scholarly journals Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis

2014 ◽  
Vol 13s5 ◽  
pp. CIN.S14055 ◽  
Author(s):  
Seyed M. Iranmanesh ◽  
Nancy L. Guo
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Copy Number ◽  
Regulatory Networks ◽  
Cancer Metastasis ◽  
Integrative Analysis ◽  
Transcriptional Level ◽  
Dna Copy Number ◽  
Driver Genes ◽  
Lung Cancer Metastasis

Integrative analysis of multi-level molecular profiles can distinguish interactions that cannot be revealed based on one kind of data in the analysis of cancer susceptibility and metastasis. DNA copy number variations (CNVs) are common in cancer cells, and their role in cell behaviors and relationship to gene expression (GE) is poorly understood. An integrative analysis of CNV and genome-wide mRNA expression can discover copy number alterations and their possible regulatory effects on GE. This study presents a novel framework to identify important genes and construct potential regulatory networks based on these genes. Using this approach, DNA copy number aberrations and their effects on GE in lung cancer progression were revealed. Specifically, this approach contains the following steps: (1) select a pool of candidate driver genes, which have significant CNV in lung cancer patient tumors or have a significant association with the clinical outcome at the transcriptional level; (2) rank important driver genes in lung cancer patients with good prognosis and poor prognosis, respectively, and use top-ranked driver genes to construct regulatory networks with the COpy Number and Expression In Cancer (CONEXIC) method; (3) identify experimentally confirmed molecular interactions in the constructed regulatory networks using Ingenuity Pathway Analysis (IPA); and (4) visualize the refined regulatory networks with the software package Genatomy. The constructed CNV/mRNA regulatory networks provide important insights into potential CNV-regulated transcriptional mechanisms in lung cancer metastasis.

scholarly journals Integrative Analysis of Membrane Proteome and MicroRNA Reveals Novel Lung Cancer Metastasis Biomarkers

2020 ◽  
Vol 11 ◽  
Author(s):  
Yan Kong ◽  
Zhi Qiao ◽  
Yongyong Ren ◽  
Georgi Z. Genchev ◽  
Maolin Ge ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Metastasis ◽  
Integrative Analysis ◽  
Membrane Proteome ◽  
Lung Cancer Metastasis

Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data

2012 ◽  
Vol 51 (7) ◽  
pp. 696-706 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Halfdan Rydbeck ◽  
Stine H. Kresse ◽  
Emilie P. Buddingh ◽  
Ana B. Lid ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Copy Number ◽  
Integrative Analysis ◽  
Expression Data ◽  
Driver Genes

scholarly journals iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

2019 ◽  
Author(s):  
Amy Li ◽  
Bjoern Chapuy ◽  
Xaralabos Varelas ◽  
Paola Sebastiani ◽  
Stefano Monti
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Regulatory Networks ◽  
Large Scale ◽  
Method Development ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cis And Trans

AbstractThe emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively.First, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks.We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible athttps://montilab.bu.edu/iEDGE.

Abstract 5128: Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data

2012 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Halfdan Rydbeck ◽  
Stine H. Kresse ◽  
Emilie P. Buddingh ◽  
Helene Roelofs ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Copy Number ◽  
Integrative Analysis ◽  
Expression Data ◽  
Driver Genes

scholarly journals Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse

Oncotarget ◽  
2016 ◽  
Vol 7 (49) ◽  
pp. 80664-80679 ◽  
Author(s):  
Patryk Krzeminski ◽  
Luis A. Corchete ◽  
Juan L. García ◽  
Lucía López-Corral ◽  
Encarna Fermiñán ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Multiple Myeloma ◽  
Copy Number ◽  
Integrative Analysis ◽  
Dna Copy Number

scholarly journals Identification of Primary and Metastatic Lung Cancer-Related lncRNAs and Potential Targeted Drugs Based on ceRNA Network

2021 ◽  
Vol 10 ◽  
Author(s):  
Siyao Dong ◽  
Cheng Wu ◽  
Chengyan Song ◽  
Baocui Qi ◽  
Lu Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Progression ◽  
Regulatory Networks ◽  
Cancer Metastasis ◽  
Lung Squamous Cell Carcinoma ◽  
Close Correlation ◽  
Therapeutic Effects ◽  
Lung Cancer Metastasis ◽  
Metastatic Lung ◽  
Specific Association

Lung cancer metastasis is the leading cause of poor prognosis and death for patients. Long noncoding RNAs (lncRNAs) have been validated the close correlation with lung cancer metastasis, but few comprehensive analyses have reported the specific association between lncRNA and cancer metastasis, especially via both competing endogenous RNA (ceRNA) regulatory relationships and functional regulatory networks. Here, we constructed primary and metastatic ceRNA networks, identified 12 and 3 candidate lncRNAs for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) respectively and excavated some drugs that might have potential therapeutic effects on lung cancer progression. In summary, this study systematically analyzed the competitive relationships and regulatory mechanism of the repeatedly dysregulated lncRNAs in lung cancer carcinogenesis and metastasis, and provided a new idea for screening potential therapeutic drugs for lung cancer.

scholarly journals Computational drug repositioning of bortezomib to reverse metastatic effect ofGALNT14in lung cancer

2018 ◽  
Author(s):  
Ok-Seon Kwon ◽  
Haeseung Lee ◽  
Hyeon-Joon Kong ◽  
Ji Eun Park ◽  
Wooin Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
In Silico ◽  
Large Scale ◽  
Cancer Metastasis ◽  
Drug Repositioning ◽  
Expression Patterns ◽  
Lung Cancer Metastasis

AbstractAlthough many molecular targets for cancer therapy have been discovered, they often show poor druggability, which is a major obstacle to develop targeted drugs. As an alternative route to drug discovery, we adopted anin silicodrug repositioning (in silicoDR) approach based on large-scale gene expression signatures, with the goal of identifying inhibitors of lung cancer metastasis. Our analysis of clinicogenomic data identified GALNT14, an enzyme involved in O-linked N-acetyl galactosamine glycosylation, as a putative driver of lung cancer metastasis leading to poor survival. To overcome the poor druggability of GALNT14, we leveraged Connectivity Map approach, anin silicoscreening for drugs that are likely to revert the metastatic expression patterns. It leads to identification of bortezomib (BTZ) as a potent metastatic inhibitor, bypassing direct inhibition of poorly druggable target, GALNT14. The anti-metastatic effect of BTZ was verifiedin vitroandin vivo. Notably, both BTZ treatment andGALNT14knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes, suggesting that BTZ acts by modulating TGFβ signalingTaken together, these results demonstrate that ourin silicoDR approach is a viable strategy to identify a candidate drug for undruggable targets, and to uncover its underlying mechanisms.

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