Transcriptome Profiling of Mouse Embryonic Fibroblast Spontaneous Immortalization: A Comparative Analysis
Bibliographic record
Abstract
Cell immortalization, a hallmark of cancer development, is a process that cells can undergo on their path to carcinogenesis. Spontaneously immortalized mouse embryonic fibroblasts (MEFs) have been used for decades; however, changes in the global transcriptome during this process have been poorly described. In our research, we characterized the poly-A RNA transcriptome changes after spontaneous immortalization. To this end, differentially expressed genes (DEGs) were screened using DESeq2 and characterized by gene ontology enrichment analysis and protein–protein interaction (PPI) network analysis to identify the potential hub genes. In our study, we identified changes in the expression of genes involved in proliferation regulation, cell adhesion, immune response and transcriptional regulation in immortalized MEFs. In addition, we performed a comparative analysis with previously reported MEF immortalization data, where we propose a predicted gene regulatory network model in immortalized MEFs based on the altered expression of Mapk11, Cdh1, Chl1, Zic1, Hoxd10 and the novel hub genes Il6 and Itgb2.
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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 | 0.001 | 0.001 |
| 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".