Prognostic Role of Immune Checkpoint Regulators in Cholangiocarcinoma: A Pilot Study
Bibliographic record
Abstract
Cholangiocarcinoma (CCA) is a hepatobiliary malignancy associated with steadily increasing incidence and poor prognosis. Ongoing clinical trials are assessing the effectiveness and safety of a few immune checkpoint inhibitors (ICIs) in CCA patients. However, these ICI treatments as monotherapies may be effective for a proportion of patients with CCA. The prevalence and distribution of other immune checkpoints (ICs) in CCA remain unclear. In this pilot study, we screened databases of CCA patients for the expression of 19 ICs and assessed the prognostic significance of these ICs in CCA patients. Notably, expression of immune modulator IDO1 and PD-L1 were linked with poor overall survival, while FASLG and NT5E were related to both worse overall survival and progression-free survival. We also identified immune modulators IDO1, FASLG, CD80, HAVCR2, NT5E, CTLA-4, LGALS9, VTCN1 and TNFRSF14 that synergized with PD-L1 and correlated with worse patient outcomes. In vitro studies revealed that the expression of ICs was closely linked with aggressive CCA subpopulations, such as cancer stem cells and cells undergoing TGF-β and TNF-α-mediated epithelial-to-mesenchymal transition. These findings suggest that the aforementioned IC molecules may serve as potential prognostic biomarkers and drug targets in CCA patients, leading to lasting and durable treatment outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".