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Gene expression signatures that can discriminate oral leukoplakia subtypes and squamous cell carcinoma

Department of Biochemistry II, National Defense Medical College, 3-2 Namiki, Tokorozawa-shi 359-8513, Japan.
Oral Oncology (Impact Factor: 3.03). 06/2007; 43(5):455-62. DOI: 10.1016/j.oraloncology.2006.04.012
Source: PubMed

ABSTRACT The purpose of this study is to generate a classifier for oral squamous cell carcinoma (OSCC) and leukoplakias (LPs), and evaluate its diagnostic potential. In order to identify marker gene candidates, differential gene expression between LPs and OSCCs were examined by cDNA microarray. The expression of 118 marker gene candidates was further evaluated by quantitative reverse transcription-PCR (QRT-PCR) analyses of 27 OSCC and 19 LP tissues. We identified 12 up-regulated and 15 down-regulated marker genes in OSCCs compared to LPs. Using Fisher's linear discriminant analysis (LDA), we demonstrated that 11-gene predictors among this novel marker set could best distinguish OSCCs from LPs (>97% accuracy), whereas a further seven of these gene predictors could be utilized to distinguish higher grade (higher than moderate) from lower grade (lower than mild) dysplasias (>95% accuracy). These predictor gene sets provide multigene classifiers for the diagnosis of pre-cancerous to cancerous transition of oral malignancy.

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Available from: Masanobu Shindoh, Aug 12, 2015
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    • "Recently, with the emergency of microarray technology that can monitor thousands of genes simultaneously, gene biomarkers are being detected for oral cancers. For example, Saintigny et al. [2] defined a 29-transcripts signature while Kondoh et al. [3] defined another 11-genes signature that can help separate oral cancers developed from OLKs from normal OLKs. Despite the good discrimination capacity of the transcript signature, few of the genes in the signature have functional relationships which make it difficult to understand the malignant transformation of oral leukoplakia. "
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    ABSTRACT: In clinic, oral leukoplakia (OLK) may develop into oral cancer. However, the mechanism underlying this transformation is still unclear. In this work, we present a new pipeline to identify oral cancer related genes and microRNAs (miRNAs) by integrating both gene and miRNA expression profiles. In particular, we find some network modules as well as their miRNA regulators that play important roles in the development of OLK to oral cancer. Among these network modules, 91.67% of genes and 37.5% of miRNAs have been previously reported to be related to oral cancer in literature. The promising results demonstrate the effectiveness and efficiency of our proposed approach.
    01/2015; 2015:1-10. DOI:10.1155/2015/841956
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    • "Before proceeding to LDA, the feature selection step was conducted to reduce the number of genes, because classical LDA requires the total scatter matrix to be nonsingular, while the matrix can be singular when the sample size (149) does not exceed the number of features (genes) (more than 30,000) [27], and tends to overfit and become less interpretable in the presence of many irrelevant and/or redundant features [28]. Based on the previous reports on microarray data analysis [29] [30], we selected only the genes that were up-regulated (fc > 2 and p < 0.05) or down-regulated (fc < 0.5 and p < 0.05) in the groups with increased or decreased liver weight when compared to the not-increased or not-decreased groups, respectively. "
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    ABSTRACT: While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the Class Association Rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.
    Toxicology Reports 11/2014; 1. DOI:10.1016/j.toxrep.2014.10.014
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    • "This is especially so because the medical literature, based on epidemiological studies, is replete with classical examples like chronic inflammation in the cirrhotic liver of alcoholics being associated with hepato-cellular carcinoma (Seitz and Stickel 2006) and chronic gastric inflammation with gastric cancer (Schottenfeld and Beebe-Dimmer 2006). The commonalities between aberrant wound healing and malignancy warranted scientists globally to evaluate critically the proteins and their variants that would be at the crossroads of inflammation and malignancy (Karin and Greten 2005; Kornman 2006) with the knowledge that Cancer involves aberrations in gene(s) and/or gene products associated with proliferation, differentiation, apoptosis, angiogenesis and invasive potential and this information should also be coupled to data obtained by human cancer cell lines (in vitro) (Kondoh et al. 2006; Jeon et al. 2004). In this context, elegant experiments have been made possible by the availability of RNAi technologies (stable RNA interference) which would permit the specific knock-down of a gene in the cellular context, thereby permitting the evaluation of its role and/or its potential in terms of its prognostic significance (Sun et al. 2007). "
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    ABSTRACT: Telephone: +91 44 4210 3440, Fax: +91 44 28472410, E-mail: p_k_suresh@rediffmail.com 2. Department of Oral and Maxillo-Facial Pathology, RAGAS, Dental College and Hospital, 2/ 102, East Coast Road, Uthandi, Chennai 600 119, Tamil Nadu, India Telephone: +91-44 -24491736, Fax: +91-44-24493718, E-mail: ran2@vsnl.com 3. Chennai Dental Research Foundation, No.56, Radhakrishnan Salai, Mylapore, Chennai 600 004 and RAGAS Dental College and Hospital, 2/102, East Coast Road, Uthandi, Chennai 600 119, Tamil Nadu, India Telephone: +91 44 4210 3440, Fax: +91 44 28472410, E-mail: bbaallaa2002@yahoo.co.in 4. Telephone: +91-44-42103440, Fax: +91-44-28472410, E-mail: gunaraga@md2.vsnl.net.in KEYWORDS Gene expression signatures; database; in vitro; ex vivo; biomarkers; genomics; transcriptomics; proteomics ABSTRACT Tumor heterogeneity has warranted the development and validation of gene expression signatures. This approach will help the oncologist in improving diagnosis and/or prognosis. Chronic Inflammation is considered to be involved in neoplasia and commonalities have been observed, in the wound healing response as well as in tumorigenesis. In this regard, the serum response of fibroblasts was shown to mimic the in vivo wound healing response in terms of the "wound response signature" using a variety of molecular biological approaches. Correlation of such signatures, using samples obtained by ex vivo and in vitro methods, developed and/or validated by a variety of genomic, transcriptomic and proteomic approaches with the clinical outcome, will enable the researcher and the oncologist/ clinician to interface and potentially develop and validate cost-effective methods of diagnosis potentially leading up to personalized therapy. Patient selection and stratification and sample size considerations are paramount in developing and validation of improved genomic classifiers.
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