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. 2023 Apr 24;24(9):7781.
doi: 10.3390/ijms24097781.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

Qasem Al-Tashi  1 , Maliazurina B Saad  1 , Amgad Muneer  1 , Rizwan Qureshi  1 , Seyedali Mirjalili  2   3   4 , Ajay Sheshadri  5 , Xiuning Le  6 , Natalie I Vokes  6 , Jianjun Zhang  6 , Jia Wu  1   6
Affiliations

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

Qasem Al-Tashi et al. Int J Mol Sci. .

Abstract

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Keywords: biomarker discovery; deep learning; feature selection; machine learning; personalized medicine; predictive biomarker; prognostic biomarker; subgroup identification.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Categorization of biomarkers.
Figure 2
Figure 2
The trend of published number of studies on biomarker identification using machine learning.
Figure 3
Figure 3
PRISMA flowchart [13].
Figure 4
Figure 4
Omics data used for prognostic and predictive biomarker discovery.
Figure 5
Figure 5
Subgroup tree- and non-tree-based methods for biomarker identification.
Figure 6
Figure 6
Map of machine learning approaches used for biomarker identification.
Figure 7
Figure 7
General machine learning framework for biomarker identification.
Figure 8
Figure 8
Types of cancer and biomarkers used by the selected studies in biomarker discovery.
See this image and copyright information in PMC

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