Special Issue "Advanced Methods in Microarrays for Cancer Research"
A special issue of Microarrays (ISSN 2076-3905).
Deadline for manuscript submissions: closed (31 January 2015)
The advent of high-throughput microarray technologies has revolutionized molecular biology and has great impact on the way genetic diseases such as cancer are diagnosed, classified, and treated. New methods emerge for the processing and the analysis of microarray data in an attempt to understand the regulation of gene targets and associating pathways, detect differentially-expressed biomarkers, and classify disease subtypes.
This special issue invites contributions to the advanced development and applications of profiling techniques to characterize microarray profiles of cancer patients and new methodologies for the identification of biomarkers relevant to various aspects of cancer screening, growth, diagnosis, treatment therapy, and prognosis. This special issue aims to stimulate further multidisciplinary research on this emerging science.
Dr. Shu-Kay Ng
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microarrays is an international peer-reviewed Open Access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.
- cancer research
- differential expression
- profiling techniques
- network analysis
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Potential biomarkers and their most correlated path in Lung Cancer
Authors: 1Katia I. Cáceres-Camacho, 1Enery Lorenzo, 1Juan Rosas, 1Michael Ortiz, 1Lynn Perez, 1Juan Acevedo, 1Juan Irizarry, 1Valerie González, 1Mauricio Cabrera-Ríos, 1,2Clara Isaza,
Affiliations: 1 The Applied Optimization Group, Industrial Engineering Department, University of Puerto Rico at Mayagüez
2 Department of Pharmacology, Physiology and Toxicology, Ponce Health Sciences University and School of Medicine
Abstract: This work presents a two-stage analysis pipeline for microarray data. The first stage detects the most differentially expressed genes through multiple criteria optimization and the second one elicits their most correlated path through integer network programming. This pipeline does not require the adjustment of any parameters by the user, the definition of significance thresholds or the use of any data normalization procedure, thereby preserving analysis objectivity and repeatability. A study on lung cancer is presented to demonstrate the capabilities of the method. In particular, the results confirm genes RAGE, SPP1 as importantly related to lung cancer, while providing evidence to include XIST, ADH1B, RPS4Y1, CYP1B1, CEACAM6, MSMB, SCGB1A1, FABP4, KRT15 and FGG for their potential role in lung cancer for different groups of study.
Keywords: microarrays, cancer research, biomarkers, network analysis
Title: Identification of Copy Number Aberrations in Breast Cancer Subtypes using Persistence Topology
Authors: Javier Arsuaga
Affiliations: Mathematics and Molecular and Cellular Biology, University of California at Davis
Abstract: DNA copy number aberrations (CNAs) are of biological and medical interest because they help identify regulatory mechanisms underlying tumor initiation and evolution. Their identification however remains a challenging task because they are hidden by CNAs that are the product of random events that occur during tumor evolution. Experimental detection of CNAs is commonly accomplished through microarray Comparative Genomic Hybridization (mCGH) assays followed by supervised or unsupervised statistical methods that interrogate the segmented profile of each patient. Here we present a new supervised algorithm for the identification of CNAs that is based on a topological representation of the data. Our method associates a 2-dimensional (2D) point cloud to each CGH profile and generates a sequence of simplicial complexes, mathematical objects that generalize the concept of a graph, for different segmentation values. Identification of CNAs is achieved by interrogating the topological properties of these simplicial complexes. We tested our approach on a published data set with the goal of identifying breast cancer CNAs associated with specific molecular subtypes. Our results confirmed all regions found in the original publication except for 17q in luminal B and detected 33 additional regions. Most of the additional regions have been reported in other independent studies. Three not previously reported CNAs, gain of 1p, gain of 2p and loss of 14q, were found on basal subtypes and validated on a second published data set. We therefore suggest that topological approaches that incorporate multiresolution analyses and that interrogate topological properties of the data can help in the identification of copy number changes in cancer.
Keywords: Copy number aberrations; breast cancer; topological data analysis