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

• Published in: International journal of molecular sciences Vol. 24; no. 9; p. 7781
• Main Authors: Al-Tashi, Qasem, Saad, Maliazurina B, Muneer, Amgad, Qureshi, Rizwan, Mirjalili, Seyedali, Sheshadri, Ajay, Le, Xiuning, Vokes, Natalie I, Zhang, Jianjun, Wu, Jia
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.04.2023; MDPI
• Subjects: Analysis; Biological markers; biomarker discovery; Biomarkers; Biomarkers - analysis; Biomarkers, Tumor; Cancer; Cancer research; Cancer therapies; Disease prevention; Health aspects; Humans; Immunotherapy; Learning algorithms; Lymphoma; Machine Learning; Medical prognosis; Neoplasms - diagnosis; Patients; personalized medicine; Precision Medicine; predictive biomarker; Prognosis; prognostic biomarker; Prospective Studies; Review; subgroup identification; Systematic review; deep learning; prognostic biomarker; predictive biomarker; machine learning; biomarker discovery; feature selection; personalized medicine; subgroup identification
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24097781

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.