Publications by authors named "Yuanli Ni"

Immunotherapy exhibited potential effects for advanced hepatocellular carcinoma, unfortunately, the clinical benefits are often countered by cancer adaptive immune suppressive response. Uncovering the mechanism how cancer cells evade immune surveillance would help to develop new immunotherapy approaches and combination therapy. In this article, by analyzing the transcriptional factors which modulate the differentially expressed genes between T cell infiltration high group and low group, we identified oncoprotein B cell lymphoma 6 (BCL6) suppresses the infiltration and activation of tumor infiltrating T lymphocytes, thus correlated with poorer clinical outcome.

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Background: Clear cell renal cell carcinoma (ccRCC) is an immunologically and histologically diverse tumor. However, how the structural heterogeneity of tumor microenvironment (TME) affects cancer progression and treatment response remains unclear. Hence, we characterized the TME architectures of ccRCC tissues using imaging mass cytometry (IMC) and explored their associations with clinical outcome and therapeutic response.

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Background: High-grade intraepithelial neoplasia (HIN) is the precursor of oesophageal squamous cell carcinoma. The molecular and functional properties of HIN are determined by intrinsic origin cells and the extrinsic microenvironment. Yet, these factors are poorly understood.

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Background: Leukemia stem cells (LSCs) are responsible for the initiation, progression, and relapse of acute myeloid leukemia (AML). Therefore, a therapeutic strategy targeting LSCs is a potential approach to eradicate AML. In this study, we aimed to identify LSC-specific surface markers and uncover the underlying mechanism of AML LSCs.

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Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available.

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Article Synopsis
  • The mixed linear model has been commonly used in genome-wide association studies (GWAS), but its effectiveness for analyzing multiple genetic loci simultaneously has not been previously evaluated.
  • Researchers developed a new model called FASTmrEMMA, which utilizes random effects from single nucleotide polymorphisms (SNPs) and a novel algorithm to analyze genetic data more efficiently.
  • Results show that FASTmrEMMA outperforms existing methods in detecting quantitative trait nucleotides (QTNs) by being more powerful, having less bias in estimates, and requiring shorter computation time.
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Genome-wide association study (GWAS) entails examining a large number of single nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying a variable selection problem in the high dimensional dataset. Although many single-locus GWAS approaches under polygenic background and population structure controls have been widely used, some significant loci fail to be detected. In this study, we used an iterative modified-sure independence screening (ISIS) approach in reducing the number of SNPs to a moderate size.

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Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL).

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