The Application of Bayesian Methods in Cancer Prognosis and Prediction

• Published in: Cancer genomics & proteomics Vol. 19; no. 1; pp. 1 - 11
• Main Authors: Chu, Jiadong, Sun, N A, Hu, Wei, Chen, Xuanli, Yi, Nengjun, Shen, Yueping
• Format: Journal Article
• Language: English
• Published: Greece International Institute of Anticancer Research 01.01.2022
• Subjects: Bayes Theorem; Bayesian analysis; Biomarkers; Biomarkers, Tumor - analysis; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Cancer; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Integration; Mathematical models; Medical prognosis; Metabolomics; Modelling; Models, Biological; Neoplasms - diagnosis; Neoplasms - epidemiology; Neoplasms - genetics; Performance prediction; Precision medicine; Precision Medicine - methods; Prediction models; Predictions; Prognosis; Proteomics; Review; Tumors; review; Bayesian; survival; integrative; multi-omics; single-omics; cancer; prognosis
• ISSN: 1109-6535; 1790-6245
• DOI: 10.21873/cgp.20298

With the development of high-throughput biological techniques, high-dimensional omics data have emerged. These molecular data provide a solid foundation for precision medicine and prognostic prediction of cancer. Bayesian methods contribute to constructing prognostic models with complex relationships in omics and improving performance by introducing different prior distribution, which is suitable for modelling the high-dimensional data involved. Using different omics, several Bayesian hierarchical approaches have been proposed for variable selection and model construction. In particular, the Bayesian methods of multi-omics integration have also been consistently proposed in recent years. Compared with single-omics, multi-omics integration modelling will contribute to improving predictive performance, gaining insights into the underlying mechanisms of tumour occurrence and development, and the discovery of more reliable biomarkers. In this work, we present a review of current proposed Bayesian approaches in prognostic prediction modelling in cancer.