Plasma proteomic profiling identified prognostic indicators with therapeutic potential for colorectal cancer
Bibliographic record
Abstract
Plasma proteins have been reported as predictors and potential targets for reducing colorectal cancer (CRC) risk. However, their potential roles in CRC prognosis remain unexplored. We measured plasma levels of 367 neuro-related proteins in CRC patients from the West China Hospital (WCH) cohort (N = 150, median follow-up = 46.72 months) via proximity extension assay. The least absolute shrinkage and selection operator penalized Cox regression identified five overall survival (OS)-and eleven disease-free survival-associated proteins, and the multiprotein signature for OS prediction was then validated in the UK Biobank (UKB) cohort (N = 1133). To overcome possible effects from confounders, we then employed Mendelian randomization analysis leveraging protein quantitative trait loci to investigate associations between genetically determined protein concentration and OS and cancer-specific survival of CRC in the UKB. We found that multiprotein signature developed in the WCH cohort (c-index = 0.784, 95% CI = 0.713-0.855) showed significant discriminative ability in the external UKB cohort (c-index = 0.616, 95% CI = 0.559-0.673). A significant association between genetically determined PD-L1 and OS (P = 0.043, HR = 1.53, 95% CI = 1.01-2.29) was observed, although we did not find strong evidence for colocalization. Additionally, single-cell and spatial transcriptome analyses illustrated PD-L1 expression localized predominantly to epithelial cells and immune cells (especially myeloid cells) in CRC tissue. The potential interactions of identified proteins were evaluated in the STRING database. Druggability evaluation also supported PD-L1 as a potential therapeutic target for CRC. Taken together, this study established multiprotein signatures for CRC prognosis and identified plasma PD-L1 as a possible biomarker and therapeutic target.
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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 | 0.001 |
| 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) | 0.001 | 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".