Long Non-coding MIR205HG Depletes Hsa-miR-590-3p Leading to Unrestrained Proliferation in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Over 70% of head & neck squamous cell carcinoma (HNSCC) patients carry TP53 oncogenic mutations.Here we studied the role of specific tumor-derived mutant p53 proteins in the aberrant transcription of long non-coding (lnc) MIR205HG gene in head and neck cancer cells.Methods: To understand the role of lncMIR205HG, that we showed to be transcriptionally regulated by mutant p53 in HNSCC, we have employed siRNA and shRNA in CAL27 and FaDu HNSCC cell lines to suppress p53 gene expression in ChIP assays and RT-qPCR.We validated our findings in a cohort of 522 HNSCC patients from The Cancer Genome Atlas Data Portal (TCGA).We further evaluated our results in 63 HNSCC tumor samples collected at our institute, 32 of which were characterized by mutated TP53 (missense mutations) while 31 were characterized by wild-type TP53.Results: Maturation of pre-MIR205HG transcript produces two non-coding RNAs, lncMIR205HG and hsa-miR-205-5p.Down-regulation of lncMIR205HG expression significantly reduced cell proliferation, cell migration and clonogenic activity of head and neck cancer cells.Expression of MIR205HG was significantly increased in HNSCC with mutated TP53 when compared with matched non-tumoral tissues.Furthermore, MIR205HG expression levels were significantly higher in tumoral samples with mutant p53 than in tumoral tissues expressing wild-type p53.Mechanistically, MIR205HG depletes endogenous miR-590-3p leading to increased cyclin B, cdk1, and YAP protein expression.Conclusions: Taken together, these findings identify a transcriptional and post-transcriptional molecular network that includes mutant p53 protein, lncMIR205HG, YAP, and other proliferation-related genes, which are enriched in HNSCC patients with poor prognosis.
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 | 0.000 | 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 | 0.001 |
| 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".