Integrated bulk and single-cell RNA-sequencing reveals SPOCK2 as a novel biomarker gene in the development of congenital pulmonary airway malformation
Bibliographic record
Abstract
Abstract Background Congenital pulmonary airway malformation (CPAM) is the most frequent pulmonary developmental malformation and the pathophysiology remains poorly understood. This study aimed to identify the characteristic gene expression patterns and the marker genes essential to CPAM. Methods Tissues from the cystic area displaying CPAM and the area of normal appearance were obtained during surgery. Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) were performed for integrating analysis. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify specifically expressed genes to CPAM. Results In total, 2074 genes were significantly differentially expressed between the CPAM and control areas. Of these differentially expressed genes (DEGs), 1675 genes were up-regulated and 399 genes were down-regulated. Gene ontology analysis revealed these DEGs were specifically enriched in ciliated epithelium and involved in immune response. We also identified several CPAM-related modules by iWGCNA, among them, P15_I4_M3 module was the most influential module for distinguishing CPAMs from controls. By combining the analysis of the expression dataset from RNA-seq and scRNA-seq, SPOCK2, STX11, and ZNF331 were highlighted in CPAM. Conclusions Through our analysis of expression datasets from both scRNA-seq and bulk RNA-seq of tissues obtained from patients with CPAM, we identified the characteristic gene expression patterns associated with the condition. Our findings suggest that SPOCK2 could be a potential biomarker gene for the diagnosis and therapeutic target in the development of CPAM, whereas STX11 and ZNF331 might serve as prognostic markers for this condition. Further investigations with larger samples and function studies are necessary to confirm the involvement of these genes in CPAM.
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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) | 0.000 | 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) | 0.053 | 0.001 |
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; both teacher heads agree on what is shown here.
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".