MicroRNA-3148 acts as molecular switch promoting malignant transformation and adipocytic differentiation of immortalized human bone marrow stromal cells via direct targeting of the SMAD2/TGFβ pathway
Bibliographic record
Abstract
MicroRNAs (miRs/miRNAs) play a key role in posttranscriptional regulation of gene expression and are implicated in a number of physiological and pathological conditions, including cellular malignant transformation. In the current study, we investigated the role of miR-3148 in regulating human stromal (mesenchymal) stem cell (hMSC) differentiation and transformation. Stable expression of miR-3148 in telomerized hMSC (hMSC-miR-3148) led to significant increase in in vitro adipocytic differentiation and suppression of osteoblastic differentiation. Concordantly, global gene expression profiling revealed significant enrichment in cholesterol biosynthesis pathway, and pathways related to enhanced cell movement and survival, whereas processes related to bone and connective tissue developments, cell death, apoptosis, and necrosis were downregulated. Global proteomic analysis using 2D-DIGE followed by mass spectrometry (MS) revealed significant changes in protein expression in hMSC-miR-3148 and enrichment in protein networks associated with carcinogenesis. Functional studies revealed that hMSC-miR-3148 exhibited enhanced in vitro cell proliferation, colony formation, migration, invasion, sphere formation, doxorubicin resistance, and increased active number of cells in S and G2/M cell cycle phases and formed sarcoma-like tumors with adipocyte infiltration when implanted into immunocompromised mice. SMAD2 was identified as bone fide gene target for miR-3148 using qRT-PCR, Western blotting, and UTR-based reporter assay. In agreement with our data, SMAD2 expression was downregulated in 47% of patients with soft tissue sarcoma. Bioinformatics analysis revealed that elevated miR-3148 expression correlates with poor prognosis in several human cancer types, including sarcoma. Our study identified miR-3148 as factor regulating hMSC differentiation and is involved in promoting malignant transformation of telomerized hMSC.
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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".