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Cell-Free Nucleic Acids

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ISBN: 9783039280742 9783039280759 Year: Pages: 248 DOI: 10.3390/books978-3-03928-075-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General)
Added to DOAB on : 2020-01-30 16:39:46
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Abstract

The deficits of mammography and the potential of noninvasive diagnostic testing using circulating miRNA profiles are presented in our first review article. Exosomes are important in the transfer of genetic information. The current knowledge on exosome-associated DNAs and on vesicle-associated DNAs and their role in pregnancy-related complications is presented in the next article. The major obstacle is the lack of a standardized technique for the isolation and measurement of exosomes. One review has summarized the latest results on cell-free nucleic acids in inflammatory bowel disease (IBD). Despite the extensive research, the etiology and exact pathogenesis are still unclear, although similarity to the cell-free ribonucleic acids (cfRNAs) observed in other autoimmune diseases seems to be relevant in IBD. Liquid biopsy is a useful tool for the differentiation of leiomyomas and sarcomas in the corpus uteri. One manuscript has collected the most important knowledge of mesenchymal uterine tumors and shows the benefits of noninvasive sampling. Microchimerism has also recently become a hot topic. It is discussed in the context of various forms of transplantation and transplantation-related advanced therapies, the available cell-free nucleic acid (cfNA) markers, and the detection platforms that have been introduced. Ovarian cancer is one of the leading serious malignancies among women, with a high incidence of mortality; the introduction of new noninvasive diagnostic markers could help in its early detection and treatment monitoring. Epigenetic regulation is very important during the development of diseases and drug resistance. Methylation changes are important signs during ovarian cancer development, and it seems that the CDH1 gene is a potential candidate for being a noninvasive biomarker in the diagnosis of ovarian cancer. Preeclampsia is a mysterious disease—despite intensive research, the exact details of its development are unknown. It seems that cell-free nucleic acids could serve as biomarkers for the early detection of this disease. Three research papers deal with the prenatal application of cfDNA. Copy number variants (CNVs) are important subjects for the study of human genome variations, as CNVs can contribute to population diversity and human genetic diseases. These are useful in NIPT as a source of population specific data. The reliability of NIPT depends on the accurate estimation of fetal fraction. Improvement in the success rate of in vitro fertilization (IVF) and embryo transfer (ET) is an important goal. The measurement of embryo-specific small noncoding RNAs in culture media could improve the efficiency of ET.

Keywords

breast cancer --- screening --- liquid biopsy --- omics --- multi-level diagnostics --- individualized patient profile --- miRNA --- mammography --- predictive and preventive approach --- personalized medicine --- cell-free DNA --- exosomes --- extracellular vesicles --- fetal DNA --- preeclampsia --- growth retardation --- gestational diabetes mellitus --- miRNA --- piRNA --- NGS --- RT-PCR --- embryo culture medium --- C19MC microRNA --- expression --- exosomes --- fetal growth restriction --- gestational hypertension --- plasma --- prediction --- preeclampsia --- pregnancy-related complications --- screening --- non-invasive prenatal testing --- statistical models --- z-score --- cell-free nucleic acids --- circulating nucleic acids --- cell-free DNAs --- cell-free RNAs --- exosomes --- inflammatory bowel disease --- neutrophil extracellular traps --- NETosis --- liquid biopsy --- cell-free nucleic acids --- circulating tumor cells --- leiomyomas --- sarcomas --- leiomyosarcomas --- exosomes --- NIPT --- fetal fraction --- statistical methods --- DNA --- maternal serum screening --- fetal cells --- liquid biopsy --- pyrosequencing --- ovarian cancer --- CDH1 --- PTEN --- PAX1 --- RASSF1 --- cfDNA --- cell-free DNA --- nuclease activity --- aging --- obesity --- gender differences --- copy number variants --- next generation sequencing --- non-invasive prenatal testing --- population study --- microchimerism --- solid organ transplantation --- hematopoietic stem cell transplantation --- genetic marker --- single nucleotide polymorphism --- deletion/insertion polymorphism --- ovarian cancer --- circulating miRNA --- blood plasma --- NanoString --- network analysis --- biomarker --- n/a

Treatment Strategies and Survival Outcomes in Breast Cancer

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ISBN: 9783039287581 / 9783039287598 Year: Pages: 264 DOI: 10.3390/books978-3-03928-759-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Medicine (General) --- Internal medicine
Added to DOAB on : 2020-06-09 16:38:57
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Treatment strategies for breast cancer are wide-ranging and often based on a multi-modality approach, depending on the stage and biology of the tumour and the acceptance and tolerance of the patient. They may include surgery, radiotherapy, and systemic therapy (endocrine therapy, chemotherapy, and targeted therapy). Advances in technologies such as oncoplastic surgery, radiation planning and delivery, and genomics, and the development of novel systemic therapy agents alongside their evaluation in ongoing clinical trials continue to strive for improvements in outcomes. In this Special Issue, we publish a collection of studies looking at all forms of therapeutic strategies for early and advanced breast cancer, focusing on their outcomes, notably survival.

