The Association of OLFM4 with the Progression and Cisplatin Resistance of Head and Neck Squamous Carcinoma
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent malignant tumor globally with a poor prognosis. Despite continuous advancements in treatment modalities, the molecular mechanisms underlying its progression and chemotherapy resistance remain unclear. In previous studies, cisplatin drug induction was performed on HNSCC patient-derived tumor organoids (HNSCC-PDOs), successfully establishing a cisplatin-resistant organoid model (HNSCC-PDOcisR). This study conducted RNA sequencing on cisplatin-resistant HNSCC-PDOcisR and their parental PDOs. Bioinformatic analysis revealed that the oncoprotein olfactomedin 4 (OLFM4) was significantly upregulated in the drug-resistant model. Combined analysis of TCGA and CPTAC databases demonstrated that OLFM4 expression correlates with poor clinical prognosis in HNSCC. In vitro cellular experiments verified that OLFM4 overexpression significantly enhanced HNSCC cell proliferation, migration, and invasion capabilities (p < 0.05), while OLFM4 knockdown inhibited these phenotypes. Additionally, OLFM4 was found to mediate cisplatin resistance by regulating levels of reactive oxygen species (ROS), malondialdehyde (MDA), and ferrous ions (Fe2⁺), suppressing cisplatin-induced oxidative stress and ferroptosis while maintaining mitochondrial membrane potential. This study confirms that OLFM4 enhances tumor cell proliferation, migration, and resistance to cisplatin-induced cell death, thereby promoting HNSCC progression. These findings suggest OLFM4 may serve as a prognostic biomarker for HNSCC and a potential therapeutic target to reverse cisplatin resistance in HNSCC.
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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 | 0.000 |
| 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".