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Artificial Intelligence for Network-Based Oncomarker Discovery and Cancer Prediction

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 10 May 2026 | Viewed by 1773

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Special Issue Editor

Dr. Oleg Blyuss

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Guest Editor

Wolfson Institute of Population Health, Queen Mary University of London, London, UK
Interests: machine learning; medical statistics; artificial intelligence; longitudinal data; early detection; active surveillance

Special Issue Information

Dear Colleagues,

The discovery of reliable biomarkers, or oncomarkers, remains one of the central challenges in oncology, with profound implications for early detection, prognosis, and personalised treatment strategies. While traditional biomarker studies have provided important insights, they often focus on individual molecules rather than the complex biological networks driving cancer initiation and progression.

Recent advances in artificial intelligence (AI) and machine learning (ML) are enabling a paradigm shift toward network-based biomarker discovery. By integrating genomics, transcriptomics, proteomics, epigenomics, and metabolomics, AI methods can uncover novel molecular interactions, dysregulated pathways, and predictive signatures that may remain hidden in conventional analyses. In particular, graph neural networks (GNNs), network embeddings, and systems biology-inspired approaches hold promise for identifying prognostic and predictive oncomarkers, improving risk stratification, and informing clinical decision support in oncology.

The aim of this Special Issue is to bring together cutting-edge research and comprehensive reviews on the applications of AI for network-based biomarker discovery and cancer prediction. We welcome contributions that explore computational innovations and translational insights with clinical relevance.

Dr. Oleg Blyuss
Guest Editor

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Keywords

artificial intelligence (AI)
network-based biomarkers
graph neural networks (GNNs)
multi-omic integration
cancer prediction
oncomarker discovery
prognostic and predictive biomarkers

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Published Papers (3 papers)

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Research

13 pages, 557 KB

Open AccessArticle

Synolitic Graph Neural Networks of High-Dimensional Proteomic Data Enhance Early Detection of Ovarian Cancer

by Alexey Zaikin, Ivan Sviridov, Janna G. Oganezova, Usha Menon, Aleksandra Gentry-Maharaj, John F. Timms and Oleg Blyuss

Cancers 2025, 17(24), 3972; https://doi.org/10.3390/cancers17243972 - 12 Dec 2025

Viewed by 136

Abstract

Background: Ovarian cancer is characterized by high mortality rates, primarily due to diagnosis at late stages. Current biomarkers, such as CA125, have demonstrated limited efficacy for early detection. While high-dimensional proteomics offers a more comprehensive view of systemic biology, the analysis of such data, where the number of features far exceeds the number of samples, presents a significant computational challenge. Methods: This study utilized a nested case–control cohort of longitudinal pre-diagnostic serum samples from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) profiled for eight candidate ovarian cancer biomarkers (CA125, HE4, PEBP4, CHI3L1, FSTL1, AGR2, SLPI, DNAH17) and 92 additional cancer-associated proteins from the Olink Oncology II panel. We employed a Synolitic Graph Neural Network framework that transforms high-dimensional multi-protein data into sample-specific, interconnected graphs using a synolitic network approach. These graphs, which encode the relational patterns between proteins, were then used to train Graph Neural Network (GNN) models for classification. Performance of the network approach was evaluated together with conventional machine learning approaches via 5-fold cross-validation on samples collected within one year of diagnosis and a separate holdout set of samples collected one to two years prior to diagnosis. Results: In samples collected within one year of ovarian cancer diagnosis, conventional machine learning models—including XGBoost, random forests, and logistic regression—achieved the highest discriminative performance, with XGBoost reaching an ROC-AUC of 92%. Graph Convolutional Networks (GCNs) achieved moderate performance in this interval (ROC-AUC ~71%), with balanced sensitivity and specificity comparable to mid-performing conventional models. In the 1–2 year early-detection window, conventional model performance declined sharply (XGBoost ROC-AUC 46%), whereas the GCN maintained robust discriminative ability (ROC-AUC ~74%) with relatively balanced sensitivity and specificity. These findings indicate that while conventional approaches excel at detecting late pre-diagnostic signals, GNNs are more stable and effective at capturing subtle early molecular changes. Conclusions: The synolitic GNN framework demonstrates robust performance in early pre-diagnostic detection of ovarian cancer, maintaining accuracy where conventional methods decline. These results highlight the potential of network-informed machine learning to identify subtle proteomic patterns and pathway-level dysregulation prior to clinical diagnosis. This proof-of-concept study supports further development of GNN approaches for early ovarian cancer detection and warrants validation in larger, independent cohorts. Full article

(This article belongs to the Special Issue Artificial Intelligence for Network-Based Oncomarker Discovery and Cancer Prediction)

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17 pages, 1466 KB

Open AccessArticle

Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence

by Olga Vershinina, Nikita Sushentsev, Alexey Zaikin, Oleg Blyuss, Tristan Barrett and Mikhail Ivanchenko

Cancers 2025, 17(22), 3598; https://doi.org/10.3390/cancers17223598 - 7 Nov 2025

Viewed by 793

Abstract

Background: Approximately half of prostate cancer (PCa) patients present with low- or intermediate-risk disease eligible for active surveillance (AS). However, a substantial proportion of individuals experience pathological progression during follow-up. In this study, we developed predictive models for histopathological PCa progression in patients on AS. Methods: The dataset comprised patients with biopsy-confirmed PCa and a minimum follow-up of two years. All patients underwent regular surveillance, including prostate-specific antigen (PSA) measurements and MRI examinations. Each patient had three to six consecutive MRI scans available for analysis. Histopathological progression was defined as an upgrade to a higher grade group on repeat targeted biopsy. Predictive modeling integrated radiomic and clinical variables using machine learning (ML). SHapley Additive exPlanations (SHAP) was used for feature interpretation. Results: Three models were obtained: (1) a baseline model utilizing radiomic features from initial MRI scans combined with baseline PSA density (PSAd) (AUC = 0.793, sensitivity = 0.690, specificity = 0.830); (2) a delta model incorporating feature changes between latest and baseline available MRI scans with final PSAd (AUC = 0.913, sensitivity = 0.793, specificity = 0.936); and (3) a time series model analyzing the complete series of radiomic features and PSAd (AUC = 0.917, sensitivity = 0.828, specificity = 0.894). Conclusions: Our predictive models demonstrated strong performance in distinguishing progressors from non-progressors, suggesting that radiomic analysis combined with ML has significant potential to enhance PCa management. This approach could enable more personalized treatment strategies and improve clinical decision-making for patients undergoing AS. Full article

(This article belongs to the Special Issue Artificial Intelligence for Network-Based Oncomarker Discovery and Cancer Prediction)

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23 pages, 1891 KB

Open AccessArticle

Subtype Characterization of Ovarian Cancer Cell Lines Using Machine Learning and Network Analysis: A Pilot Study

by Rama Krishna Thelagathoti, Dinesh S. Chandel, Chao Jiang, Wesley A. Tom, Gary Krzyzanowski, Appolinaire Olou and M. Rohan Fernando

Cancers 2025, 17(21), 3509; https://doi.org/10.3390/cancers17213509 - 31 Oct 2025

Cited by 1 | Viewed by 648

Abstract

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article

(This article belongs to the Special Issue Artificial Intelligence for Network-Based Oncomarker Discovery and Cancer Prediction)

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