Exploration of plasma proteomic landscapes using Olink Reveal technology to identify biomarkers for early-stage/localized gastric cancer.
- 1Medical Experiment Center, General Hospital of Ningxia Medical University, 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China.
- 2First Clinical Medical College, Ningxia Medical University, 1180 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China.
- 3Medical Experiment Center, General Hospital of Ningxia Medical University, 804 Shengli South Street, Yinchuan, 750004, Ningxia Hui Autonomous Region, China. [email protected].
Abstract
Purpose
Blood-based biomarkers for the early detection of gastric cancer remain limited. We conducted a systematic analysis of pre-treatment plasma proteomics profiled using the Olink Reveal panel and developed diagnostic prediction models within a leakage-controlled, outer out-of-fold (OOF) internal validation framework based on nested cross-validation. In parallel, pathway-level activity was summarized using a predefined exploratory suppressive burden index to support biological interpretation.Methods
We analysed plasma samples from 90 participants (30 healthy controls, 30 early-stage/localized gastric cancer, 30 advanced gastric cancer) using the Olink Reveal platform. Group E comprises stage T1 and T2 tumours; consequently, this group is operationally defined as early-stage/localized gastric cancer, rather than classic early gastric cancer in the strict sense. The advanced group comprised T3/T4 disease. Differential abundance was tested with age- and sex-adjusted limma models. Predictive models were developed using nested cross-validation, with preprocessing and tuning performed within training folds and performance evaluated through outer-loop OOF predicted probabilities. The suppressive burden index used predefined Reactome terms and explicit orientation signs.Results
Age- and sex-adjusted analyses identified several proteins differentially abundant between early gastric cancer and healthy controls. Reactome term-set analyses indicated immune-related pathway variation across disease states, although these pathway-level findings should be interpreted as exploratory. OOF internal validation showed that proteomic models improved discrimination over age/sex covariates for the main early-detection task, while the relative performance of protein-only and full models varied across resampling analyses. Calibration slopes and decision-curve results were reported from OOF predictions and interpreted as internal-validation evidence only.Conclusion
Plasma proteomics captures systemic immune alterations associated with early-stage/localized gastric cancer and may support minimally invasive risk modeling. Given the modest single-center cohort and absence of external validation, the diagnostic models and suppressive burden index should be considered exploratory and require independent validation before clinical use.
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BibTeX
@article{ding2026exploration,
title = {{Exploration of plasma proteomic landscapes using Olink Reveal technology to identify biomarkers for early-stage/localized gastric cancer.}},
author = {Shao Rui Ding and Xiang Li and Xiang Dong and Ping Chen},
journal = {BMC cancer},
year = {2026},
month = jul,
doi = {10.1186/s12885-026-16490-5},
url = {https://europepmc.org/article/MED/42928152},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Exploration of plasma proteomic landscapes using Olink Reveal technology to identify biomarkers for early-stage/localized gastric cancer.
AU - Shao Rui Ding
AU - Xiang Li
AU - Xiang Dong
AU - Ping Chen
PY - 2026
DA - 2026/07/29
JO - BMC cancer
DO - 10.1186/s12885-026-16490-5
UR - https://europepmc.org/article/MED/42928152
AB - Purpose Blood-based biomarkers for the early detection of gastric cancer remain limited. We conducted a systematic analysis of pre-treatment plasma proteomics profiled using the Olink Reveal panel and developed diagnostic prediction models within a leakage-controlled, outer out-of-fold (OOF) internal validation framework based on nested cross-validation. In parallel, pathway-level activity was summarized using a predefined exploratory suppressive burden index to support biological interpretation.Methods We analysed plasma samples from 90 participants (30 healthy controls, 30 early-stage/localized gastric cancer, 30 advanced gastric cancer) using the Olink Reveal platform. Group E comprises stage T1 and T2 tumours; consequently, this group is operationally defined as early-stage/localized gastric cancer, rather than classic early gastric cancer in the strict sense. The advanced group comprised T3/T4 disease. Differential abundance was tested with age- and sex-adjusted limma models. Predictive models were developed using nested cross-validation, with preprocessing and tuning performed within training folds and performance evaluated through outer-loop OOF predicted probabilities. The suppressive burden index used predefined Reactome terms and explicit orientation signs.Results Age- and sex-adjusted analyses identified several proteins differentially abundant between early gastric cancer and healthy controls. Reactome term-set analyses indicated immune-related pathway variation across disease states, although these pathway-level findings should be interpreted as exploratory. OOF internal validation showed that proteomic models improved discrimination over age/sex covariates for the main early-detection task, while the relative performance of protein-only and full models varied across resampling analyses. Calibration slopes and decision-curve results were reported from OOF predictions and interpreted as internal-validation evidence only.Conclusion Plasma proteomics captures systemic immune alterations associated with early-stage/localized gastric cancer and may support minimally invasive risk modeling. Given the modest single-center cohort and absence of external validation, the diagnostic models and suppressive burden index should be considered exploratory and require independent validation before clinical use.
SN - 1471-2407
ER -