub genes DDIT3 and DNAJB1 . Animal experiments showed significantly increased mRNA and protein levels of FTO, YTHDF3 and DNAJB1 and significantly decreased levels of IGF2BP2 in lung tissue of COPD mice compared to the control group. Conclusion Our findings suggest that DDIT3 and DNAJB1 as autophagy hub genes, along with FTO, IGF2BP2 and YTHDF3 as RNA methylation genes, may play crucial roles in the development of COPD. These findings, supported by bulk-seq and scRNA-seq data, contribute novel genetic evidence for understanding the epigenetics of COPD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".