Mutual Information–based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG

Sep 22, 2026

Mutual information (MI)-based approaches are increasingly used in cancer genomics, yet genome-wide prognostic biomarker discovery using MI remains relatively underexplored. This review summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG), including clinical endpoint discretization, genome-wide MI screening, candidate ranking, and downstream survival-analysis validation. Prior MI-POG studies identified SLC20A1 as a prognostic biomarker in hormone receptor-positive breast cancer, with unfavorable survival associations and independent validation in METABRIC. The framework integrates molecular–clinical dependencies into an information-theoretic model via fixed-time outcome discretization. Cross-dataset applications indicate potential use across distinct tumor types, while robustness and generalizability require further validation.

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doi: 10.21873/cgp.20606 Review Mutual Information–based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG KAZUNORI AKIMOTO1,2, SHOMATAMORI1,2 and KEIKO SATO2,3 1 Department of Medicinal and Life Sciences, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Tokyo, Japan; 2 Research Division of Medical Data Science, Research Institute for Science and Technology, Tokyo University of Science, Noda, Japan; 3 Department of Information Sciences, Faculty of Science and Technology, Tokyo University of Science, Noda, Japan Abstract Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) asa prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomesand was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular–clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular–clinical dependencies using mutual information. Representative applications from previously published continued ✉ Kazunori Akimoto, Department of Medicinal and Life Sciences, Faculty of Pharmaceutical Sciences, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan. Tel: +81 358761567, [e-mail:](e-mail: akimoto@rs.tus.ac.jp)[ akimoto@rs.tus.ac.jp](e-mail: akimoto@rs.tus.ac.jp) Received March 11, 2026 | Revised June 11, 2026 | Accepted June 18, 2026 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. ©2026 The Author(s) . Published by the International Institute of Anticancer Research. studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required. Keywords: Mutual information, MI-POG, prognostic biomarker, cancer genomics, precision oncology. Introduction Cancer genomics has reshaped modern oncology by enabling molecular classification, prognostic stratification, and therapy selection based on tumor-specific genomic characteristics rather than histopathological features alone (1-3). Large-scale initiatives such as The Cancer Genome Atlas (TCGA) and other comprehensive sequencing efforts have delineated the mutational and transcriptomic landscapes of diverse malignancies (4). As high-throughput technologies generate increasingly complex multi-omics datasets, the central challenge in precision oncology has shifted from data acquisition to extracting clinically meaningful information from high-dimensional molecular data (4, 5). Id

FAQs

What problem does the MI-POG framework address in cancer genomics?

It targets genome-wide discovery of prognostic biomarkers using mutual information, aiming to extract clinically meaningful molecular–clinical dependencies from high-dimensional omics data.

What are the main steps of the MI-POG workflow described in the review?

The framework includes clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches.

Which example biomarker is reported from previous MI-POG applications?

SLC20A1 is reported as a prognostic biomarker in hormone receptor-positive breast cancer, linked to unfavorable survival outcomes and independently validated in the METABRIC cohort.

Sep 22, 2026

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