PD.128 Gene expression profiles of oralsquamous cell carcinoma depending on lymph node status

2005 ◽  
Vol 1 (1) ◽  
pp. 104
Author(s):  
T. Fillies ◽  
H. Bürger ◽  
B. Brandt ◽  
D. Kemmmg ◽  
R. Werkmeister ◽  
...  
2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 483-483
Author(s):  
John Hogan ◽  
Michael O Callaghan ◽  
Conor Judge ◽  
Cormac O Connor ◽  
A Aziz ◽  
...  

483 Background: Currently, techniques that determine lymph node positivity (prior to resection) have poor sensitivity and specificity. The ability to determine lymph node status, based on preoperative biopsies, would greatly assist in planning treatment in colorectal cancer. This is particularly relevant in polyp-detected cancers. This study aims to harness the potential of public gene expression repositories, to develop gene expression profiles that could accurately determine nodal status in colorectal cancer. Methods: Public gene expression repositories were screened for experiments comparing metastatic and non-metastatic colorectal cancer. A customized graphic user interface was developed to extract genes dysregulated across the majority of identified studies (i.e., consensus profiles or “CP”). The utility of CP was tested by determining if classifiers could be derived that determined nodal positivity or negativity. CP-derived classifiers were tested on separate Affymetrix and Illumina–based experiments and collated outputs compiled in summary-receiver operator curve characteristic format. The association between classification and oncologic outcome was determined using an additional, independent data set. Results: Four consensus profiles were generated from which classifiers were derived that accurately determined node positive and negative status (pooled AUC were 0.79 ± 0.04 and 0.8 ± 0.03 for nodal positivity and negativity respectively). Overall AUC ranged from 0.73 to 0.86 demonstrating high accuracy across consensus profile type, classification technique and array platform used. As CP enabled classification of nodal status, survival outcomes could be compared for those predicted node negative or positive. Patterns of disease-free and overall survival were identical to those observed for standard histopathologic nodal status. Genes contained within consensus profiles were strongly linked to the metastatic process and included FYN, WNT5A, COL8A1 and BMP. Conclusions: Microarray expression data available in public gene expression repositories can be harnessed to generate consensus profiles. The latter are a source of classifiers that have prognostic and predictive properties.


2019 ◽  
Vol 94 (3) ◽  
Author(s):  
Purnima Gupta ◽  
Naveed Shahzad ◽  
Alexis Harold ◽  
Masahiro Shuda ◽  
Assunta Venuti ◽  
...  

ABSTRACT Merkel cell polyomavirus (MCPyV) is the first human polyomavirus etiologically associated with Merkel cell carcinoma (MCC), a rare and aggressive form of skin cancer. Similar to other polyomaviruses, MCPyV encodes early T antigen genes, viral oncogenes required for MCC tumor growth. To identify the unique oncogenic properties of MCPyV, we analyzed the gene expression profiles in human spontaneously immortalized keratinocytes (NIKs) expressing the early genes from six distinct human polyomaviruses (PyVs), including MCPyV. A comparison of the gene expression profiles revealed 28 genes specifically deregulated by MCPyV. In particular, the MCPyV early gene downregulated the expression of the tumor suppressor gene N-myc downstream-regulated gene 1 (NDRG1) in MCPyV gene-expressing NIKs and hTERT-MCPyV gene-expressing human keratinocytes (HK) compared to their expression in the controls. In MCPyV-positive MCC cells, the expression of NDRG1 was downregulated by the MCPyV early gene, as T antigen knockdown rescued the level of NDRG1. In addition, NDRG1 overexpression in hTERT-MCPyV gene-expressing HK or MCC cells resulted in a decrease in the number of cells in S phase and cell proliferation inhibition. Moreover, a decrease in wound healing capacity in hTERT-MCPyV gene-expressing HK was observed. Further analysis revealed that NDRG1 exerts its biological effect in Merkel cell lines by regulating the expression of the cyclin-dependent kinase 2 (CDK2) and cyclin D1 proteins. Overall, NDRG1 plays an important role in MCPyV-induced cellular proliferation. IMPORTANCE Merkel cell carcinoma was first described in 1972 as a neuroendocrine tumor of skin, most cases of which were reported in 2008 to be caused by a PyV named Merkel cell polyomavirus (MCPyV), the first PyV linked to human cancer. Thereafter, numerous studies have been conducted to understand the etiology of this virus-induced carcinogenesis. However, it is still a new field, and much work is needed to understand the molecular pathogenesis of MCC. In the current work, we sought to identify the host genes specifically deregulated by MCPyV, as opposed to other PyVs, in order to better understand the relevance of the genes analyzed on the biological impact and progression of the disease. These findings open newer avenues for targeted drug therapies, thereby providing hope for the management of patients suffering from this highly aggressive cancer.


2004 ◽  
Vol 2 (3) ◽  
pp. 125
Author(s):  
B Weigelt ◽  
A.M Glas ◽  
L.F Wessels ◽  
A.T Witteveen ◽  
A.J Bosma ◽  
...  

2019 ◽  
Vol 48 (3) ◽  
pp. 030006051989383 ◽  
Author(s):  
Xing Wu ◽  
Linlin Wang ◽  
Fan Feng ◽  
Suyan Tian

Objective To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications. Methods Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes. Results The five-gene diagnostic signature including keratin 5 ( KRT5), mucin 1 ( MUC1), triggering receptor expressed on myeloid cells 1 ( TREM1), complement C3 ( C3) and transmembrane serine protease 2 ( TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide ( ADH1C), alpha-2-glycoprotein 1, zinc-binding ( AZGP1), clusterin ( CLU), cyclin dependent kinase 1 ( CDK1) and paternally expressed 10 ( PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set. Conclusions Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.


PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0239783
Author(s):  
Inger-Heidi Bjerkli ◽  
Helene Laurvik ◽  
Elisabeth Sivy Nginamau ◽  
Tine M. Søland ◽  
Daniela Costea ◽  
...  

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