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Biomedicines
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Biomarkers and New Therapeutical Strategies for Cancer Diagnosis and Treatment...
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Biomarkers and New Therapeutical Strategies for Cancer Diagnosis and Treatment (Volume II)

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 3909

Special Issue Editors

Prof. Dr. Agata Grazia D'Amico

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Guest Editor
Department of Drug and Health Sciences, University of Catania, 95100 Catania, Italy
Interests: neuroanatomy; neuroscience; neuropeptides; neural stem cells; identification of carcinogenic bi-omarkers; study of molecular mechanisms involved in cancers progression
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luca Vanella

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Guest Editor
Department of Drug and Health Sciences, Section of Biochemistry, University of Catania, Catania, Italy
Interests: biochemistry; antioxidants; oxidative stress; heme oxygenase
Special Issues, Collections and Topics in MDPI journals
Dr. Antonella Marino Gammazza

E-Mail Website
Guest Editor
Department of Biomedicine, Neuroscience and Advanced Diagnostics Institute of Human Anatomy and Histology, University of Palermo, Palermo, Italy
Interests: anatomy; oxidative stress; molecular chaperones; cell signaling; cell biology; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancers could be considered multigenic pathologies with complex etiologies. Many tumors are characterized by high histological and molecular heterogeneity as well as a highly tumorigenic subpopulation of cancerous stem cells derived from normal stem cells affected by the inflammatory microenvironment.

These pluripotent stem cells are the main culprits of tumor resistance to chemotherapy. Today, there are many antineoplastic agents used in clinical practice that have developed severe side effects and are also responsible for the alteration of molecular mechanisms leading to drug resistance. Therefore, the identification of new therapeutical targets and the characterization of molecular mechanisms underlying cancer progression and chemoresistance represent an emerging issue in the oncology field.

Notably, a growing number of studies have indicated that miRNAs have multiple functions in tumorigenesis, cancer cell proliferation and apoptosis, cancer cell invasion and migration, therapeutic resistance, and the tumor microenvironment.

The aim of this Special Issue is to gather information about promising biomarkers useful for diagnosis and patient monitoring in different cancers, as well as the identification of bioactive compounds to be used as therapeutic agents to counteract proliferation and chemoresistance. The latter is often associated with the overexpression of endogenous antioxidant systems and alteration of redox homeostasis. Preclinical studies can be designed to transfer new knowledge from basic to biomedical science, in order to generate advanced diagnostic and therapeutic applications.

Prof. Dr. Agata Grazia D'Amico
Prof. Dr. Luca Vanella
Dr. Antonella Marino Gammazza
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. Biomedicines 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 2600 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.

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Published Papers (1 paper)

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Research

17 pages, 1627 KiB  
Article
Discovering Common miRNA Signatures Underlying Female-Specific Cancers via a Machine Learning Approach Driven by the Cancer Hallmark ERBB
by Katia Pane, Mario Zanfardino, Anna Maria Grimaldi, Gustavo Baldassarre, Marco Salvatore, Mariarosaria Incoronato and Monica Franzese
Biomedicines 2022, 10(6), 1306; https://doi.org/10.3390/biomedicines10061306 - 2 Jun 2022
Cited by 2 | Viewed by 2886
Abstract
Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a [...] Read more.
Big data processing, using omics data integration and machine learning (ML) methods, drive efforts to discover diagnostic and prognostic biomarkers for clinical decision making. Previously, we used the TCGA database for gene expression profiling of breast, ovary, and endometrial cancers, and identified a top-scoring network centered on the ERBB2 gene, which plays a crucial role in carcinogenesis in the three estrogen-dependent tumors. Here, we focused on microRNA expression signature similarity, asking whether they could target the ERBB family. We applied an ML approach on integrated TCGA miRNA profiling of breast, endometrium, and ovarian cancer to identify common miRNA signatures differentiating tumor and normal conditions. Using the ML-based algorithm and the miRTarBase database, we found 205 features and 158 miRNAs targeting ERBB isoforms, respectively. By merging the results of both databases and ranking each feature according to the weighted Support Vector Machine model, we prioritized 42 features, with accuracy (0.98), AUC (0.93–95% CI 0.917–0.94), sensitivity (0.85), and specificity (0.99), indicating their diagnostic capability to discriminate between the two conditions. In vitro validations by qRT-PCR experiments, using model and parental cell lines for each tumor type showed that five miRNAs (hsa-mir-323a-3p, hsa-mir-323b-3p, hsa-mir-331-3p, hsa-mir-381-3p, and hsa-mir-1301-3p) had expressed trend concordance between breast, ovarian, and endometrium cancer cell lines compared with normal lines, confirming our in silico predictions. This shows that an integrated computational approach combined with biological knowledge, could identify expression signatures as potential diagnostic biomarkers common to multiple tumors. Full article
(This article belongs to the Special Issue Biomarkers and New Therapeutical Strategies for Cancer Diagnosis and Treatment (Volume II))
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