scholarly journals Regulatory network controlling tumor-promoting inflammation in human cancers

2018 ◽  
Author(s):  
Zhe Ji ◽  
Lizhi He ◽  
Aviv Regev ◽  
Kevin Struhl
Keyword(s):  
Cancer Cells ◽  
Regulatory Network ◽  
Feedback Loop ◽  
Genome Instability ◽  
Transcriptional Regulatory Network ◽  
Cellular Transformation ◽  
Inflammatory Index ◽  
Expression Of Genes ◽  
Oncogenic Pathways ◽  
Transcriptional Regulatory

ABSTRACTUsing an inducible, inflammatory model of breast cellular transformation, we describe the transcriptional regulatory network mediated by STAT3, NF-κB, and AP-1 factors on a genomic scale. These regulators form transcriptional complexes that directly regulate the expression of hundreds of genes in oncogenic pathways via a positive feedback loop. This inflammatory feedback loop, which functions to various extents in many types of cancer cells and patient tumors, is the basis for an “inflammation” index that defines cancer types by functional criteria. We identify a network of non-inflammatory genes whose expression is well correlated with the cancer inflammatory index. Conversely, the inflammation index is negatively correlated with expression of genes involved in DNA metabolism, and transformation is associated with genome instability. Inflammatory tumors are preferentially associated with infiltrating immune cells that might be recruited to the site of the tumor via inflammatory molecules produced by the cancer cells.

scholarly journals Inflammatory regulatory network mediated by the joint action of NF-kB, STAT3, and AP-1 factors is involved in many human cancers

2019 ◽  
Vol 116 (19) ◽  
pp. 9453-9462 ◽  
Author(s):  
Zhe Ji ◽  
Lizhi He ◽  
Aviv Regev ◽  
Kevin Struhl
Keyword(s):  
Cancer Cells ◽  
Regulatory Network ◽  
Feedback Loop ◽  
Genome Instability ◽  
Transcriptional Regulatory Network ◽  
Cellular Transformation ◽  
Inflammatory Index ◽  
Oncogenic Pathways ◽  
Cancer Inflammation ◽  
Personalized Cancer Therapy

Using an inducible, inflammatory model of breast cellular transformation, we describe the transcriptional regulatory network mediated by STAT3, NF-κB, and AP-1 factors on a genomic scale. These proinflammatory regulators form transcriptional complexes that directly regulate the expression of hundreds of genes in oncogenic pathways via a positive feedback loop. This transcriptional feedback loop and associated network functions to various extents in many types of cancer cells and patient tumors, and it is the basis for a cancer inflammation index that defines cancer types by functional criteria. We identify a network of noninflammatory genes whose expression is well correlated with the cancer inflammatory index. Conversely, the cancer inflammation index is negatively correlated with the expression of genes involved in DNA metabolism, and transformation is associated with genome instability. We identify drugs whose efficacy in cell lines is correlated with the cancer inflammation index, suggesting the possibility of using this index for personalized cancer therapy. Inflammatory tumors are preferentially associated with infiltrating immune cells that might be recruited to the site of the tumor via inflammatory molecules produced by the cancer cells.

scholarly journals Human transcriptional gene regulatory network compiled from 14 data resources

2021 ◽  
Author(s):  
VIJAYKUMAR Yogesh MULEY ◽  
Rainer Koenig
Keyword(s):  
Regulatory Network ◽  
Transcriptional Repression ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Confidence Score ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Significant Gene ◽  
Spatio Temporal ◽  
Weak Expression

Transcriptional regulatory network (TRN) orchestrates spatio-temporal expression of genes to generate cellular responses for survival. The transcription factors (TF) regulating expression of their target genes (TG) are the fundamental units of TRN. Several databases have been developed to catalogue human TRN based on low- and high-throughput experimental and computational studies considering their importance in understanding cellular physiology. However, literature lacks comparative assessment on the strength and weakness of each database. In this study, we compared over 2.2 million regulatory pairs between 1,379 TF and 22,518 TG assembled from 14 data resources. Our study reveals that the TF and TG were common across data resources but not their regulatory pairs. We observed that the TF and TG of the regulatory pairs showed weak expression correlation, significant gene ontology overlap, co-citations in PubMed and low numbers of TF-TG pairs representing transcriptional repression relationships. Furthermore, each TF-TG regulatory pair assigned a combined confidence score reflecting its reliability based on its presence in multiple databases and co-expression. The TRN containing 2,246,598 TF-TG pairs, of which, 44,284 with the information on TF′s activating or repressing effects on their TG is available upon request. This study brings the information about transcriptional regulation scattered over the literature and databases at one place in the form of one of the most comprehensive and complete human TRNs assembled to date, which will be a valuable resource for benchmarking TRN prediction tools, and to the scientific community working in functional genomics, gene expression and gene regulation analysis.

scholarly journals Topological and causal structure of the yeast transcriptional regulatory network

10.1038/ng873 ◽  
2002 ◽  
Vol 31 (1) ◽  
pp. 60-63 ◽  
Author(s):  
Nabil Guelzim ◽  
Samuele Bottani ◽  
Paul Bourgine ◽  
François Képès
Keyword(s):  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Causal Structure ◽  
Transcriptional Regulatory

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

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