Transcriptomic analyses of lung tissues reveal key genes associated with progression of systemic sclerosis-interstitial lung disease (SSc-ILD)
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis-interstitial lung disease (SSc-ILD) is the leading cause of death in SSc, affecting around 50 % of the patients. Lung tissue of patients with early-stage SSc-ILD is characterized by a predominant inflammatory response with inconspicuous fibrosis, which may progress to honeycombing fibrosis. Hence, a better understanding of the molecular mechanisms underpinning SSc-ILD pathogenesis is needed to improve treatment options and progression prediction. This transcriptomic study aims to reveal the differential gene expression between control (ctrl) lung tissue and inflammatory, prefibrotic and fibrotic lung tissue to capture progression of early to late phase SSc-ILD. METHODS: Twelve explanted lungs from patients with SSc-ILD were used to analyze gene expression from formalin-fixed paraffin-embedded lung tissues with varying stages of ILD (n = 18) and control lung tissue (n = 6). The SSc-ILD tissues were stratified into three ROIs: inflammatory, prefibrotic, and fibrotic using histological assessments to define a longitudinal simulation of early to late phases of SSc-ILD. The nanoString (nS) nCounter Human Fibrosis Panel was used to profile the transcriptome in the regions of interest. Validation of potential targetswas performed with immunohistochemistry in the same tissues that were used for transcriptome analysis. RESULTS: To validate our simulation model, we performed subgroup analysis that showed an incremental increase in pathway scores related to the severity of fibrosis. Ctrl vs SSc-ILD comparison demonstrated 24 differentially expressed genes, two of which had the most pronounced p-values. Cyclin-dependent kinase inhibitor (cdkn2c) was overexpressed (P = 0.00052) in SSc-ILD compared to ctrl, while expression of Pellino E3 ubiquitin-protein ligase 1 (peli1) showed lower expression (P = 0.0012). Additionally, in all four groups, cdkn2c and peli1 gene expression showed an incremental increase and decrease, respectively. Immunohistochemistry of cdkn2c showed consistent results with the nS analysis. CONCLUSION: More cdkn2c and less peli1 expression were associated with more advanced stages of SSc-ILD on histologic assessment. We report the potential of the cell cycle inhibitor and senescence marker, cdkn2c (p18) to be associated with fibrosis progression.
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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".