A Call for Gene Expression Analysis in Whole Blood of Patients With Rheumatoid Arthritis (RA) as a Biomarker for RA-Associated Interstitial Lung Disease
Bibliographic record
Abstract
Objective Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is one of the most common and prognostic organ manifestations of RA. Therefore, to allow effective treatment, it is of crucial importance to diagnose RA-ILD at the earliest possible stage. So far, the gold standard of early detection has been high-resolution computed tomography (HRCT) of the lungs. This procedure involves considerable radiation exposure for the patient and is therefore unsuitable as a routine screening measure for ethical reasons. Here, we propose the analysis of characteristic gene expression patterns as a biomarker to aid in the early detection and initiation of appropriate, possibly antifibrotic, therapy. Methods To investigate unique molecular patterns of RA-ILD, whole blood samples were taken from 12 female patients with RA-ILD (n = 7) or RA (n = 5). The RNA was extracted, sequenced by RNA-Seq, and analyzed for characteristic differences in the gene expression patterns between patients with RA-ILD and those with RA without ILD. Results The differential gene expression analysis revealed 9 significantly upregulated genes in RA-ILD compared to RA without ILD: arginase 1 ( ARG1 ), thymidylate synthetase ( TYMS ), sortilin 1 ( SORT1 ), marker of proliferation Ki-67 ( MKI67 ), olfactomedin 4 ( OLFM4), baculoviral inhibitor of apoptosis repeat containing 5 ( BIRC5 ), membrane spanning 4-domains A4A ( MS4A4A ), C-type lectin domain family 12 member A ( CLEC12A ), and the long intergenic nonprotein coding RNA ( LINC02967 ). Conclusion All gene products of these genes (except for LINC02967 ) are known from the literature to be involved in the pathogenesis of fibrosis. Further, for some, a contribution to the development of pulmonary fibrosis has even been demonstrated in experimental studies. Therefore, the results presented here provide an encouraging perspective for using specific gene expression patterns as biomarkers for the early detection and differential diagnosis of RA-ILD as a routine screening test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".