Comprehensive network analysis of genes expressed in human oropharyngeal cancer

Ye, Yu; Li, Simin; Bi, Liangjia

doi:10.1016/j.amjoto.2014.11.002

Cited by 3 publications

(3 citation statements)

References 38 publications

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“… 14 identified distinct immune signatures in tumor-infiltrating lymphocytes (TILs), more specifically in B-cells, related to the adaptive immune response against HPV in those tumors. Gene expression involving microarray technology in HPV+ versus HPV− HNSCC has also been studied 15 , 16 . Other studies considered differences in methylation profiles.…”

Section: Introductionmentioning

confidence: 99%

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

Costa

Boroni

Soares

2018

Sci Rep

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The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV− HNSCC and to provide insights into potential regulatory mechanisms/effects within the analyzed networks. We selected cases deposited in The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information. The intersection among differentially expressed and differentially methylated genes showed the negative correlations between the levels of methylation and expression, suggesting that these genes have their expression levels regulated by methylation alteration patterns in their promoter. Weighted correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three distinct co-expression modules were associated with HPV status and molecular signatures. Validation using independent studies reporting biological experimental data converged for the most significant genes in all modules. This study provides insights into complex genetic and epigenetic particularities in the development and progression of HNSCC according to HPV status, and contribute to unveiling specific genes/pathways as novel therapeutic targets in HNSCC.

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Section: Introductionmentioning

confidence: 99%

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

Costa

Boroni

Soares

2018

Sci Rep

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“…*Correspondence: dario.disilvestre@itb.cnr.it 1 Institute for Biomedical Technologies -National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy Full list of author information is available at the end of the article The magnitude of -omics data provides the opportunity to decode in alternative way the role of biological molecules and processes characterizing the emergent phenotypes. In this scenario, a common procedure to evaluate gene expression levels is based on statistics that measure the dependence between variables, and the resulting co-expression networks are used to identify genes functionally related or controlled by the same transcriptional regulatory program [11][12][13]. Unlike gene expression levels, the use of proteomic data to infer co-expression networks has been explored through few studies [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

Vella

Zoppis

Mauri

et al. 2017

J Bioinform Sys Biology

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The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

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“…Wood et al (2016) (12) identified distinct immune signatures in tumor-infiltrating lymphocytes (TILs), more specifically in B-cells, related to the adaptive immune response against HPV in those tumors. Gene expression involving microarray technology in HPV+ versus HPV-HNSCC has also been studied (13,14). Other studies considered differences in methylation profiles.…”

Section: Introductionmentioning

confidence: 99%

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

Costa

Boroni

Soares

2017

Preprint

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The human papillomavirus (HPV) is present in a significant fraction of head-and-neck squamous cell cancer (HNSCC). However, a comprehensive understanding of disease progression profiles comparing HPV+ and HPV-HNSCC cases is still lacking. The main goal of this study was to identify distinct co-expression patterns between HPV+ and HPV-HNSCC and to provide insights into potential regulatory mechanisms/effects (such as methylation and mutation) within the analyzed networks. For conducting this, we selected 276 samples from The Cancer Genome Atlas database comprising data of gene expression, methylation profiles and mutational patterns, in addition to clinical information (HPV status and tumor staging). We further added external information such as the identification of transcription factors to the networks. Genes were selected as differentially expressed and differentially methylated based on HPV status, of which 12 genes were doubly selected, including SYCP2, GJB6, FLRT3, PITX2 and CCNA1. Weight correlation network analysis was used to identify co-expression modules and a systematic approach was applied to refine them and identify key regulatory elements integrating results from the other omics. Three main modules were associated with distinct co-expression patterns in HPV+ versus HPV-HNSCC. The molecular signatures found were mainly related to cell fate specification, keratinocyte differentiation, focal adhesion and regulation of protein oligomerization. This study provides comprehensive insights into complex genetic and epigenetic particularities in the development and progression of HNSCC in patients according to HPV status, identifying unseen gene interactions, and may contribute to unveiling specific genes/pathways as novel therapeutic targets for HNSCC.

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Comprehensive network analysis of genes expressed in human oropharyngeal cancer

Cited by 3 publications

References 38 publications

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

Distinct co-expression networks using multi-omic data reveal novel interventional targets in HPV-positive and negative head-and-neck squamous cell cancer

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