Graphene Oxide‐Based Gene Modulation in Preferential Elimination of Lung Cancer Cells in a 3D Tumor Microenvironment Model
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer‐related mortality worldwide, owing to its aggressive nature, late‐stage diagnosis, and resistance to conventional therapies. Gene therapy offers a promising alternative by modulating specific genetic pathways to target cancer cells while sparing healthy ones. This study investigates the potential of chemically functionalized nanoscale graphene oxide (GO) as carriers for delivering therapeutic genes in a 3D tumor microenvironment (TME) model, incorporating lung cancer cells, human lung fibroblasts, and macrophages in a Matrigel‐collagen matrix to mimic the structural properties and immune functions. These therapeutic genes, including small interfering RNAs and plasmid DNAs, regulate immune evasion markers (CD47 and CD24) and apoptosis‐inducing proteins (ANT1). GO nanocarriers demonstrate preferential uptake in cancer cells, achieving transfection and gene modulation within the TME model. The individual delivery of genes downregulates cancer markers and induces ANT1 expression, resulting in lung cancer cell elimination. Co‐delivery of CD47_siRNA and ANT1_pDNA produces synergistic efficacy, enhancing cancer cell elimination. These findings highlight the potential of GO‐based gene therapies as a targeted and effective approach for lung cancer treatment, setting the stage for in vivo validation and clinical translation.
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 | 0.000 | 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) | 0.000 | 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".