miR-20a mediates temozolomide-resistance in glioblastoma cells via negatively regulating LRIG1 expression

Qi, Xuchen; Zhan, Qiwei; Zhou, Daoyang; Yan, Qingfeng; Wang, Yirong; Mo, Lianjie; Wan, Yingfeng; Xie, Dajiang; Xie, Jixi; Yang, Shuxu

doi:10.1016/j.biopha.2015.01.026

Cited by 25 publications

(16 citation statements)

References 32 publications

(34 reference statements)

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“…LRIG1 is highly expressed in the brain and negatively regulates the EGFR signaling pathway (49). Reduced LRIG1 expression correlates with tumor aggressiveness as well as resistance to temozolomide and radiotherapy (50, 51). An additional association at 3p14.1 is with rs812481 and colorectal cancer risk (52), as this SNP is correlated with rs11706832 the association signals appear to overlap ( r 2 = 0.67, D′ = 0.83).…”

Section: Chronological History Of Glioma Risk Loci Discoverymentioning

confidence: 99%

Genome-Wide Association Studies in Glioma

Kinnersley

Houlston

Bondy

2018

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Since the first reports in 2009, genome-wide association studies (GWAS) have been successful in identifying germline variants associated with glioma susceptibility. In this review, we describe a chronological history of glioma GWAS, culminating in the most recent study comprising 12,496 cases and 18,190 controls. We additionally summarize associations at the 27 glioma-risk SNPs that have been reported so far. Future efforts are likely to be principally focused on assessing association of germline-risk SNPs with particular molecular subgroups of glioma, as well as investigating the functional basis of the risk loci in tumor formation. These ongoing studies will be important to maximize the impact of research into glioma susceptibility, both in terms of insight into tumor etiology as well as opportunities for clinical translation.

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Section: Chronological History Of Glioma Risk Loci Discoverymentioning

confidence: 99%

Genome-Wide Association Studies in Glioma

Kinnersley

Houlston

Bondy

2018

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…Although we did not identify an eQTL in this gene, LRIG1 is highly expressed in the brain and is a pan-negative regulator of the epidermal growth factor receptor (EGFR) signaling pathway, which inhibits hypoxia-induced vasculogenic mimicry via EGFR–PI3K–AKT pathway suppression and epithelial-to-mesenchymal transition 26 . Reduced LRIG1 expression is linked to tumor aggressiveness, temozolomide resistance and radio-resistance 27, 28 . We have previously shown an association for glioma at EGFR (7p11.2) 7 , which is well established to be pivotal in both the initiation of primary GBM and the progression of lower-grade glioma to grade IV.…”

mentioning

confidence: 99%

Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors

Melin¹,

Barnholtz‐Sloan²,

Wrensch³

et al. 2017

Nat Genet

274

364

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Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10−9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10−10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10−8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10−11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10−10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10−9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10−10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10−10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10−9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10−8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10−10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10−11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10−9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.

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“…, proliferation, angiogenesis, and invasion (Henriksen et al 2014; Singh et al 2012; Zhang et al 2009). Due to their broader functional relevance, miRNA-20a , -204 , -181c , and -340 also have been identified as oncogenic genes and may serve as targets for treatment of GBM (Wang et al 2015; Wei et al 2015; Xia et al 2015; Huang et al 2015; Ruan et al 2015). Additionally, several target genes of these miRNAs have been validated in previous studies, e.g.…”

Section: Resultsmentioning

confidence: 99%

A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease

Zhang

Lin

et al. 2017

G3 Genes|Genomes|Genetics

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Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.

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miR-20a mediates temozolomide-resistance in glioblastoma cells via negatively regulating LRIG1 expression

Cited by 25 publications

References 32 publications

Genome-Wide Association Studies in Glioma

Genome-Wide Association Studies in Glioma

Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors

A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease

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