Computational Analysis of Breast Cancer GWAS Loci Identifies the Putative Deleterious Effect of <i>STXBP4</i> and <i>ZNF404</i> Gene Variants

Masoodi, Tariq; Banaganapalli, Babajan; Vaidyanathan, Venkatesh; Talluri, Venkateswara Rao; Shaik, Noor Ahmad

doi:10.1002/jcb.26080

Cited by 11 publications

(11 citation statements)

References 39 publications

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“…Despite generally good prognosis for BC patients, there is a wide variation in survival [1]. Some of the common risk factors for BC include age, genetic background, hormonal factors, reproductive and menstrual history, excessive alcohol consumption, radiation, benign breast disease, and obesity [2]. Genetic variation has been shown to affect both susceptibility and prognosis of BC [1].…”

Section: Introductionmentioning

confidence: 99%

“…Genetic variation has been shown to affect both susceptibility and prognosis of BC [1]. Genome wide association studies (GWAS) have recently been used to identify new loci for BC, and so far almost 100 loci have been identified [2]. In addition to providing new prognostic markers, genetic determinants of prognosis can also give new biological insight for progression of BC [3].…”

Section: Introductionmentioning

confidence: 99%

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Polymorphisms in Survivin (BIRC5 Gene) Are Associated with Age of Onset in Breast Cancer Patients

Sušac¹,

Ozretić

Gregorić

et al. 2019

Journal of Oncology

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Survivin, encoded by BIRC5 gene (baculoviral IAP repeat containing 5), belongs to the family of inhibitors of apoptosis proteins (IAPs). In mammalian cells it participates in the control of mitosis, apoptosis regulation, and cellular stress response. Its expression is increased in almost all types of cancers. The aim of this study was to investigate the role of BIRC5 polymorphisms in breast cancer (BC) and to connect survivin expression with various clinicopathological characteristics of BC patients. Blood and archival tumour tissue samples were collected from 26 BC patients from Croatia. Survivin expression was determined immunohistochemically. BIRC5 promoter, coding region, and 3’UTR were genotyped. DNA from 74 healthy women was used as control. BIRC5 polymorphisms and survivin expression were tested against age of onset, histological grade, tumour type and size, lymph node status, oestrogen, progesterone, Her2, and Ki67 status. Numbers of samples with weak, moderate, and strong survivin expression were 9 (33.3%), 11 (40.7%), and 7 (25.9%), respectively. Most patients had nuclear survivin staining (92.6%). High survivin expression was significantly associated with negative oestrogen receptor status (p=0.007) and positive Ki67 expression (p=0.032). Ki67 expression was also positively correlated with histological grade (p=0.0009). Fourteen polymorphisms were found in BC samples, located mostly in promoter and 3’UTR of BIRC5. There was no significant difference in the distribution of polymorphisms between BC and control samples. Among clinicopathological characteristics of BC patients, alleles of five BIRC5 polymorphisms were associated with younger age of onset: c.-644T>C (55.8 years [y] vs. 48.1 y; p=0.006), c.-241C>T (54.2 y vs. 45.0; p=0.029), c.9809T>C (55.8 y vs. 48.1 y; p=0.006), c.-1547C>T (58.3 y vs. 50.9 y; p=0.011), and c.9386T>C (50.8 y vs. 59.5 y; p=0.004). To assess the significance of BIRC5 polymorphisms and survivin expression as predictive and prognostic biomarkers for BC further research with a larger sample size is needed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Polymorphisms in Survivin (BIRC5 Gene) Are Associated with Age of Onset in Breast Cancer Patients

Sušac¹,

Ozretić

Gregorić

et al. 2019

Journal of Oncology

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“…Even though there were respective 18 (56.25%) and 2 (100%) independent TWAS genes of breast and ovarian cancer by PrediXcan also identified by TIGAR (see Figure S11 and Tables S8, S9, S10), only TIGAR identified the novel TWAS genes UBE2MP1 and FRG1EP shared by both breast and ovarian cancer and the known GWAS risk genes FGF10 [11,58] and TOX3 [36,38] of breast cancer. Other exclusive independent TWAS genes identified by TIGAR include lncRNA RP11-758M4.4 which was shown to be a potential biomarker of breast cancer [59], RPS23 which was found to be over-expressed in advanced colorectal adenocarcinomas [60], and ZNF404 whose dysregulation was linked to breast cancer pathogenesis by eQTL analyses [61,62].…”

