Adipocytes promoted anoikis resistance of head and neck cancer cells via overexpressed ITGA5
Bibliographic record
Abstract
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) was the sixth most common cancer worldwide. Given the evidence that adipocytes are related to the progression of various cancers, this study explored the internal connection and mechanism between HNSCC and adipocytes. METHODS: Quantitative real-time PCR (qRT-PCR) was used to examine the transfection efficiency of integrin subunit α5 (ITGA5) overexpression plasmid and small interfering RNA of ITGA5 (si-ITGA5). Colony formation assay was employed to detect the colony formation rate of FaDu and SAS cells, while flow cytometry was used to measure cell apoptosis rate. The protein levels of cleaved-caspase-3 (apoptosis marker) and cell adhesion markers (ITGA5, integrin subunit β1 (ITGB1) and cluster of differentiation 44 (CD44)) were analyzed by western blotting. After HNSCC cells and adipocytes were co-cultured, the last three experiments above-mentioned were repeated and Calcein AM/EthD-1 double stain assay was performed to examine anoikis. RESULTS: Overexpressed ITGA5 enhanced colony formation and inhibited anoikis in HNSCC cells (p < 0 p < .001), while reducing the expression of the apoptosis-related protein and increasing those of adherence-related proteins (p < 0.05). However, si-ITGA5 had the opposite effect. After co-culture of HNSCC cells with adipocytes, cell colony formation was increased, anoikis was inhibited and the expressions of ITGA5 and cell adhesion markers were upregulated (p < 0.05). The effects of HNSCC cells co-cultured with adipocytes on colony formation, anoikis and cell adhesion markers were reversed by ITGA5 silencing (p < 0.01). CONCLUSION: Adipocytes promote anoikis resistance of head and neck cancer cells via overexpressed ITGA5.
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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 | 0.000 | 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 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".