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Sökning: WFRF:(Xin Lele)

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1.
  • Ren, Luyao, et al. (författare)
  • Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance
  • 2023
  • Ingår i: Genome Biology. - : BioMed Central (BMC). - 1465-6906 .- 1474-760X. ; 24:1
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome.RESULTS: We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data.CONCLUSIONS: The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.
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2.
  • Xie, Yuan, et al. (författare)
  • Wnt signaling regulates MFSD2A-dependent drug delivery through endothelial transcytosis in glioma
  • 2023
  • Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 25:6, s. 1073-1084
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Systemic delivery of anti-tumor therapeutic agents to brain tumors is thwarted by the blood-brain barrier (BBB), an organotypic specialization of brain endothelial cells (ECs). A failure of pharmacological compounds to cross BBB is one culprit for the dismal prognosis of glioblastoma (GBM) patients. Identification of novel vascular targets to overcome the challenges posed by the BBB in tumors for GBM treatment is urgently needed.Methods: Temozolomide (TMZ) delivery was investigated in CT2A and PDGFB-driven RCAS/tv-a orthotopic glioma models. Transcriptome analysis was performed on ECs from murine gliomas. Mfsd2a deficient, Cav1 deficient, and Mfsd2a EC-specific inducible mice were developed to study the underlying molecular mechanisms.Results: We demonstrated that inhibiting Wnt signaling by LGK974 could increase TMZ delivery and sensitize glioma to chemotherapy in both murine glioma models. Transcriptome analysis of ECs from murine gliomas revealed that Wnt signaling inhibition enhanced vascular transcytosis as indicated by the upregulation of PLVAP and downregulation of MFSD2A. Mfsd2a deficiency in mice enhances TMZ delivery in tumors, whereas constitutive expression of Mfsd2a in ECs suppresses the enhanced TMZ delivery induced by Wnt pathway inhibition in murine glioma. In addition, Wnt signaling inhibition enhanced caveolin-1 (Cav1)-positive caveolae-mediated transcytosis in tumor ECs. Moreover, Wnt signaling inhibitor or Mfsd2a deficiency fails to enhance TMZ penetration in tumors from Cav1-deficient mice.Conclusions: These results demonstrated that Wnt signaling regulates MFSD2A-dependent TMZ delivery through a caveolae-mediated EC transcytosis pathway. Our findings identify Wnt signaling as a promising therapeutic target to improve drug delivery for GBM treatment.
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3.
  • Yang, Fan, et al. (författare)
  • Uncovering a Distinct Gene Signature in Endothelial Cells Associated With Contrast Enhancement in Glioblastoma
  • 2021
  • Ingår i: Frontiers in Oncology. - : Frontiers Media S.A.. - 2234-943X. ; 11
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose Glioblastoma (GBM) is the most aggressive and lethal type of brain tumors. Magnetic resonance imaging (MRI) has been commonly used for GBM diagnosis. Contrast enhancement (CE) on T1-weighted sequences are presented in nearly all GBM as a result of high vascular permeability in glioblastomas. Although several radiomics studies indicated that CE is associated with distinct molecular signatures in tumors, the effects of vascular endothelial cells, the key component of blood brain barrier (BBB) controlling vascular permeability, on CE have not been thoroughly analyzed. Methods Endothelial cell enriched genes have been identified using transcriptome data from 128 patients by a systematic method based on correlation analysis. Distinct endothelial cell enriched genes associated with CE were identified by analyzing difference of correlation score between CE-high and CE-low GBM cases. Immunohistochemical staining was performed on in-house patient cohort to validate the selected genes associated with CE. Moreover, a survival analysis was conducted to uncover the relation between CE and patient survival. Results We illustrated that CE is associated with distinct vascular molecular imprints characterized by up-regulation of pro-inflammatory genes and deregulation of BBB related genes. Among them, PLVAP is up-regulated, whereas TJP1 and ABCG2 are down-regulated in the vasculature of GBM with high CE. In addition, we found that the high CE is associated with poor prognosis and GBM mesenchymal subtype. Conclusion We provide an additional insight to reveal the molecular trait for CE in MRI images with special focus on vascular endothelial cells, linking CE with BBB disruption in the molecular level. This study provides a potential new direction that may be applied for the treatment optimization based on MRI features.
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