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Special Issue "Big Data for Oncology"

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: closed (31 March 2017)

Special Issue Editor

Guest Editor
Dr. William Chi-shing Cho

Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
E-Mail
Interests: cancer biomarker; chinese medicine; diabetes mellitus; evidence-based medicine; genomics; microRNA; molecular diagnostics; nasopharyngeal carcinoma; non-small cell lung cancer; proteomics

Special Issue Information

Dear Colleagues,

In the era of Big Data, we are flooded by a large amount of information and data. The informatics age will probably revolutionize our understanding and management of information in diverse disciplines. A number of international consortia and institutions have set rules to guarantee the transparency and traceability of data so as to ensure its usefulness.

In this Special Issue, we, in particular, focus on how Big Data impacts, and offer solution to, oncology. With the emergence of many high-throughput technologies, and the heterogeneous data generated from different platforms, there is a need to synthesize the useful data from the various platforms, so as to provide information for accurate diagnoses, and the prescription of the right drug and the correct treatment to the right patient for the effective management of cancer.

We welcome original papers and review articles that focus on the latest advances of Big Data. The following key areas are covered, but are not exclusive:

  • Next-generation oncology
  • Standardized data collection to build prediction models in oncology
  • Integrative clustering of multi-omics data: application to molecular classification of cancer
  • Next-generation informatics for Big Data in the precision medicine era
  • Big Data for cancer signaling pathway study
  • Application of Big Data for cancer biomarker discovery and validation
  • Big Data for cancer drug discovery
  • Regulatory networks from Big Data: a roadmap for oncologists
  • Next-generation sequencing and Big Data for cancer research
  • Imaging in the era of Big Data
  • Clinical trials, data protection and Big Data
  • Dig Data in Chinese medicine for oncology services

Dr. William Chi-shing Cho
Guest Editor

Manuscript Submission Information

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. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access monthly 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 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

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Open AccessArticle ADAM9 Expression Is Associate with Glioma Tumor Grade and Histological Type, and Acts as a Prognostic Factor in Lower-Grade Gliomas
Int. J. Mol. Sci. 2016, 17(9), 1276; doi:10.3390/ijms17091276
Received: 31 May 2016 / Revised: 23 July 2016 / Accepted: 25 July 2016 / Published: 26 August 2016
Cited by 1 | PDF Full-text (734 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The A disintegrin and metalloproteinase 9 (ADAM9) protein has been suggested to promote carcinoma invasion and appears to be overexpressed in various human cancers. However, its role has rarely been investigated in gliomas and, thus, in the current study we have evaluated ADAM9
[...] Read more.
The A disintegrin and metalloproteinase 9 (ADAM9) protein has been suggested to promote carcinoma invasion and appears to be overexpressed in various human cancers. However, its role has rarely been investigated in gliomas and, thus, in the current study we have evaluated ADAM9 expression in gliomas and examined the relevance of its expression in the prognosis of glioma patients. Clinical characteristics, RNA sequence data, and the case follow-ups were reviewed for 303 patients who had histological, confirmed gliomas. The ADAM9 expression between lower-grade glioma (LGG) and glioblastoma (GBM) patients was compared and its association with progression-free survival (PFS) and overall survival (OS) was assessed to evaluate its prognostic value. Our data suggested that GBM patients had significantly higher expression of ADAM9 in comparison to LGG patients (p < 0.001, t-test). In addition, among the LGG patients, aggressive astrocytic tumors displayed significantly higher ADAM9 expression than oligodendroglial tumors (p < 0.001, t-test). Moreover, high ADAM9 expression also correlated with poor clinical outcome (p < 0.001 and p < 0.001, log-rank test, for PFS and OS, respectively) in LGG patients. Further, multivariate analysis suggested ADAM9 expression to be an independent marker of poor survival (p = 0.002 and p = 0.003, for PFS and OS, respectively). These results suggest that ADAM9 mRNA expression is associated with tumor grade and histological type in gliomas and can serve as an independent prognostic factor, specifically in LGG patients. Full article
(This article belongs to the Special Issue Big Data for Oncology)
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Open AccessArticle Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer
Int. J. Mol. Sci. 2016, 17(8), 1272; doi:10.3390/ijms17081272
Received: 21 June 2016 / Revised: 22 July 2016 / Accepted: 27 July 2016 / Published: 5 August 2016
Cited by 4 | PDF Full-text (3176 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular
[...] Read more.
Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis. Full article
(This article belongs to the Special Issue Big Data for Oncology)
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Open AccessArticle Gene Set-Based Functionome Analysis of Pathogenesis in Epithelial Ovarian Serous Carcinoma and the Molecular Features in Different FIGO Stages
Int. J. Mol. Sci. 2016, 17(6), 886; doi:10.3390/ijms17060886
Received: 13 April 2016 / Revised: 7 May 2016 / Accepted: 16 May 2016 / Published: 6 June 2016
Cited by 3 | PDF Full-text (5249 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Serous carcinoma (SC) is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO) staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not
[...] Read more.
Serous carcinoma (SC) is the most common subtype of epithelial ovarian carcinoma and is divided into four stages by the Federation of Gynecologists and Obstetrics (FIGO) staging system. Currently, the molecular functions and biological processes of SC at different FIGO stages have not been quantified. Here, we conducted a whole-genome integrative analysis to investigate the functions of SC at different stages. The function, as defined by the GO term or canonical pathway gene set, was quantified by measuring the changes in the gene expressional order between cancerous and normal control states. The quantified function, i.e., the gene set regularity (GSR) index, was utilized to investigate the pathogenesis and functional regulation of SC at different FIGO stages. We showed that the informativeness of the GSR indices was sufficient for accurate pattern recognition and classification for machine learning. The function regularity presented by the GSR indices showed stepwise deterioration during SC progression from FIGO stage I to stage IV. The pathogenesis of SC was centered on cell cycle deregulation and accompanied with multiple functional aberrations as well as their interactions. Full article
(This article belongs to the Special Issue Big Data for Oncology)
Figures

