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Renal Cell Carcinoma

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ISBN: 9783039286386 / 9783039286393 Year: Pages: 500 DOI: 10.3390/books978-3-03928-639-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology
Added to DOAB on : 2020-06-09 16:38:57
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Abstract

Renal cancer is a health problem of major concern worldwide. Although tyrosine kinase inhibitors and immune check-point blockade treatments, alone or in combination, are giving promising results, failures are quite frequent due to intratumor heterogeneity and to the acquisition of drug resistance. The spectrum of renal cell carcinoma subtypes is wide. Up to 70–80% of renal tumors are clear cell renal cell carcinomas, a clinically aggressive tumor subtype linked to VHL gene inactivation. Next in frequency, the papillary renal cell carcinoma category encompasses an intricate puzzle of classic and newly described entities with poorly defined limits, some of them pending definite clarification. Likewise, the chromophobe–oncocytoma duality, the so-called hybrid tumors and oncocytic neoplasms, remain to be well profiled. Finally, a growing list of very uncommon renal tumors linked to specific molecular signatures fulfill the current portrait of renal cell neoplasia. This Special Issue of Cancers regards RCC from very different perspectives, from the intimate basic mechanisms governing this disease to the clinical practice principles of their diagnoses and treatments. The interested reader will have the opportunity to contact with some of the most recent findings and will be updated with excellent reviews.

Keywords

ghrelin --- aurora A --- MMP10 --- invasion --- sarcomatoid --- RCC --- immunotherapy --- checkpoint inhibitors --- survival --- PD-L1 --- chronic kidney disease --- nephrectomy --- overall survival --- recurrence free survival --- renal cell carcinoma --- statins --- uric acid --- intratumour heterogeneity --- metastatic ccRCC --- copy number alteration --- mutation --- gene expression --- MiT family translocation renal cell carcinoma --- Xp11 translocation renal cell carcinoma --- t(6 --- 11) translocation renal cell carcinoma --- FISH --- TFE3 --- TFEB --- TFEB-amplified renal cell carcinoma --- renal cell carcinoma --- immune checkpoint inhibitors --- tyrosine kinase inhibitors --- efficacy --- toxicity --- cytoreductive nephrectomy --- Papillary renal cell carcinoma (pRCC) --- proteome profiling --- metabolome profiling --- glutathione metabolism --- metabolic reprogramming --- IL4R? --- IL13R?1 --- renal cell carcinoma --- JAK2 --- FOXO3 --- clear cell renal cell carcinoma --- identification of circular RNAs --- experimental validation of circular RNA --- diagnostic and prognostic markers --- circular RNAs in a clinico-genomic predictive model --- cancer-specific survival --- recurrence-free survival --- overall survival --- chromophobe renal cell carcinoma --- pale cell --- eosinophilic variant --- chromosomal loss --- copy number analysis --- renal cell carcinoma --- clear cell renal cell carcinoma --- AMP-activated protein kinases --- immunohistochemistry --- prognosis --- SMAD proteins --- transforming growth factor beta --- renal cell cancer --- microRNA --- metabolome --- proliferation --- PPP --- pentose phosphate pathway --- TCA cycle --- miR-155-5p --- miR-146a-5p --- TCGA --- renal cell carcinoma --- metastasis --- MTA2 --- MMP-9 --- miR-133b --- kidney cancer --- immunotherapy --- renal cell --- inflammation markers --- programmed death-ligand 1 --- immune checkpoint inhibitors --- prognostic factors --- predictive factors --- glutathione transferase omega 1 --- glutathione transferase omega 2 --- polymorphism --- PI3K/Akt/mTOR --- Raf/MEK/ERK --- IL-1? --- pro-IL-1? --- gene signature --- renal cancer --- survival prediction --- polybromo-1 --- PBRM1 --- renal cell carcinoma --- biomarker --- prognosis --- predictive role --- collecting duct carcinoma --- RNA sequencing --- solute carrier proteins --- kidney --- renal cell carcinoma --- molecular genetic features --- practical approach --- review --- renal cell carcinoma --- sarcomatoid --- immunotherapy --- renal cell carcinoma --- checkpoint inhibitors --- VEGF inhibitors --- mTOR inhibitors --- kidney --- emerging entity --- new entity --- oncocytic renal tumor --- unclassified renal cell carcinoma --- unclassified renal tumor --- anaplastic lymphoma kinase rearrangement --- ALK --- ESC --- HOT --- LOT --- drug sensitivity --- immune infiltration --- renal cancer --- targeted therapy --- tumor slice culture --- clear cell Renal Cell Carcinoma --- urine --- glycoproteomics --- N-glycomapping --- label-free --- glycomarkers --- everolimus --- EVI1 --- genetic association --- mTOR --- clear cell renal cell carcinoma --- curcumin --- renal cell cancer --- tumor adhesion --- tumor migration --- integrins --- NK cells --- kidney cancer --- renal cell carcinoma --- IL-2 --- cancer immunotherapy --- tumor microenvironment --- von Hippel–Lindau --- EMT like --- hyperosmolality --- chromophobe renal cell carcinoma --- copy number loss --- CDKN1A expression --- patient survival --- prognosis --- 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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Abstract

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 (2)


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