Section: Compare With Twas Results By Predixcanmentioning

confidence: 99%

“…Other exclusive independent TWAS genes identified by TIGAR include lncRNA RP11-758M4. 4 which was shown to be a potential biomarker of breast cancer 68 , RPS23 which was found to be over-expressed in advanced colorectal adenocarcinomas [MIM: 114500] 69 , and ZNF404 whose dysregulation was linked to breast cancer pathogenesis by eQTL analyses 70,71 . Potentially novel TWAS risk genes by PrediXcan and TIGAR that were not identified by previous GWAS are presented in Table S11.…”

Section: Twas Of Breast and Ovarian Cancermentioning

confidence: 99%

TIGAR-V2: Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8

Parrish

Gibson

Epstein

et al. 2021

Preprint

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Standard Transcriptome-Wide Association Study (TWAS) methods first train gene expression prediction models using reference transcriptomic data, and then test the association between the predicted genetically regulated gene expression and phenotype of interest. Most existing TWAS tools require cumbersome preparation of genotype input files and extra coding to enable parallel computation. To improve the efficiency of TWAS tools, we develop TIGAR-V2, which directly reads VCF files, enables parallel computation, and reduces up to 90% computation cost compared to the original version. TIGAR-V2 can train gene expression imputation models using either nonparametric Bayesian Dirichlet Process Regression (DPR) or Elastic-Net (as used by PrediXcan), perform TWAS using either individual-level or summary-level GWAS data, and implements both burden and variance-component test statistics for inference. We trained gene expression prediction models by DPR for 49 tissues using GTEx V8 by TIGAR-V2 and illustrated the usefulness of these nonparametric Bayesian DPR eQTL weights through TWAS of breast and ovarian cancer utilizing public GWAS summary statistics. We identified 88 and 37 risk genes respectively for breast and ovarian cancer, most of which are either known or near previously identified GWAS (~95%) or TWAS (~40%) risk genes of the corresponding phenotype and three novel independent TWAS risk genes with known functions in carcinogenesis. These findings suggest that TWAS can provide biological insight into the transcriptional regulation of complex diseases. TIGAR-V2 tool, trained Bayesian cis-eQTL weights, and LD information from GTEX V8 are publicly available, providing a useful resource for mapping risk genes of complex diseases.

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“…Recent genome-wide association studies (GWAS) and improved single-nucleotide polymorphism (SNP) analyses have revealed hundreds of common genetic mutations closely related to T2DM. However, the relationship between gene polymorphism and the occurrence of T2DM, as well as its underlying mechanism, is still unclear [ 2 , 3 , 4 , 5 ]. Consequently, the etiology and pathogenesis of T2DM have not been fully elucidated.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment of type 2 diabetes in northern China based on the logistic regression model

Liu

et al. 2021

THC

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BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex disease with high incidence and serious harm associated with polygenic determination. This study aimed to develop a predictive model so as to assess the risk of T2DM and apply it to health care and disease prevention in northern China. OBJECTIVE: Based on genotyping results, a risk warning model for type 2 diabetes was established. METHODS: Blood samples of 1042 patients with T2DM in northern China were collected. Multiplex polymerase chain reaction and high-throughput sequencing (NGS) techniques were used to design the amplification-based targeted sequencing panel to sequence the 21 T2DM susceptibility genes. RESULT: The related key gene KQT-like subfamily member 1 played an important role in the T2DM risk model, and single-nucleotide polymorphism rs2237892 was highly significant, with a P value of 1.2 × 10-5. CONCLUSIONS: Susceptibility genes in different populations were examined, and a model was developed to assess the risk-based genetic analysis. The performance of the model reached 92.8%.

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Computational Analysis of Breast Cancer GWAS Loci Identifies the Putative Deleterious Effect of STXBP4 and ZNF404 Gene Variants

Cited by 11 publications

References 39 publications

Polymorphisms in Survivin (BIRC5 Gene) Are Associated with Age of Onset in Breast Cancer Patients

Polymorphisms in Survivin (BIRC5 Gene) Are Associated with Age of Onset in Breast Cancer Patients

TIGAR-V2: Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8

Risk assessment of type 2 diabetes in northern China based on the logistic regression model

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