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On page 1 showing 1 ~ 3 papers out of 3 papers

A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer.

  • Nada A Al-Tassan‎ et al.
  • Scientific reports‎
  • 2015‎

Genome-wide association studies (GWAS) of colorectal cancer (CRC) have identified 23 susceptibility loci thus far. Analyses of previously conducted GWAS indicate additional risk loci are yet to be discovered. To identify novel CRC susceptibility loci, we conducted a new GWAS and performed a meta-analysis with five published GWAS (totalling 7,577 cases and 9,979 controls of European ancestry), imputing genotypes utilising the 1000 Genomes Project. The combined analysis identified new, significant associations with CRC at 1p36.2 marked by rs72647484 (minor allele frequency [MAF] = 0.09) near CDC42 and WNT4 (P = 1.21 × 10(-8), odds ratio [OR] = 1.21 ) and at 16q24.1 marked by rs16941835 (MAF = 0.21, P = 5.06 × 10(-8); OR = 1.15) within the long non-coding RNA (lncRNA) RP11-58A18.1 and ~500 kb from the nearest coding gene FOXL1. Additionally we identified a promising association at 10p13 with rs10904849 intronic to CUBN (MAF = 0.32, P = 7.01 × 10(-8); OR = 1.14). These findings provide further insights into the genetic and biological basis of inherited genetic susceptibility to CRC. Additionally, our analysis further demonstrates that imputation can be used to exploit GWAS data to identify novel disease-causing variants.


Activin signaling is an essential component of the TGF-β induced pro-metastatic phenotype in colorectal cancer.

  • Jonas J Staudacher‎ et al.
  • Scientific reports‎
  • 2017‎

Advanced colorectal cancer (CRC) remains a critical health care challenge worldwide. Various TGF-β superfamily members are important in colorectal cancer metastasis, but their signaling effects and predictive value have only been assessed in isolation. Here, we examine cross-regulation and combined functions of the two most prominent TGF-β superfamily members activin and TGF-β in advanced colorectal cancer. In two clinical cohorts we observed by immune-based assay that combined serum and tissue activin and TGF-β ligand levels predicts outcome in CRC patients and is superior to single ligand assessment. While TGF-β growth suppression is independent of activin, TGF-β treatment leads to increased activin secretion in colon cancer cells and TGF-β induced cellular migration is dependent on activin, indicating pathway cross-regulation and functional interaction in vitro. mRNA expression of activin and TGF-β pathway members were queried in silico using the TCGA data set. Coordinated ligand and receptor expression is common in solid tumors for activin and TGF-β pathway members. In conclusion, activin and TGF-β are strongly connected signaling pathways that are important in advanced CRC. Assessing activin and TGF-β signaling as a unit yields important insights applicable to future diagnostic and therapeutic interventions.


A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring.

  • Souptik Barua‎ et al.
  • Scientific reports‎
  • 2024‎

In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37-63%)] compared to pre-T2D [36% (95% CI 31-48%), p = 0.01] and at-risk participants [34% (95% CI 27-39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.


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