CRCs-CAFs crosstalk-targeted nano-delivery system reprograms tumor microenvironment for oxaliplatin resistance reversing and liver metastasis inhibition in colorectal cancer
Bibliographic record
Abstract
The five-year survival rate of patients with colorectal cancer (CRC) liver metastasis is less than 30 %, and chemotherapy resistance and metastatic microenvironment remodeling are the current treatment bottlenecks. Cancer-associated fibroblasts (CAFs) in the tumor microenvironment (TME) form a "CRCs-CAFs crosstalk" with colorectal cancer cells (CRCs) by secreting dense extracellular matrix (ECM), free fatty acids (FFA), and pro-metastatic factors, driving a vicious cycle of drug resistance and metastasis. During liver metastasis, hepatic stellate cells (HSCs)-derived CAFs (HSC-CAFs) promote tumor metastasis by remodeling the pre-metastatic microenvironment. Based on clinical sample RNA sequencing and mouse single-cell sequencing to reveal ECM signal enrichment and CAFs activation characteristics, we innovatively constructed a nano-delivery system using hyaluronic acid-modified MIL-100 nanoparticles (OEMH NPs) co-loaded with oxaliplatin (OXA) and epigallocatechin gallate (EGCG). This system can target the CRCs-CAFs crosstalk through CD44 receptor: on the one hand, OEMH NPs can inhibit CAFs activation and reduce ECM deposition, improve drug penetration and down-regulate FFA metabolic reprogramming, reverse OXA resistance; on the other hand, OEMH NPs can block the transformation of HSCs to CAFs, down-regulate pro-metastatic factors such as VEGF/IL-11/ANG, induce vascular normalization, and reprogram the pre-metastatic microenvironment. This strategy can simultaneously achieve primary lesion drug sensitization and liver metastasis inhibition, providing a new paradigm for the treatment of advanced colorectal cancer to break through the traditional treatment dilemma through dual reprogramming of metabolism and microenvironment, and has significant clinical translation potential.
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) | 0.001 | 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".