Integrated Genomic and Functional microRNA Analysis Identifies miR-30-5p as a Tumor Suppressor and Potential Therapeutic Nanomedicine in Head and Neck Cancer
Bibliographic record
Abstract
Abstract Purpose: To identify deregulated and inhibitory miRNAs and generate novel mimics for replacement nanomedicine for head and neck squamous cell carcinomas (HNSCC). Experimental Design: We integrated miRNA and mRNA expression, copy number variation, and DNA methylation results from The Cancer Genome Atlas (TCGA), with a functional genome-wide screen. Results: We reveal that the miR-30 family is commonly repressed, and all 5 members sharing these seed sequence similarly inhibit HNSCC proliferation in vitro. We uncover a previously unrecognized inverse relationship with overexpression of a network of important predicted target mRNAs deregulated in HNSCC, that includes key molecules involved in proliferation (EGFR, MET, IGF1R, IRS1, E2F7), differentiation (WNT7B, FZD2), adhesion, and invasion (ITGA6, SERPINE1). Reexpression of the most differentially repressed family member, miR-30a-5p, suppressed this mRNA program, selected signaling proteins and pathways, and inhibited cell proliferation, migration, and invasion in vitro. Furthermore, a novel miR-30a-5p mimic formulated into a targeted nanomedicine significantly inhibited HNSCC xenograft tumor growth and target growth receptors EGFR and MET in vivo. Significantly decreased miR-30a/e family expression was related to DNA promoter hypermethylation and/or copy loss in TCGA data, and clinically with decreased disease-specific survival in a validation dataset. Strikingly, decreased miR-30e-5p distinguished oropharyngeal HNSCC with poor prognosis in TCGA (P = 0.002) and validation (P = 0.007) datasets, identifying a novel candidate biomarker and target for this HNSCC subset. Conclusions: We identify the miR-30 family as an important regulator of signal networks and tumor suppressor in a subset of HNSCC patients, which may benefit from miRNA replacement nanomedicine therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".