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Journals
Biomolecules
Special Issues
Predictive Biomarker for Oncology
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Predictive Biomarker for Oncology

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Cellular Biochemistry".

Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 6734

Special Issue Editors

Prof. Dr. Young Kee Shin

E-Mail Website
Guest Editor
Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
Interests: biomarker discovery; translational R&D; anti-cancer therapeutic antibody; antibody cytokine fusion protein
Dr. Jooseok Kim

E-Mail Website
Co-Guest Editor
Research Institute of Pharmaceutical Sciences and College of Pharmacy, Seoul National University, Seoul 08826, Korea
Interests: c-MET; EGFR; tyrosine kinase inhibitor; Non-small cell lung cancer (NSCLC); companion diagnostics (CDx)

Special Issue Information

Dear Colleagues,

Precision oncology has increasingly focused on the development of predictive biomarkers and targeted anti-cancer drugs, and the co-development process of novel predictive biomarkers and targeted anti-cancer drugs is contributing to the advance of precision oncology. Recent predictive biomarker research has been conducted for a variety of  (1) predictive biomarker candidates including HER2, EGFR, PDL1, microsatellite instability-high, and homologous recombination deficiency, (2) platform technologies (e.g., next-generation sequencing, multiomics, and single-cell genomics), and (3) sample resources, such as exosome, circulating tumor DNA, and circulating tumor cells. The research on various aspects of predictive biomarkers will help therapeutic decisions, provide benefits for patients, and lead to precision oncology for all cancer patients.

This Special Issue will host review and research articles that focus on the composition of therapeutic and diagnostic approaches using predictive biomarkers and targeted anti-cancer drugs that would provide theoretical background and evidence for precision oncology. In addition, we specifically welcome multidisciplinary research articles including omics and bioinformatic analysis, non-clinical, and clinical models.

Prof. Dr. Young Kee Shin
Guest Editor
Dr. Jooseok Kim
Co-Guest Editor

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. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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

  • predictive biomarker
  • anti-cancer drug
  • targeted cancer therapy
  • immuno-oncology
  • tissue-agnostic cancer therapy
  • precision medicine
  • personalized medicine
  • companion diagnostics

Published Papers (2 papers)

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Research

19 pages, 11803 KiB  
Article
Molecular Initiating Events Associated with Drug-Induced Liver Malignant Tumors: An Integrated Study of the FDA Adverse Event Reporting System and Toxicity Predictions
by Kota Kurosaki and Yoshihiro Uesawa
Biomolecules 2021, 11(7), 944; https://doi.org/10.3390/biom11070944 - 25 Jun 2021
Cited by 8 | Viewed by 2625
Abstract
Liver malignant tumors (LMTs) represent a serious adverse drug event associated with drug-induced liver injury. Increases in endocrine-disrupting chemicals (EDCs) have attracted attention in recent years, due to their liver function-inhibiting abilities. Exposure to EDCs can induce nonalcoholic fatty liver disease and nonalcoholic [...] Read more.
Liver malignant tumors (LMTs) represent a serious adverse drug event associated with drug-induced liver injury. Increases in endocrine-disrupting chemicals (EDCs) have attracted attention in recent years, due to their liver function-inhibiting abilities. Exposure to EDCs can induce nonalcoholic fatty liver disease and nonalcoholic steatohepatitis, which are major etiologies of LMTs, through interaction with nuclear receptors (NR) and stress response pathways (SRs). Therefore, exposure to potential EDC drugs could be associated with drug-induced LMTs. However, the drug classes associated with LMTs and the molecular initiating events (MIEs) that are specific to these drugs are not well understood. In this study, using the Food and Drug Administration Adverse Event Reporting System, we detected LMT-inducing drug signals based on adjusted odds ratios. Furthermore, based on the hypothesis that drug-induced LMTs are triggered by NR and SR modulation of potential EDCs, we used the quantitative structure–activity relationship platform for toxicity prediction to identify potential MIEs that are specific to LMT-inducing drug classes. Events related to cell proliferation and apoptosis, DNA damage, and lipid accumulation were identified as potential MIEs, and their relevance to LMTs was supported by the literature. The findings of this study may contribute to drug development and research, as well as regulatory decision making. Full article
(This article belongs to the Special Issue Predictive Biomarker for Oncology)
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15 pages, 2077 KiB  
Article
Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS
by Claudiu Răchieriu, Dan Tudor Eniu, Emil Moiş, Florin Graur, Carmen Socaciu, Mihai Adrian Socaciu and Nadim Al Hajjar
Biomolecules 2021, 11(3), 417; https://doi.org/10.3390/biom11030417 - 11 Mar 2021
Cited by 21 | Viewed by 3482
Abstract
Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I [...] Read more.
Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI+ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the “lipogenic phenotype” specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer. Full article
(This article belongs to the Special Issue Predictive Biomarker for Oncology)
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