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Risk Factors for Pancreatic Cancer: Underlying Mechanisms and Potential Targets

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889194681 Year: Pages: 115 DOI: 10.3389/978-2-88919-468-1 Language: English
Publisher: Frontiers Media SA
Subject: Physiology --- Science (General)
Added to DOAB on : 2016-03-10 08:14:32
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Abstract

Pancreatic Cancer has been and still is one of the deadliest types of human malignancies. The annual mortality rates almost equal incidence rates making this disease virtually universally fatal. The 5-year survival of patients with pancreatic cancer is a dismal 5% or less. Therapeutic strategies are extremely limited with gemcitabine extending the survival by a disappointing few weeks. The failure of several randomized clinical trials in the past decade investigating the therapeutic efficacy of different mono- and combination therapies reflects our limited knowledge of pancreatic cancer biology. In addition, biomarkers for early detection are sorely missing. Several pancreatic cancer risk factors have been identified. Unfortunately, the underlying mechanisms linking these risk factors to cancer development are poorly understood. Well known possible and probable risk factors for the development of pancreatic cancer are age, smoking, chronic pancreatitis, obesity, and type-2 diabetes mellitus. Age is certainly of the most important risk factors as most cases of pancreatic cancer occur in the elderly population. Smoking ten cigarettes a day increases the risk by 2.6 times and smoking a pack per day increases it by 5 folds. Chronic pancreatitis increases the risk of pancreatic cancer by up to 13 times. Patients with hereditary forms of chronic pancreatitis have an even higher risk. Obesity, a growing global health problem, increases the risk of pancreatic cancer by about 1.5 fold. Type-2 diabetes mellitus is also associated with an increased risk of pancreatic cancer by at least two-fold. The more recent the onset of diabetes, the stronger the correlation with pancreatic cancer is. In addition, heavy alcohol drinking, a family history of the disease, male gender and African American ethnicity are other risk factors for pancreatic cancer. Pancreatic cancer is characterized by several genetic alterations including mutations in the Kras proto-oncogene and mutations in the tumor suppressor genes p53 and p16. While Kras mutations are currently thought as early events present in a certain percentage of pancreatic intraepithelial neoplasias (PanINs), known precursor lesions of pancreatic ductal adenocarcinomas, mutations in tumor suppressor genes, e.g. p53, seem to accumulate later during progression. In addition, several intracellular signaling pathways are amplified or enhanced, including the MAPK/ERK and PI3K/AKT signaling modules. Overall, these genetic alterations lead to enhanced and sustained proliferation, resistance to cell death, invasive and metastatic potential, and angiogenesis, all hallmarks of cancers. The scope of this Research Topic is to collect data and knowledge of how risk factors increase the risk of initiation/progression of pancreatic cancer. Of particular interest are potential underlying molecular mechanisms. Understanding the molecular mechanisms and driving signaling pathways will ultimately allow the development of targeted interventions to disrupt the risk factor-induced cancer development. This Research Topic is interested in a broad range of risk factors, including genetic and environmental, and welcomes original papers, mini and full reviews, and hypothesis papers. Manuscripts that address the effect of combination of risk factors on pancreatic cancer development and progression are of great interest as well.

The Treatment of Metastatic Non-small Cell Lung Cancer (NSCLC) in New Era of Personalised Medicine

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Book Series: Frontiers Research Topics ISSN: 16648714 ISBN: 9782889195435 Year: Pages: 140 DOI: 10.3389/978-2-88919-543-5 Language: English
Publisher: Frontiers Media SA
Subject: Oncology --- Medicine (General)
Added to DOAB on : 2016-01-19 14:05:46
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Lung cancer is the leading cause of cancer related mortality in Canada and USA. Majority of the patients present in advanced stage of the disease and of these only about 2% will be alive at 5 years. NSCLC is the most common form of lung cancer, accounting for approximately 87% of cases. Systemic chemotherapies have been used to treat metastatic NSCLC for decades, but the improvements of outcomes have reached a plateau. Recent advances in understanding signalling pathways for malignant cells, their interconections,the importance of various receptors and biomarkers and the interplay between various oncogenes have led to the development of targeted treatments that are improving both efficacy and safety of the treatments. Knowledge about the advantages of treatments with the targeted agents in metastatic NSCLC is growing rapidly. Combining various targeted agents or sequencing them properly will be important in the era of personalised medicine and overcoming development of the resistence to various targeted agents will be challenging. The importance of a team work,from the diagnosis through various treatments, to supportive care, from the interventional radiologists, pneumologists or surgeons, who have to obtain a satisfactory tumor tissue specimen, to pathologists, radiation and medical oncologists, to supportive care specialists, will be described in our publications. We will cover completely present and future approaches to personalised medicine in this rapidly evolving field of metastatic NSCLC.

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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