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Record W4412008298 · doi:10.1080/14796694.2025.2521871

The significance of mitochondrial DNA changes during the onset and progression of head and neck squamous cell carcinoma

2025· review· en· W4412008298 on OpenAlexaff
Amnani Aminuddin, Poh Kuan Wong, Siti Fathiah Masre, Pei Yuen Ng, Muhammad Asyaari Zakaria, Eng Wee Chua

Bibliographic record

VenueFuture Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsMedicineHead and neck squamous-cell carcinomaMitochondrial DNAHead and neckBasal cellOncologyHead and neck cancerCancer researchInternal medicinePathologyCancerBiologyGeneticsSurgeryGene

Abstract

fetched live from OpenAlex

Mitochondrial DNA (mtDNA) is integral to cellular function. Alterations in mtDNA can lead to significant disruptions in mitochondrial function, including cellular energy production, metabolism, and apoptosis regulation. Mitochondrial dysfunction has emerged as a crucial factor in the progression of cancer, including head and neck squamous cell carcinoma (HNSCC). Based on a literature search conducted from August 2024 to March 2025 on PubMed and Google Scholar, our review summarizes the relevance of mtDNA alterations in HNSCC development. HNSCC has been found to cause significant variations in mtDNA, including mutations, copy number variations (or known as mtDNA content) and large-scale deletions. Notably, these alterations varied by disease stage, different key aspects of mitochondrial function, such as cellular growth, senescence, metabolism and apoptosis, and cell-specific genetic context. We further suggest that drugs that modulate mitochondrial pathways, mitochondrial transplantation, and gene-editing technologies, are future treatment strategies for HNSCC. Lastly, we discuss the potential of co-roles of nuclear DNA mutations with mtDNA alterations in HNSCC to provide a holistic view of HNSCC pathogenesis and the possibility for combined therapeutic strategies in HNSCC.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.347
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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