Identification of key miRNA signature and pathways involved in multiple myeloma by integrated bioinformatics analysis
Bibliographic record
Abstract
Multiple myeloma (MM) is one of the most common types of hematologic malignancy for which the underlying molecular mechanisms remain largely unclear. Dysregulated miRNA expression has been shown to be involved in MM tumorigenesis, progression and drug response. Therefore, a comprehensive analysis based on miRNA-level integrated strategy was performed. This study aimed to elucidate key miRNA signatures and pathways in MM by integrated bioinformatics analysis. Expression profiles GSE24371, GSE49261 and GSE54156 were obtained from the Gene Expression Omnibus database, and differentially expressed miRNAs (DEMirs) with p < 0.05 were identified. The target genes of these DEMirs were obtained from ENCORI database, and functional enrichment, subpathway enrichment and protein–protein interaction network construction were performed. The key target genes were identified by random walk algorithm and survival verification was performed. and discussion: First, six up-regulated and four down-regulated DEMirs shared between any two GSE data sets were identified. Second, target genes (DEMirTGs) by up-regulated and down-regulated DEMirs were obtained. Functional and subpathway enrichment analysis showed that these up-regulated DEMirs are consistently involved in the Wnt signaling pathway. Moreover, enrichment of the down-regulated DEMirs is mainly in the MAPK signaling pathway. Finally, a protein–protein interaction sub-network for these DEMirTGs was constructed, the correlations between the two key genes were identified and survival in MM was evaluated using multiple independent data sets. We identified miRNA signatures and key target genes that were closely related to MM biology, and these genes might serve as potential therapeutic targets for MM patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".