Open AccessArticle The Clinical Significance of the Insulin-Like Growth Factor-1 Receptor Polymorphism in Non-Small-Cell Lung Cancer with Epidermal Growth Factor Receptor Mutation
Int. J. Mol. Sci. 2016, 17(5), 763; doi:10.3390/ijms17050763
Received: 6 April 2016 / Revised: 6 May 2016 / Accepted: 16 May 2016 / Published: 18 May 2016
PDF Full-text (214 KB) | HTML Full-text | XML Full-text
Abstract
The insulin-like growth factor 1 (IGF1) signaling pathway mediates multiple cancer cell biological processes. IGF1 receptor (IGF1R) expression has been used as a reporter of the clinical significance of non-small-cell lung carcinoma (NSCLC). However, the association between IGF1R genetic variants and the clinical
[...] Read more.
The insulin-like growth factor 1 (IGF1) signaling pathway mediates multiple cancer cell biological processes. IGF1 receptor (IGF1R) expression has been used as a reporter of the clinical significance of non-small-cell lung carcinoma (NSCLC). However, the association between IGF1R genetic variants and the clinical utility of NSCLC positive for epidermal growth factor receptor (EGFR) mutation is not clear. The current study investigated the association between the IGF1R genetic variants, the occurrence of EGFR mutations, and clinicopathological characteristics in NSCLC patients. A total of 452 participants, including 362 adenocarcinoma lung cancer and 90 squamous cell carcinoma lung cancer patients, were selected for analysis of IGF1R genetic variants (rs7166348, rs2229765, and rs8038415) using real-time polymerase chain reaction (PCR)genotyping. The results indicated that GA + AA genotypes of IGF1R rs2229765 were significantly associated with EGFR mutation in female lung adenocarcinoma patients (odds ratio (OR) = 0.39, 95% confidence interval (CI) = 0.17–0.87). Moreover, The GA + AA genotype IGF1R rs2229765 was significantly associated with EGFR L858R mutation (p = 0.02) but not with the exon 19 in-frame deletion. Furthermore, among patients without EGFR mutation, those who have at least one polymorphic A allele of IGF1R rs7166348 have an increased incidence of lymph node metastasis when compared with those patients homozygous for GG (OR, 2.75; 95% CI, 1.20–2.31). Our results showed that IGF1R genetic variants are related to EGFR mutation in female lung adenocarcinoma patients and may be a predictive factor for tumor lymph node metastasis in Taiwanese patients with NSCLC. Full article
(This article belongs to the Special Issue Big Data for Oncology)

Review

Jump to: Research

Open AccessReview Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology
Int. J. Mol. Sci. 2017, 18(1), 37; doi:10.3390/ijms18010037
Received: 18 October 2016 / Revised: 14 December 2016 / Accepted: 17 December 2016 / Published: 27 December 2016
Cited by 1 | PDF Full-text (1433 KB) | HTML Full-text | XML Full-text
Abstract
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for
[...] Read more.
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring “big data” applications in pediatric oncology. Computational strategies derived from big data science–network- and machine learning-based modeling and drug repositioning—hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which “big data” and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases. Full article
(This article belongs to the Special Issue Big Data for Oncology)
Figures

Open AccessReview Cofilin-1 and Other ADF/Cofilin Superfamily Members in Human Malignant Cells
Int. J. Mol. Sci. 2017, 18(1), 10; doi:10.3390/ijms18010010
Received: 30 September 2016 / Revised: 18 November 2016 / Accepted: 1 December 2016 / Published: 22 December 2016
Cited by 2 | PDF Full-text (2097 KB) | HTML Full-text | XML Full-text
Abstract
Identification of actin-depolymerizing factor homology (ADF-H) domains in the structures of several related proteins led first to the formation of the ADF/cofilin family, which then expanded to the ADF/cofilin superfamily. This superfamily includes the well-studied cofilin-1 (Cfl-1) and about a dozen different human
[...] Read more.
Identification of actin-depolymerizing factor homology (ADF-H) domains in the structures of several related proteins led first to the formation of the ADF/cofilin family, which then expanded to the ADF/cofilin superfamily. This superfamily includes the well-studied cofilin-1 (Cfl-1) and about a dozen different human proteins that interact directly or indirectly with the actin cytoskeleton, provide its remodeling, and alter cell motility. According to some data, Cfl-1 is contained in various human malignant cells (HMCs) and is involved in the formation of malignant properties, including invasiveness, metastatic potential, and resistance to chemotherapeutic drugs. The presence of other ADF/cofilin superfamily proteins in HMCs and their involvement in the regulation of cell motility were discovered with the use of various OMICS technologies. In our review, we discuss the results of the study of Cfl-1 and other ADF/cofilin superfamily proteins, which may be of interest for solving different problems of molecular oncology, as well as for the prospects of further investigations of these proteins in HMCs. Full article
(This article belongs to the Special Issue Big Data for Oncology)
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