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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Medicine --- Oncology --- Gastrointestinal malignancies --- circulating tumor DNA (ctDNA) --- colorectal cancer --- liquid biopsy

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Medicine --- Oncology --- Gastrointestinal malignancies --- circulating tumor DNA (ctDNA) --- colorectal cancer --- liquid biopsy

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Pharmacology --- liquid biopsy --- precision oncology --- ctDNA (circulating tumor DNA) --- Circulating molecular marker --- personalized medicine

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Malignant mesothelioma (MM) is a rare and aggressive cancer, related to chronic inflammation and oxidative stress caused mainly by exposure to asbestos. Although this mineral has been banned for decades in many countries, epidemiologists predict the MM epidemic will last past 2040, raising many concerns in public health given its late diagnosis, dismal prognosis, and lack of current efficient therapies.To deal with this situation, important breakthroughs have recently been made in the understanding of MM’s complex biology and the carcinogenic process of the different patterns of the disease. Examples of these include the development of new biomarkers and the deciphering of gene–environment interactions, molecular mechanisms of invasiveness, deregulated pathways, altered expression of miRNAs, DNA damage repair, or metabolic profile. From now on, MM’s aggressive and chemoresistant character appears linked to a polyclonal malignancy, and heterogeneity in molecular alterations.Given these improvements, new therapeutic targets are being explored to solve the double challenge faced by clinicians. The first is to reduce tumor development and its wasting consequences as soon as possible, without resistance and with limited toxicity. The second is to stimulate the recognition of tumor cells by the induction of a specific immune response. This Special Issue will highlight all these aspects.

Medicine --- well-differentiated papillary mesothelioma --- WDPM --- malignant mesothelioma --- DNA sequencing --- mutation --- mesothelioma --- tumor suppressor --- targeted therapy --- immunotherapy --- biomarkers --- proteomics --- macrophage-capping protein --- fatty acid-binding protein --- laminin subunit beta-2 --- selenium-binding protein 1 --- carcinogenesis --- malignant pleural mesothelioma --- asbestos exposure --- DNA methylation --- lymphocyte-to-monocyte ratio --- epigenome-wide analysis --- survival analysis --- metabolomics --- radiotherapy --- cancers --- inflammation --- infiltrating immune cells --- prognostic biomarker --- predictive biomarker --- immune therapy --- VATS --- extrapleural pneumonectomy --- pleurectomy decortication --- therapy response --- survival --- FDG --- PET-CT --- mesothelium --- oxidative stress --- redox-sensitive factors --- asbestos --- carbon nanotubes --- protein-protein interactions --- systems biology --- network analysis --- drug repurposing --- pleural mesothelioma --- gene expression --- immunogenicity --- sarcomatoid --- epithelioid --- first line --- meta-analysis --- systematic review --- MPM --- lurbinectedin --- DNA damage response --- histotype --- Hippo pathway --- NF2 --- BAP1 --- CDKN2A --- PTCH1 --- SETD2 --- MTAP --- liquid biopsies --- circulating tumor DNA --- plasma --- cancer-specific mutations --- genomics --- cancer biomarkers --- tumor microenvironment --- tumor-associated macrophages --- dendritic cells --- immunohistochemistry --- interaction analysis --- pleural effusion

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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.

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