Cancer stem cells enrichment with surface markers CD271 and CD44 in human head and neck squamous cell carcinomas
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) has a poor 5-year survival rate of 50%. One potential reason for treatment failure is the presence of cancer stem cells (CSCs). Several cell markers, particularly CD44, have been used to isolate CSCs. However, isolating a pure population of CSC in HNSCC still remains a challenging task. Recent findings show that normal oral stem cells were isolated using CD271 as a marker. Thus, we investigated the combined use of CD271 and CD44 to isolate an enriched subpopulation of CSCs, followed by their characterization in vitro, in vivo, and in patients' tissue samples. Fluorescent-activated cell sorting was used to isolate CD44+/CD271+ and CD44+/CD271- from two human HNSCC cell lines. Cell growth and self-renewal were measured with MTT and sphere/colony formation assays. Treatment-resistance was tested against chemotherapy (cisplatin and 5-fluorouracil) and ionizing radiation. Self-renewal, resistance, and stemness-related genes expression were measured with qRT-PCR. In vivo tumorigenicity was tested with an orthotopic immunodeficient mouse model of oral cancer. Finally, we examined the co-localization of CD44+/CD271+ in patients' tissue samples. We found that CD271+ cells were a subpopulation of CD44+ cells in human HNSCC cell lines and tissues. CD44+/CD271+ cells exhibited higher cell proliferation, sphere/colony formation, chemo- and radio-resistance, upregulation of CSCs-related genes, and in vivo tumorigenicity when compared to CD44+/CD271- or the parental cell line. These cell markers showed increased expression in patients with the increase of the tumor stage. In conclusion, using both CD44 and CD271 allowed the isolation of CSCs from HNSCC. These enriched CSCs will be more relevant in future treatment and HNSCC progression studies.
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 | 0.001 | 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".