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Emerging Advances in Cancer Biomarkers: Machine Learning, Radiomics, Genomics, and More

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Genetics and Genomics".

Deadline for manuscript submissions: 20 February 2025 | Viewed by 2143

Special Issue Editors

Dr. Hung-Yu Lin

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Guest Editor
Research Assistant Center, Show Chwan Memorial Hospital, Changhua 500, Taiwan
Interests: lncRNA; miRNA; epigenetics; cancers; ageing-related diseases; RNA-targeting therapy
Special Issues, Collections and Topics in MDPI journals
Dr. Pei-Yi Chu

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Guest Editor
Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 402, Taiwan
Interests: lncRNA; miRNA; epigenetics; cancers; ageing-related diseases; RNA-targeting therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, the exponential growth in human genomics has enabled personalized medicine to enter the realm of diagnostics, prognostics and treatment for cancers. More recently, human radiomics has also been implicated in medical-image-guided precision medicine for cancers. Genomics and radiomics can be mutually linked and integrated to form an emerging discipline named “radiogenomics”, which aims to correlate imaging features with genetic characteristics.

Advances in artificial intelligence and machine learning have spurred significant interest in precision medicine. The application of these techniques in the analysis of genomics, radiomics and/or radiogenomics provides an opportunity for many aspects of clinical oncology, including the identification of biomarkers, the development of therapeutics, and the clarification of the mechanisms implicated.

This Special Issue aims to highlight the two omics bases involved in the improvement of precision oncology. Research areas may include (but are not limited to) genomics, radiomics, radiogenomics, transcriptomics, pharmacogenetics, and the machine learning approach. We welcome basic research papers addressing cancer biomarkers, and professional opinions and reviews investigating the broad role of molecular biology and imaging in the clinical management of cancer. We look forward to receiving your contributions.

Dr. Hung-Yu Lin
Dr. Pei-Yi Chu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cancers
  • biomarkers
  • precision medicine
  • artificial intelligence
  • machine learning
  • radiomics
  • genomics
  • radiogenomics
  • multi-omics
  • pharmacogenetics

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Published Papers (2 papers)

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14 pages, 2233 KiB  
Article
Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status
by Kamala K. Arumalla, Jean-François Haince, Rashid A. Bux, Guoyu Huang, Paramjit S. Tappia, Bram Ramjiawan, W. Randolph Ford and Maria Vaida
Int. J. Mol. Sci. 2024, 25(23), 13029; https://doi.org/10.3390/ijms252313029 - 4 Dec 2024
Viewed by 247
Abstract
Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This [...] Read more.
Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This study uses metabolomics data from plasma samples to detect metabolite biomarkers that could distinguish ER-positive from ER-negative breast cancers in a non-invasive manner. The dataset includes demographic information, ER status, and metabolite levels from 188 breast cancer patients and 73 healthy controls. Recursive Feature Elimination (RFE) with a Random Forest (RF) classifier identified an optimal subset of 30 features—29 biomarkers and age—that achieved the highest area under the curve (AUC). To address the class imbalance, Gaussian noise-based augmentation and Adaptive Synthetic Oversampling (ADASYN) were applied, ensuring balanced representation during training. Four machine learning (ML) algorithms—Random Forest, Support Vector Classifier (SVC), XGBoost, and Logistic Regression (LR)—were evaluated using grid search. The Random Forest classifier emerged as the top performer, achieving an AUC of 0.95 and an accuracy of 93%. These results suggest that ML has great promise for identifying specific metabolites linked to ER expression, paving the development of a novel analytical tool that can minimize current challenges in identifying ER status, and improve the precision of breast cancer subtyping. Full article
(This article belongs to the Special Issue Emerging Advances in Cancer Biomarkers: Machine Learning, Radiomics, Genomics, and More)
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18 pages, 19195 KiB  
Article
Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression
by Agustina Sabater, Pablo Sanchis, Rocio Seniuk, Gaston Pascual, Nicolas Anselmino, Daniel F. Alonso, Federico Cayol, Elba Vazquez, Marcelo Marti, Javier Cotignola, Ayelen Toro, Estefania Labanca, Juan Bizzotto and Geraldine Gueron
Int. J. Mol. Sci. 2024, 25(21), 11356; https://doi.org/10.3390/ijms252111356 - 22 Oct 2024
Viewed by 1351
Abstract
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied [...] Read more.
Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the seven-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management. Full article
(This article belongs to the Special Issue Emerging Advances in Cancer Biomarkers: Machine Learning, Radiomics, Genomics, and More)
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