Hyaluronic acid hydrogel to modulate tumor cell clustering and programmed death ligand 1 signaling in cancer stem cells.
Bibliographic record
Abstract
e14584 Background: The root of malignancy lies in the extracellular matrix (ECM) surrounding the stromal cells. Thus, current therapeutic targets manipulate abnormal tumor microenvironments (TME), which nourish cancer stem cells (CSC) and induce tumor cell clustering (TCC). Notably, despite the first line of clinical therapy with surgery and chemotherapy, uncontrolled self-renewal of CSCs, conversion of non-CSCs to CSCs, and TCC trigger cancer recurrence. The transmembrane glycoprotein CD44 is thought to be a potential master regulator in cancer recurrence as it interacts with hyaluronic acid (HA) in TME, expresses aberrantly on CSCs, induces TCC by homophilic interactions like Plakoglobin despite less understanding on their crosstalk, and promotes expression of pro-tumorigenic factor programmed death ligand 1 (PD-L1). Notably, CD44 is expressed in multiple isoforms, and splice isoform switching of CD44 dictates the properties of CSC, which still needs to be clarified. Hence, we aimed to characterize CSCs based on CD44 isoform expression, investigate the crosstalk between CD44 and Plakoglobin, and then design a novel injectable HA hydrogel that can disguise TME and suppress PD-L1 to combat cancer recurrence. Methods: To characterize CSCs, we prepared different scaffolds of HA of varied molecular weights and other ECM proteins, cultured cancer cells (MDA-MB-231, HeLa), and determined the expression of CD44 isoforms. We sorted CD44+/CD24- expressing CSC by flow cytometry. We implemented immunoprecipitation to detect the interaction between CD44 and Plakoglobin. We created a novel HA hydrogel composed of low and very high molecular weight HA, thiolated gelatin, and other components crosslinked with PEGDA. Using a torsional rheometer, we measured matrix stiffness. We assessed the cell proliferation efficiency of CSC by a colony-forming assay. Results: The study disclosed that CD44 isoforms expressed differently based on ECM properties. Matrix stiffness dictated CD44 isoform expression. We observed that CD44 interacted with Plakoglobin. CD44 ablation upregulated Plakoglobin significantly. Tumor suppressor p53 played a significant role in CD44-Plakoglobin crosstalk. We observed that the injectable hydrogel modulated CD44-Plakoglobin signaling and inhibited TCC, likely promoting anoikis. The hydrogel affected CD44 splicing and modified the activity of the CD44 intracellular domain, which interacts with PD-L1 and thus suppressed PD-L1 expression significantly in CSC. Conclusions: Currently, antibodies targeting PD-1/PD-L1 signaling have been clinically approved for various cancers. However, this regimen of treatment is expensive. We report a novel injectable hydrogel that can disguise TME, modulate CSCs and PD-L1, and may advance an alternative cost-effective avenue for cancer immunotherapy.
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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".