Targeting the Ubiquitin Signaling Cascade in Tumor Microenvironment for Cancer Therapy
Bibliographic record
Abstract
Tumor microenvironments are composed of a myriad of elements, both cellular (immune cells, cancer-associated fibroblasts, mesenchymal stem cells, etc.) and non-cellular (extracellular matrix, cytokines, growth factors, etc.), which collectively provide a permissive environment enabling tumor progression. In this review, we focused on the regulation of tumor microenvironment through ubiquitination. Ubiquitination is a reversible protein post-translational modification that regulates various key biological processes, whereby ubiquitin is attached to substrates through a catalytic cascade coordinated by multiple enzymes, including E1 ubiquitin-activating enzymes, E2 ubiquitin-conjugating enzymes and E3 ubiquitin ligases. In contrast, ubiquitin can be removed by deubiquitinases in the process of deubiquitination. Here, we discuss the roles of E3 ligases and deubiquitinases as modulators of both cellular and non-cellular components in tumor microenvironment, providing potential therapeutic targets for cancer therapy. Finally, we introduced several emerging technologies that can be utilized to develop effective therapeutic agents for targeting tumor microenvironment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".