Deubiquitinating enzyme JOSD2 modulates cGAS to facilitate immune evasion in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) remains a formidable clinical challenge, many patients exhibit limited responses to conventional chemotherapy and targeted therapies. Although immunotherapy has demonstrated potential, its efficacy is largely restricted to a subset of patients with high microsatellite instability (MSI-H), highlighting the critical need to identify key molecular drivers of immune evasion in CRC. Through comprehensive bioinformatic analysis, we identified the deubiquitinating enzyme JOSD2 as a key player in CRC progression, with elevated expression correlating with poor prognosis in MSI-H patients (HR = 4.79, 95% CI: 3.6–7.96) and dysregulation of multiple immune-related pathways. Mechanistically, we discovered that JOSD2 suppresses cGAS enzymatic activity by removing K27-linked ubiquitination, thereby promoting M2 polarization of macrophages, a process critical for immunosuppression in the tumor microenvironment. Furthermore, using the JOSD2 catalytic inhibitor HY041004, we demonstrated both in vitro and in vivo that the inhibition of JOSD2 activated the cGAS-STING signaling pathway, leading to robust anti-tumor effects in CRC. These findings not only uncover a novel immunomodulatory mechanism in colorectal cancer but also provide a therapeutic rationale for the development of JOSD2-targeted anticancer strategies.
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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".