Uncovering dendritic cell specific biomarkers for diagnosis and prognosis of cardiomyopathy using single cell RNA sequencing and comprehensive bioinformatics analysis
Bibliographic record
Abstract
Cardiomyopathy is a type of cardiovascular disorder that is a primary cause of death globally, killing millions of people each year. Cardiomyopathy detection and early diagnosis are crucial in reducing negative health effects. Thus, this study aims to use single cell RNA sequencing, and bioinformatics analysis to uncover dendritic cell-specific biomarkers, gene ontology, pathways, regulatory interaction networks, and protein-chemical compounds related to the molecular mechanism of cardiomyopathy progression. Two RNAseq datasets GSE65446 and GSE155495 also were evaluated to identify significant biomarkers in cardiomyopathy, and 123 mutual DEGs appeared between scRNAseq and RNAseq datasets. In addition, the DAVID online platform and FunRich software were utilized to detect cell communication in innate immune responses, type 1 IFN, antigen processing and presentation, allograft rejection and viral infection significant gene ontology and metabolic pathways in cardiomyopathy. The protein-protein interaction (PPI) network revealed five key hub proteins (ITGAX, IRF7, MX1, HLA-B, and IRF1). Following that, several transcription factors (GATA2, FOXC1, SREBF1, STAT3, and NFKB1) as well as microRNA (hsa-mir-26a-5p, hsa-mir-129-2-3p, etc.) were predicted. Prospective chemical substances such as tretinoin, valproic acid, and arsenic trioxide have been predicted to be linked to cardiomyopathy treatment. The acceptable value of receiver operating characteristic (ROC) curve analysis revealed that biomarkers play critical roles in cardiomyopathy. This study identifies molecular indicators at the RNA and protein levels that may be useful in improving understanding of molecular causes, early diagnosis, and devising favorable cardiomyopathy treatment. More research will be needed to validate our predicted findings as future clinical biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".