Role of the NF-κB Transcriptome and Proteome as Biomarkers in Human Head and Neck Squamous Cell Carcinomas

Chen, Zhong; Yan, Bin; Waes, Carter Van

doi:10.2217/17520363.2.4.409

Cited by 24 publications

(15 citation statements)

References 101 publications

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“…Although further literature search on humans could not validate our highly consistent results, we discovered that these interactions are associated to additional types of cancer, such as HNC [187], CRC [158], renal cancer (RC), small cell lung cancer [188], malignant melanoma (MM) [189-192] and OVCa [193]. Additionally, regarding our further research on the interaction between IL6 and TGFB1 genes on mouse models, we have confirmed our initial results principally for BC [155-157] and PC [159] and have noticed associations with epithelial cancer [194], skin tumour [195], LC [196], OVCa and cervical cancer (CC) [197,198] and HNSCC [199]. Second, we found that the interaction between MMP-2 and EGF was associated with LC, BC and GC (Table 4).…”

Section: Resultssupporting

confidence: 68%

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

et al. 2013

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A large number of common disorders, including cancer, have complex genetic traits, with multiple genetic and environmental components contributing to susceptibility. A literature search revealed that even among several meta-analyses, there were ambiguous results and conclusions. In the current study, we conducted a thorough meta-analysis gathering the published meta-analysis studies previously reported to correlate any random effect or predictive value of genome variations in certain genes for various types of cancer. The overall analysis was initially aimed to result in associations (1) among genes which when mutated lead to different types of cancer (e.g. common metabolic pathways) and (2) between groups of genes and types of cancer. We have meta-analysed 150 meta-analysis articles which included 4,474 studies, 2,452,510 cases and 3,091,626 controls (5,544,136 individuals in total) including various racial groups and other population groups (native Americans, Latinos, Aborigines, etc.). Our results were not only consistent with previously published literature but also depicted novel correlations of genes with new cancer types. Our analysis revealed a total of 17 gene-disease pairs that are affected and generated gene/disease clusters, many of which proved to be independent of the criteria used, which suggests that these clusters are biologically meaningful.

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Section: Resultssupporting

confidence: 68%

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

et al. 2013

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“…The transcriptome and proteome modifiers play a significant role in cell growth and survival, especially following therapy. 47 , 48 It is well described that alternate splicing and miRNA affect the eventual translation of the expressed gene, and post-translational modifications further control cellular effects of the transcribed proteins. 49 – 51 Variability in single nucleotide polymorphism also affects response to therapy.…”

Section: Discussionmentioning

confidence: 99%

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

et al. 2014

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With advent of several treatment options in multiple myeloma, a selection of effective regimen has become an important issue. Use of GEP is considered an important tool in predicting outcome; however, it is unclear whether such genomic analysis alone can adequately predict therapeutic response. We evaluated ability of GEP to predict complete response in MM. GEP from pre-treatment MM cells from 136 uniformly treated MM patients with response data on an IFM, France led study were analyzed. To evaluate variability in predictive power due to microarray platform or treatment types, additional datasets from three different studies (n= 511) were analyzed using same methods. We used several machine learning methods to derive a prediction model using training and test subsets of the original four datasets. Among all methods employed for GEP-based CR predictive capability, we got accuracy range of 56% to 78% in test datasets and no significant difference with regard to GEP platforms, treatment regimens or in newly-diagnosed or relapsed patients. Importantly, permuted p-value showed no statistically significant CR predictive information in GEP data. This analysis suggests that GEP-based signature has limited power to predict CR in MM, highlighting the need to develop comprehensive predictive model using integrated genomics approach.

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“…In a systems biology study, we have also identified 748 potential NF-κB target genes that are functionally associated with HNSCC by using an integrative model COGRIM [16]. NF-κB and related signaling pathways have served as potential biomarkers and therapeutic targets for HNSCC and other human cancers [17], [18], [19]. Together with investigations from our and other laboratories, we have shown that p53 and NF-κB are critical regulatory determinants of multiple gene expression programs, interacting pathways, and malignant phenotypes of HNSCC [15], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Unraveling Regulatory Programs for NF-kappaB, p53 and MicroRNAs in Head and Neck Squamous Cell Carcinoma

Yan

Yang

et al. 2013

PLoS ONE

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In head and neck squamous cell carcinoma (HNSCC), mutations of p53 usually coexist with aberrant activation of NF-kappaB (NF-κB), other transcription factors and microRNAs, which promote tumor pathogenesis. However, how these factors and microRNAs interact to globally modulate gene expression and mediate oncogenesis is not fully understood. We devised a novel bioinformatics method to uncover interactive relationships between transcription factors or microRNAs and genes. This approach is based on matrix decomposition modeling under the joint constraints of sparseness and regulator-target connectivity, and able to integrate gene expression profiling and binding data of regulators. We employed this method to infer the gene regulatory networks in HNSCC. We found that the majority of the predicted p53 targets overlapped with those for NF-κB, suggesting that the two transcription factors exert a concerted modulation on regulatory programs in tumor cells. We further investigated the interrelationships of p53 and NF-κB with five additional transcription factors, AP1, CEBPB, EGR1, SP1 and STAT3, and microRNAs mir21 and mir34ac. The resulting gene networks indicate that interactions among NF-κB, p53, and the two miRNAs likely regulate progression of HNSCC. We experimentally validated our findings by determining expression of the predicted NF-κB and p53 target genes by siRNA knock down, and by examining p53 binding activity on promoters of predicted target genes in the tumor cell lines. Our results elucidating the cross-regulations among NF-κB, p53, and microRNAs provide insights into the complex regulatory mechanisms underlying HNSCC, and shows an efficient approach to inferring gene regulatory programs in biological complex systems.

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Role of the NF-κB Transcriptome and Proteome as Biomarkers in Human Head and Neck Squamous Cell Carcinomas

Cited by 24 publications

References 101 publications

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

Unraveling Regulatory Programs for NF-kappaB, p53 and MicroRNAs in Head and Neck Squamous Cell Carcinoma

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