Keywords

advanced breast cancer --- metastatic --- chemotherapy --- antihormone therapy --- HER2 c-erbB2 --- HER2/neu --- trastuzumab --- pertuzumab --- T-DM1 --- lapatinib --- LKB1 --- Breast Cancer --- Older women --- Metformin --- Endocrine therapy --- breast cancer --- breast-conserving therapy --- mastectomy --- outcome --- comparative effectiveness --- metastatic breast cancer --- liquid biopsy --- cell-free DNA --- next-generation sequencing --- circulating tumor cells --- overdiagnosis --- mammography screening --- invasive breast cancer --- zero-inflated Poisson regression model --- breast cancer --- stage IV --- incidence --- tumor biology --- NCDB --- SEER --- Src kinase --- basal-like breast cancer --- cMet --- breast cancer --- radiotherapy --- lymph-node ratio --- disease-free survival --- physical activity --- breast cancer survivors --- physical function --- social well-being --- exercise characteristics --- APOBEC3B --- gene expression --- breast cancer --- ductal carcinoma in situ --- infiltrating breast cancer --- PIK3CA --- ERCC1 --- anthracycline resistance --- taxane sensitivity --- breast cancer --- colorectal cancer --- relative survival --- older patients --- geriatric oncology --- cancer treatment --- metastatic breast cancer --- lactate dehydrogenase --- serum biomarker --- LDH --- monitoring metastatic breast cancer --- palbociclib --- ribociclib --- abemaciclib --- fulvestrant --- aromatase inhibitors --- metastatic breast cancer --- contralateral prophylactic mastectomy --- contralateral breast cancer --- BRCA --- CHEK2 --- PALB2 --- ATM --- mutation carriers --- family history --- survival --- breast cancer --- young women --- histone deacetylase --- HDAC5 inhibitors --- LMK-235 --- breast cancer --- cyclin E --- older patients --- biomarker --- tumor biology --- prognosis --- survival --- n/a

Application of Bioinformatics in Cancers

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ISBN: 9783039217885 9783039217892 Year: Pages: 418 DOI: 10.3390/books978-3-03921-789-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Biotechnology
Added to DOAB on : 2019-12-09 11:49:16
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This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible.

Keywords

comorbidity score --- mortality --- locoregionally advanced --- HNSCC --- curative surgery --- traditional Chinese medicine --- health strengthening herb --- cancer treatment --- network pharmacology --- network target --- high-throughput analysis --- brain metastases --- colorectal cancer --- KRAS mutation --- PD-L1 --- tumor infiltrating lymphocytes --- drug resistance --- gefitinib --- erlotinib --- biostatistics --- bioinformatics --- Bufadienolide-like chemicals --- molecular mechanism --- anti-cancer --- bioinformatics --- cancer --- brain --- pathophysiology --- imaging --- machine learning --- extreme learning --- deep learning --- neurological disorders --- pancreatic cancer --- TCGA --- curation --- DNA --- RNA --- protein --- single-biomarkers --- multiple-biomarkers --- cancer-related pathways --- colorectal cancer --- DNA sequence profile --- Monte Carlo --- mixture of normal distributions --- somatic mutation --- tumor --- mutable motif --- activation induced deaminase --- AID/APOBEC --- transcriptional signatures --- copy number variation --- copy number aberration --- TCGA mining --- cancer CRISPR --- firehose --- gene signature extraction --- gene loss biomarkers --- gene inactivation biomarkers --- biomarker discovery --- chemotherapy --- microarray --- ovarian cancer --- predictive model --- machine learning --- overall survival --- observed survival interval --- skin cutaneous melanoma --- The Cancer Genome Atlas --- omics --- breast cancer prognosis --- artificial intelligence --- machine learning --- decision support systems --- cancer prognosis --- independent prognostic power --- omics profiles --- histopathological imaging features --- cancer --- intratumor heterogeneity --- genomic instability --- epigenetics --- mitochondrial metabolism --- miRNAs --- cancer biomarkers --- breast cancer detection --- machine learning --- feature selection --- classification --- denoising autoencoders --- breast cancer --- feature extraction and interpretation --- concatenated deep feature --- cancer modeling --- interaction --- histopathological imaging --- clinical/environmental factors --- oral cancer --- miRNA --- bioinformatics --- datasets --- biomarkers --- TCGA --- GEO DataSets --- hormone sensitive cancers --- breast cancer --- StAR --- estrogen --- steroidogenic enzymes --- hTERT --- telomerase --- telomeres --- alternative splicing --- network analysis --- hierarchical clustering analysis --- differential gene expression analysis --- cancer biomarker --- diseases genes --- variable selection --- false discovery rate --- knockoffs --- bioinformatics --- copy number variation --- cell-free DNA --- methylation --- mutation --- next generation sequencing --- self-organizing map --- head and neck cancer --- treatment de-escalation --- HP --- molecular subtypes --- tumor microenvironment --- Bioinformatics tool --- R package --- machine learning --- meta-analysis --- biomarker signature --- gene expression analysis --- survival analysis --- functional analysis --- bioinformatics --- machine learning --- artificial intelligence --- Network Analysis --- single-cell sequencing --- circulating tumor DNA (ctDNA) --- Neoantigen Prediction --- precision medicine --- Computational Immunology

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MDPI - Multidisciplinary Digital Publishing Institute (3)


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2020 (2)

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