Role of MicroRNAs in Human Cancer
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".
Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 14387
Special Issue Editor
2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
Interests: biomarkers; radiation; biostatistics; microRNA; monogenic diabetes; pancreatic cancer; ovarian cancer
Special Issue Information
Dear Colleagues,
Over the last decade, microRNAs have attracted significant and undiminishing attention as key regulators of various biological processes. The involvement of miRNAs in carcinogenesis and their impact on the outcome of oncologic care is profound and widespread, with multiple miRNAs affecting crucial survival pathways and modulating the survival of cancerous cells. The advent of NGS techniques and improvements in qPCR specificity have allowed the rapid accumulation of knowledge on how members of this specific class of ribonucleic acid molecules affect processes like DNA repair, the synthesis of specific proteins, drug resistance, and cell-to-cell communication. Considerable advances have also been made in the application of microRNAs as biomarkers, as they can be readily quantified using various methods in nearly all types of human samples. While miRNA therapeutics are still a thing of the (near) future, microRNAs undoubtedly exert a major impact on cancer biology and are a field of intense study.
The present Special Issue is aimed at collecting latest advances and outstanding research works investigating the role of microRNAs in affecting the pathogenesis, detection, treatment, and prognosis of human cancers, including malignancies manifesting in specific patient populations with a genetic predisposition to malignancies. Original research and review manuscripts are welcome.
Dr. Wojciech Fendler
Guest Editor
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Keywords
- carcinogenesis
- non-coding RNA
- post-transcriptional regulation
- RNA therapeutics
- biomarkers
- predictive models
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